urban accounting and welfare: online appendixerossi/uawapp.pdfsan bernardino r ontario santa fe...

Urban Accounting and Welfare: Online Appendix

Klaus Desmet

Universidad Carlos III

Esteban Rossi-Hansberg

Princeton University

1

APPENDIX A: CITY CHARACTERISTICS AND ROBUSTNESS

‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Dayton

Tampa‐St. Petersburg‐Clearwater

Lubbock

Albuquerque

Oklah

oma City

Akron

Springfield

Nap

aTo

ledo

Orlan

do‐Kissimmee

Grand Rap

ids‐Wyoming

La Crosse

Syracuse

Detroit‐W

arren‐Livonia

Salem

San Francisco‐Oakland‐Fremont

Norw

ich‐New

London

Peo

ria

Baltimore‐Towson

Tulsa

Austin‐Round Rock

Eau Claire

Cincinnati‐Middletown

Den

ver‐Aurora‐Broomfield

Atlan

ta‐San

dy Springs‐M

arietta

Fort W

ayne

Los Angeles‐Long Beach‐San

ta Ana

Raleigh

‐Cary

Alban

y‐Schen

ectady‐Troy

Rochester

Boise City‐Nam

pa

Portland‐Van

couver‐Beaverton

Amarillo

Chicago‐Nap

erville‐Joliet

Jacksonville

Richmond

Las Vegas‐Parad

ise

Chattanooga

Cleveland‐Elyria‐Men

tor

Pittsburgh

Med

ford

Wichita

Harrisburg‐Carlisle

Boulder

Birmingham

‐Hoover

Mem

phis

Beaumont‐Port Arthur

Green

Bay

Waterloo‐Ced

ar Falls

Evan

sville

Billings

Kan

sas City

Portland‐South Portland‐Biddeford

Huntsville

Omah

a‐Council Bluffs

Ren

o‐Sparks

Milw

aukee‐Wau

kesha‐West Allis

Bloomington‐Norm

alDaven

port‐M

oline‐Rock Island

Dallas‐Fort W

orth‐Arlington

Charleston

Houston‐Sugar Lan

d‐Baytown

Columbus

Minneapolis‐St. Pau

l‐Bloomington

Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington

Appleton

Indianap

olis‐Carmel

Knoxville

Wau

sau

Lexington‐Fayette

New

York‐Northern New

Jersey‐Long Island

Roan

oke

Boston‐Cam

bridge‐Quincy

Ced

ar Rap

ids

Gulfport‐Biloxi

Santa Rosa‐Petaluma

Tren

ton‐Ewing

Durham

‐Chapel Hill

Hartford‐W

est H

artford‐East H

artford

Ban

gor

Sioux Falls

Winston‐Salem

Green

sboro‐High Point

San Antonio

Burlington‐South Burlington

Mad

ison

San Jo

se‐Sunnyvale ‐Santa Clara

Lafayette

San Luis Obispo‐Paso Robles

Des M

oines‐W

est Des M

oines

South Ben

d‐M

ishaw

aka

Charlotte‐Gastonia‐Concord

Fargo

Bridgeport‐Stamford‐Norw

alk

Percentage

Change in Population

Cities with Largest Percentage Change in City Population Without Amenity Differences

‐20%

0%

ion

Cities with Smallest Percentage Change in City Population Without Amenity Differences

‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Dayton


Lubbock

Albuquerque

Oklah

oma City

Akron

Springfield

Nap

aTo

ledo

Orlan

do‐Kissimmee

Grand Rap

ids‐Wyoming

La Crosse

Syracuse

Detroit‐W

arren‐Livonia

Salem


Norw

ich‐New

London

Peo

ria

Baltimore‐Towson

Tulsa

Austin‐Round Rock

Eau Claire


Den

ver‐Aurora‐Broomfield

Atlan

ta‐San

dy Springs‐M

arietta

Fort W

ayne

Los Angeles‐Long Beach‐San

ta Ana

Raleigh

‐Cary

Alban

y‐Schen

ectady‐Troy

Rochester

Boise City‐Nam

pa

Portland‐Van

couver‐Beaverton

Amarillo

Chicago‐Nap

erville‐Joliet

Jacksonville

Richmond

Las Vegas‐Parad

ise

Chattanooga


tor

Pittsburgh

Med

ford

Wichita


Boulder

Birmingham

‐Hoover

Mem

phis


Green

Bay

Waterloo‐Ced

ar Falls

Evan

sville

Billings

Kan

sas City


Huntsville

Omah

a‐Council Bluffs

Ren

o‐Sparks

Milw

aukee‐Wau

kesha‐West Allis

Bloomington‐Norm

alDaven

port‐M

oline‐Rock Island

Dallas‐Fort W

orth‐Arlington

Charleston

Houston‐Sugar Lan

d‐Baytown

Columbus


l‐Bloomington

Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington

Appleton

Indianap

olis‐Carmel

Knoxville

Wau

sau

Lexington‐Fayette

New

York‐Northern New

Jersey‐Long Island

Roan

oke

Boston‐Cam

bridge‐Quincy

Ced

ar Rap

ids

Gulfport‐Biloxi


Tren

ton‐Ewing

Durham

‐Chapel Hill

Hartford‐W

est H

artford‐East H

artford

Ban

gor

Sioux Falls

Winston‐Salem

Green

sboro‐High Point

San Antonio


Mad

ison

San Jo

se‐Sunnyvale ‐Santa Clara

Lafayette


Des M

oines‐W

est Des M

oines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage



‐100%

‐80%

‐60%

‐40%

‐20%

0%

Poughkeep

sie‐New

burgh‐…

Modesto

Pueb

loDeltona‐Daytona Beach‐…

Brownsville‐Harlingen

Riverside‐San Bernardino‐ …

Stockton

Visalia‐Porterville

Johnstown

Coeu

r d'Alene

Laredo

Salinas

Hagerstown‐M

artinsburg

Ogden

‐Clearfield

Provo

‐Orem

Chico

Muskegon‐Norton Shores

Ocala

Punta Gorda

Pen

sacola‐Ferry Pass‐Brent

Cham

paign

‐Urbana

Holland‐Grand Haven

El Paso

Greeley

Tucson

Bloomington

Flint

Anderson

Palm Bay‐M

elbourne‐…

Fort Collins‐Loveland

Fresno

Bakersfield

Springfield

Santa Fe

Tallahassee

Santa Cruz‐Watsonville

Asheville

Jackson

Worcester

Utica‐Rome

Hickory‐Len

oir‐M

organ

ton

Yakima

Colorado Springs

Gainesville

Niles‐Ben

ton Harbor

Eugene‐Springfield

Augusta‐Richmond County

Kalam

azoo‐Portage

Bingham

ton

Bremerton‐Silverdale

York‐Han

over

Allentown‐Bethlehem

‐ …Fayetteville‐Springdale‐…

Jacksonville

McAllen‐Edinburg‐M

ission

Racine

Vinelan

d‐M

illville‐Bridgeton

Oxnard‐Thousand Oaks‐…

Topeka

Tuscaloosa

Lansing‐East Lan

sing

Savannah

Fayetteville

Can

ton‐M

assillon

Cap

e Coral‐Fort M

yers

Lynchburg

Johnson City

Rockford

Corpus Christi

Duluth

Bellingham

Columbus

Erie

Janesville

Scranton‐W

ilkes‐Barre

Virginia Beach‐Norfolk‐…

Reading

Mobile

Saginaw

‐Saginaw

…Sacram

ento‐Arden

‐Arcade‐…

Joplin

Youngstown‐W

arren‐ …

Salt Lake City

San Diego

‐Carlsbad

‐San

…Iowa City

Nap

les‐Marco Island

Phoen

ix‐M

esa‐Scottsdale

Buffalo‐Niagara Falls

Honolulu

Seattle‐Tacoma‐Bellevue

Spokane

Lancaster

Springfield

St. Louis

Huntington‐Ashland

Ann Arbor

Percentage



Figure A1: Changes in Population Sizes with Average Amenities

2

‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Columbia

Youngstown‐W

arren‐Boardman


Iowa City

San Diego

‐Carlsbad

‐San

Marcos

Salt Lake City

Billings


Sacram

ento‐Arden

‐Arcade‐Roseville

Amarillo

Scranton‐W

ilkes‐Barre

Johnson City

Lansing‐East Lan

sing

Erie

Racine

Boise City‐Nam

pa

Duluth

Corpus Christi

Chattanooga

Lubbock

Mobile

Rockford

Janesville


Tuscaloosa


Nap

les‐Marco Island

Niles‐Ben

ton Harbor

Lancaster

Lynchburg

La Crosse

Bingham

ton

Phoen

ix‐M

esa ‐Scottsdale

Ban

gor

Yakima

Topeka

Kalam

azoo‐Portage

York‐Han

over

Fayetteville‐Springdale‐Rogers


‐Easton

Anderson

Springfield

Colorado Springs

Utica‐Rome

Jackson

Vinelan

d‐M


Bakersfield

Gainesville

Eau Claire

El Paso

Fresno


Hickory‐Len

oir‐M

organ

ton

Can

ton‐M

assillon

Bloomington

Oxnard‐Thousand Oaks‐Ven

tura


Joplin

Tucson

Savannah

Worcester

Flint

Cham

paign

‐Urbana

Springfield

Tallahassee

Cap

e Coral‐Fort M

yers

St. Louis


Reading


Palm Bay‐M

elbourne‐Titusville

Pen


Bellingham

Med

ford

Chico


Asheville

Salinas

Stockton

Laredo


Ogden

‐Clearfield

Johnstown

Deltona‐Daytona Beach‐Orm

ond Beach

Pueb

loRiverside‐San Bernardino‐Ontario

Santa Fe

Modesto

Provo

‐Orem

Greeley

Punta Gorda

Poughkeep

sie‐New

burgh‐M

iddletown

Ocala


Coeu

r d'Alene

Hagerstown‐M

artinsburg


ission

Percentage


Cities with Largest Percentage Change in City Population Without Efficiency Differences

0%

20%

n

Cities with Smallest Percentage Change in City Population Without Efficiency Differences

‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Columbia

Youngstown‐W

arren‐Boardman


Iowa City

San Diego

‐Carlsbad

‐San

Marcos

Salt Lake City

Billings


Sacram

ento‐Arden


Amarillo

Scranton‐W

ilkes‐Barre

Johnson City

Lansing‐East Lan

sing

Erie

Racine

Boise City‐Nam

pa

Duluth

Corpus Christi

Chattanooga

Lubbock

Mobile

Rockford

Janesville


Tuscaloosa


Nap

les‐Marco Island

Niles‐Ben

ton Harbor

Lancaster

Lynchburg

La Crosse

Bingham

ton

Phoen

ix‐M

esa ‐Scottsdale

Ban

gor

Yakima

Topeka

Kalam

azoo‐Portage

York‐Han

over



‐Easton

Anderson

Springfield

Colorado Springs

Utica‐Rome

Jackson

Vinelan

d‐M


Bakersfield

Gainesville

Eau Claire

El Paso

Fresno


Hickory‐Len

oir‐M

organ

ton

Can

ton‐M

assillon

Bloomington


tura


Joplin

Tucson

Savannah

Worcester

Flint

Cham

paign

‐Urbana

Springfield

Tallahassee

Cap

e Coral‐Fort M

yers

St. Louis


Reading


Palm Bay‐M


Pen


Bellingham

Med

ford

Chico


Asheville

Salinas

Stockton

Laredo


Ogden

‐Clearfield

Johnstown

Deltona‐Daytona Beach‐Orm

ond Beach

Pueb

loRiverside‐San Bernardino‐Ontario

Santa Fe

Modesto

Provo

‐Orem

Greeley

Punta Gorda

Poughkeep

sie‐New

burgh‐M

iddletown

Ocala


Coeu

r d'Alene

Hagerstown‐M

artinsburg


ission

Percentage



‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

Bridgeport‐Stamford‐…

Charlotte‐Gastonia‐…

Lafayette

San Luis Obispo‐Paso …

Durham

‐Chapel Hill

Des M

oines‐W

est Des …

San …

Hartford‐W

est …

Tren

ton‐Ewing

New

York‐Northern …

Houston‐Sugar …

Boston‐Cam

bridge‐Qu…

Baton Rouge

Sioux Falls

Green

sboro‐High Point

Philadelphia‐Cam

den

‐…Charleston

Winston‐Salem

Indianap

olis‐Carmel

Gulfport‐Biloxi

Milw

aukee‐…

Evan

sville


l‐…

Dallas‐Fort W

orth‐…

Daven

port‐M

oline‐…

Mad

ison

South Ben

d‐M

ishaw

aka


tor


Knoxville

Pittsburgh

Mem

phis

Bloomington‐Norm

alOmah

a‐Council Bluffs

Green

Bay

Boulder

Lexington‐Fayette

Birmingham

‐Hoover


Chicago‐Nap

erville‐…

Columbus

San Antonio

Kan

sas City

Waterloo‐Ced

ar Falls

Richmond

Wichita


Ced

ar Rap

ids

Detroit‐W

arren‐Livonia

Rochester


Fort W

ayne

Huntsville

Portland‐Van

couver‐…

Fargo

Los Angeles‐Long …

Peo

ria

Las Vegas‐Parad

ise

Ren

o‐Sparks

Dayton

Den

ver‐Aurora‐…

Syracuse

Burlington‐South …

Ann Arbor

Tulsa

San Francisco‐…

Toledo

Appleton

Alban

y‐Schen

ectady‐…

Salem

Atlan

ta‐San

dy Springs‐…

Baltimore‐Towson

Austin‐Round Rock

Nap

aBuffalo‐Niagara Falls

Oklah

oma City


Columbus

Springfield

Norw

ich‐New

London

Wau

sau

Jacksonville

Honolulu

Raleigh

‐Cary

Spokane

Grand Rap

ids‐Wyoming

Orlan

do‐Kissimmee

Jacksonville

Virginia Beach‐…

Albuquerque

Akron

Portland‐South …

Fayetteville

Saginaw

‐Saginaw

…Roan

oke

Percentage



Figure A2: Changes in Population Sizes with Average Effi ciency

3

1100%

1300%

1500%ulation

Cities with Largest Percentage Change in City Population Without Excessive Friction Differences

100%

300%

500%

700%

900%

1100%

1300%

1500%Percentage

Chan

ge in

Population


‐100%

100%

300%

500%

700%

900%

1100%

1300%

1500%

Colorado Springs


alk


Pensacola‐Ferry Pass‐Brent

Ren

o‐Sparks

Des Moines‐W

est Des Moines

Toledo

Wichita

Tallahassee

Dayton

Lexington‐Fayette



Youngstown‐W

arren‐Boardman

Mobile

El Paso

Naples‐Marco Island

Winston‐Salem

Syracuse

Salinas



Eau Claire

Hickory‐Len

oir‐M

organton

York‐Hanover

Baton Rouge



Flint

Huntsville


Amarillo

Joplin


South Ben

d‐M

ishaw

aka

Greeley

Lubbock

Corpus Christi

Salt Lake City

Appleton

Rockford

Ced

ar Rapids

Punta Gorda


Laredo

Lansing‐East Lansing

Billings

Johnstown

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Gainesville

Davenport‐M

oline‐Rock Island

Erie

Chico


Peoria

Pueblo

Lynchburg

Norw

ich‐New London

Duluth

Spokane

La Crosse

Trenton‐Ewing

Wausau

Topeka

Green

Bay

Boulder

Ann Arbor



Durham

‐Chapel Hill

Vineland‐M


Bingham

ton

Gulfport‐Biloxi

Cham

paign

‐Urbana

Evansville

Janesville

Tuscaloosa



Fayetteville

Charleston

Yakima

Lafayette

ginaw

‐Saginaw

Township North

Jackson

Johnson City

Springfield

Bloomington

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

alIowa City

Columbus

Niles‐Ben

ton Harbor

Napa

Anderson

Jacksonville

Percentage

Chan

ge in

Population


‐100%

100%

300%

500%

700%

900%

1100%

1300%

1500%

Colorado Springs


alk



Ren

o‐Sparks

Des Moines‐W

est Des Moines

Toledo

Wichita

Tallahassee

Dayton

Lexington‐Fayette



Youngstown‐W

arren‐Boardman

Mobile

El Paso


Winston‐Salem

Syracuse

Salinas



Eau Claire

Hickory‐Len

oir‐M

organton

York‐Hanover

Baton Rouge



Flint

Huntsville


Amarillo

Joplin


South Ben

d‐M

ishaw

aka

Greeley

Lubbock

Corpus Christi

Salt Lake City

Appleton

Rockford

Ced

ar Rapids

Punta Gorda


Laredo


Billings

Johnstown

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Gainesville

Davenport‐M

oline‐Rock Island

Erie

Chico


Peoria

Pueblo

Lynchburg

Norw

ich‐New London

Duluth

Spokane

La Crosse

Trenton‐Ewing

Wausau

Topeka

Green

Bay

Boulder

Ann Arbor



Durham

‐Chapel Hill

Vineland‐M


Bingham

ton

Gulfport‐Biloxi

Cham

paign

‐Urbana

Evansville

Janesville

Tuscaloosa



Fayetteville

Charleston

Yakima

Lafayette

Saginaw

‐Saginaw

Township North

Jackson

Johnson City

Springfield

Bloomington

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

alIowa City

Columbus

Niles‐Ben

ton Harbor

Napa

Anderson

Jacksonville

Percentage

Chan

ge in

Population


60%

80%

Cities with Smallest Percentage Change in City Population Without Excessive Friction Differences

‐100%

100%

300%

500%

700%

900%

1100%

1300%

1500%

Colorado Springs


alk



Ren

o‐Sparks

Des Moines‐W

est Des Moines

Toledo

Wichita

Tallahassee

Dayton

Lexington‐Fayette



Youngstown‐W

arren‐Boardman

Mobile

El Paso


Winston‐Salem

Syracuse

Salinas



Eau Claire

Hickory‐Len

oir‐M

organton

York‐Hanover

Baton Rouge



Flint

Huntsville


Amarillo

Joplin


South Ben

d‐M

ishaw

aka

Greeley

Lubbock

Corpus Christi

Salt Lake City

Appleton

Rockford

Ced

ar Rapids

Punta Gorda


Laredo


Billings

Johnstown

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Gainesville

Davenport‐M

oline‐Rock Island

Erie

Chico


Peoria

Pueblo

Lynchburg

Norw

ich‐New London

Duluth

Spokane

La Crosse

Trenton‐Ewing

Wausau

Topeka

Green

Bay

Boulder

Ann Arbor



Durham

‐Chapel Hill

Vineland‐M


Bingham

ton

Gulfport‐Biloxi

Cham

paign

‐Urbana

Evansville

Janesville

Tuscaloosa



Fayetteville

Charleston

Yakima

Lafayette

Saginaw

‐Saginaw

Township North

Jackson

Johnson City

Springfield

Bloomington

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

alIowa City

Columbus

Niles‐Ben

ton Harbor

Napa

Anderson

Jacksonville

Percentage

Chan

ge in

Population


‐40%

‐20%

0%

20%

40%

60%

80%

ntage Chan

ge in

Population


‐100%

100%

300%

500%

700%

900%

1100%

1300%

1500%

Colorado Springs


alk



Ren

o‐Sparks

Des Moines‐W

est Des Moines

Toledo

Wichita

Tallahassee

Dayton

Lexington‐Fayette



Youngstown‐W

arren‐Boardman

Mobile

El Paso


Winston‐Salem

Syracuse

Salinas



Eau Claire

Hickory‐Len

oir‐M

organton

York‐Hanover

Baton Rouge



Flint

Huntsville


Amarillo

Joplin


South Ben

d‐M

ishaw

aka

Greeley

Lubbock

Corpus Christi

Salt Lake City

Appleton

Rockford

Ced

ar Rapids

Punta Gorda


Laredo


Billings

Johnstown

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Gainesville

Davenport‐M

oline‐Rock Island

Erie

Chico


Peoria

Pueblo

Lynchburg

Norw

ich‐New London

Duluth

Spokane

La Crosse

Trenton‐Ewing

Wausau

Topeka

Green

Bay

Boulder

Ann Arbor



Durham

‐Chapel Hill

Vineland‐M


Bingham

ton

Gulfport‐Biloxi

Cham

paign

‐Urbana

Evansville

Janesville

Tuscaloosa



Fayetteville

Charleston

Yakima

Lafayette

Saginaw

‐Saginaw

Township North

Jackson

Johnson City

Springfield

Bloomington

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

alIowa City

Columbus

Niles‐Ben

ton Harbor

Napa

Anderson

Jacksonville

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

ork‐Northern New …

geles‐Long Beach‐…

e‐San Bernardino‐…

x‐Mesa‐Scottsdale

o‐Naperville‐Joliet

nta‐Sandy Springs‐…

t Worth‐Arlington

elphia‐Cam

den

‐Wil …

e‐Sunnyvale‐Santa …

Tampa‐St. …

Sioux Falls

Cam

bridge‐Quincy

it‐W

arren‐Livonia

uston‐Sugar Land‐…

nneapolis‐St. Paul‐…

rancisco‐Oakland‐…

Baltimore‐Towson

Salem

eepsie‐Newburgh‐…

town‐M

artinsburg

rlando‐Kissimmee

‐Edinburg‐M

ission

Aurora‐Broomfield

acramen

to‐Arden

‐…Kansas City

San Antonio

Columbus

iego

‐Carlsbad

‐San

…rtland‐Vancouver‐…

as Vegas‐Paradise

Jacksonville

Pittsburgh

nnati‐Middletown

St. Louis

Austin‐Round Rock

d‐Thousand Oaks‐…

e Coral‐Fort M

yers

on‐Norton Shores

Reading

Santa Fe

Medford

Luis Obispo‐Paso …

Modesto

dianapolis‐Carmel

and‐Elyria‐Mentor

Raleigh

‐Cary

nia Beach‐Norfolk‐…

Worcester

Springfield

Boise City‐Nam

pa

Chattanooga

Ocala

Provo

‐Orem

Coeu

r d'Alene

harlotte‐Gastonia‐…

Memphis

Oklahoma City

d‐South Portland‐…

Asheville

town‐Bethlehem

‐ …aukee‐Waukesha‐…

Tucson

Savannah

rmingham

‐Hoover

Richmond

Springfield

Stockton

Ogden

‐Clearfield

Schen

ectady‐Troy

m Bay‐M

elbourne‐…

rd‐W

est Hartford‐…

Albuquerque

Lancaster

Rapids‐Wyoming

Fresno

ffalo‐Niagara Falls

Madison

Rochester

Akron

Columbia

Fargo

Tulsa

a‐Daytona Beach‐ …

Roanoke

Canton‐M

assillon

aha‐Council Bluffs

Honolulu

Knoxville

Bangor

Bellingham

nsboro‐High Point

Bakersfield

Percentage

Chan

ge in

Population


‐100%

100%

300%

500%

700%

900%

1100%

1300%

1500%

Colorado Springs


alk



Ren

o‐Sparks

Des Moines‐W

est Des Moines

Toledo

Wichita

Tallahassee

Dayton

Lexington‐Fayette



Youngstown‐W

arren‐Boardman

Mobile

El Paso


Winston‐Salem

Syracuse

Salinas



Eau Claire

Hickory‐Len

oir‐M

organton

York‐Hanover

Baton Rouge



Flint

Huntsville


Amarillo

Joplin


South Ben

d‐M

ishaw

aka

Greeley

Lubbock

Corpus Christi

Salt Lake City

Appleton

Rockford

Ced

ar Rapids

Punta Gorda


Laredo


Billings

Johnstown

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Gainesville

Davenport‐M

oline‐Rock Island

Erie

Chico


Peoria

Pueblo

Lynchburg

Norw

ich‐New London

Duluth

Spokane

La Crosse

Trenton‐Ewing

Wausau

Topeka

Green

Bay

Boulder

Ann Arbor



Durham

‐Chapel Hill

Vineland‐M


Bingham

ton

Gulfport‐Biloxi

Cham

paign

‐Urbana

Evansville

Janesville

Tuscaloosa



Fayetteville

Charleston

Yakima

Lafayette

Saginaw

‐Saginaw

Township North

Jackson

Johnson City

Springfield

Bloomington

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

alIowa City

Columbus

Niles‐Ben

ton Harbor

Napa

Anderson

Jacksonville

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

New York‐Northern New …

Los Angeles‐Long Beach‐…

Riverside‐San

Bernardino‐…

Phoenix‐M

esa‐Scottsdale

Chicago‐Naperville‐Joliet

Atlanta‐Sandy Springs‐ …

Dallas‐Fort W

orth‐Arlington

Philadelphia‐Cam

den

‐Wil …

San Jose‐Sunnyvale‐Santa …

Tampa‐St. …

Sioux Falls

Boston‐Cam

bridge‐Quincy

Detroit‐W

arren‐Livonia

Houston‐Sugar Land‐ …

Minneapolis‐St. Paul‐…

San Francisco‐Oakland‐…

Baltimore‐Towson

Salem

Poughkeepsie‐Newburgh‐ …

Hagerstown‐M

artinsburg

Orlando‐Kissimmee


ission

Denver‐Aurora‐Broomfield

Sacram

ento‐Arden

‐ …Kansas City

San Antonio

Columbus

San Diego

‐Carlsbad

‐San

…Portland‐Vancouver‐…

Las Vegas‐Paradise

Jacksonville

Pittsburgh


St. Louis

Austin‐Round Rock


Cape Coral‐Fort M

yers


Reading

Santa Fe

Medford


Modesto

Indianapolis‐Carmel

Cleveland‐Elyria‐Mentor

Raleigh

‐Cary

Virginia Beach‐Norfolk‐ …

Worcester

Springfield

Boise City‐Nam

pa

Chattanooga

Ocala

Provo

‐Orem

Coeu

r d'Alene

Charlotte‐Gastonia‐ …

Memphis

Oklahoma City

Portland‐South Portland‐ …

Asheville


‐ …Milw

aukee‐W

aukesha‐…

Tucson

Savannah

Birmingham

‐Hoover

Richmond

Springfield

Stockton

Ogden

‐Clearfield

Albany‐Schenectady‐Troy

Palm Bay‐M

elbourne‐ …

Hartford‐W

est Hartford‐…

Albuquerque

Lancaster

Grand Rapids‐Wyoming

Fresno


Madison

Rochester

Akron

Columbia

Fargo

Tulsa

Deltona‐Daytona Beach‐ …

Roanoke

Canton‐M

assillon

Omaha‐Council Bluffs

Honolulu

Knoxville

Bangor

Bellingham

Green

sboro‐High Point

Bakersfield

Percentage

Chan

ge in

Population


Figure A3: Changes in Population Sizes with Average Excessive Frictions

4

150%

180%

210%

240%Population


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

a k r n e y d n a e o g e e m a t n a e n a k n d a e a y y r a n o t e d e a r h d a e r r s r y e s s y e d s sl s d n n n s n e n l n e d e u y e s xi a g l d s m t r o n n a e s s a d o k

Percentage

Chan

ge in

Population


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Lubbock


Dayton

Albuquerque

Oklahoma City

Springfield

Akron

Napa

Orlando‐Kissimmee

Toledo


La Crosse

Syracuse

Salem

Detroit‐W

arren‐Livonia


Norw

ich‐New London

Peoria

Eau Claire

Baltimore‐Towson

Tulsa

Austin‐Round Rock



Atlanta‐Sandy Springs‐M

arietta

Fort W

ayne

Los Angeles‐Long Beach‐Santa Ana

Raleigh

‐Cary


Rochester

Boise City‐Nam

pa

Portland‐Vancouver‐Beaverton

Amarillo


Jacksonville

Richmond


Chattanooga


Pittsburgh

Medford

Wichita


Boulder

Birmingham

‐Hoover

Memphis


Green

Bay

Evansville

Waterloo‐Ced

ar Falls

Billings

Kansas City

Huntsville



Milw

aukee‐W

aukesha‐West Allis

Bloomington‐Norm

alRen

o‐Sparks

Davenport‐M

oline‐Rock Island

Charleston

Dallas‐Fort W

orth‐Arlington

Houston‐Sugar Land‐Baytown

Columbus

Minneapolis‐St. Paul‐Bloomington

Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington


Appleton

Knoxville

ew York‐Northern New Jersey‐Long Island

Lexington‐Fayette

Wausau

Boston‐Cam

bridge‐Quincy

Roanoke

Ced

ar Rapids

Gulfport‐Biloxi


Trenton‐Ewing

Durham

‐Chapel Hill

Hartford‐W

est Hartford‐East Hartford

Sioux Falls

Winston‐Salem

Green

sboro‐High Point

Bangor

San Antonio


Madison

San Jose‐Sunnyvale‐Santa Clara

Lafayette


Des Moines‐W

est Des Moines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage

Chan

ge in

Population


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Lubbock


Dayton

Albuquerque

Oklahoma City

Springfield

Akron

Napa

Orlando‐Kissimmee

Toledo


La Crosse

Syracuse

Salem

Detroit‐W

arren‐Livonia


Norw

ich‐New London

Peoria

Eau Claire

Baltimore‐Towson

Tulsa

Austin‐Round Rock




arietta

Fort W

ayne


Raleigh

‐Cary


Rochester

Boise City‐Nam

pa


Amarillo


Jacksonville

Richmond


Chattanooga


Pittsburgh

Medford

Wichita


Boulder

Birmingham

‐Hoover

Memphis


Green

Bay

Evansville

Waterloo‐Ced

ar Falls

Billings

Kansas City

Huntsville



Milw

aukee‐W


Bloomington‐Norm

alRen

o‐Sparks

Davenport‐M

oline‐Rock Island

Charleston

Dallas‐Fort W

orth‐Arlington


Columbus


Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington


Appleton

Knoxville

New York‐Northern New Jersey‐Long Island

Lexington‐Fayette

Wausau

Boston‐Cam

bridge‐Quincy

Roanoke

Ced

ar Rapids

Gulfport‐Biloxi


Trenton‐Ewing

Durham

‐Chapel Hill

Hartford‐W


Sioux Falls

Winston‐Salem

Green

sboro‐High Point

Bangor

San Antonio


Madison


Lafayette


Des Moines‐W

est Des Moines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage

Chan

ge in

Population


0%


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Lubbock


Dayton

Albuquerque

Oklahoma City

Springfield

Akron

Napa

Orlando‐Kissimmee

Toledo


La Crosse

Syracuse

Salem

Detroit‐W

arren‐Livonia


Norw

ich‐New London

Peoria

Eau Claire

Baltimore‐Towson

Tulsa

Austin‐Round Rock




arietta

Fort W

ayne


Raleigh

‐Cary


Rochester

Boise City‐Nam

pa


Amarillo


Jacksonville

Richmond


Chattanooga


Pittsburgh

Medford

Wichita


Boulder

Birmingham

‐Hoover

Memphis


Green

Bay

Evansville

Waterloo‐Ced

ar Falls

Billings

Kansas City

Huntsville



Milw

aukee‐W


Bloomington‐Norm

alRen

o‐Sparks

Davenport‐M

oline‐Rock Island

Charleston

Dallas‐Fort W

orth‐Arlington


Columbus


Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington


Appleton

Knoxville


Lexington‐Fayette

Wausau

Boston‐Cam

bridge‐Quincy

Roanoke

Ced

ar Rapids

Gulfport‐Biloxi


Trenton‐Ewing

Durham

‐Chapel Hill

Hartford‐W


Sioux Falls

Winston‐Salem

Green

sboro‐High Point

Bangor

San Antonio


Madison


Lafayette


Des Moines‐W

est Des Moines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage

Chan

ge in

Population


‐60%

‐40%

‐20%

0%

ntage Chan

ge in

Population


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Lubbock


Dayton

Albuquerque

Oklahoma City

Springfield

Akron

Napa

Orlando‐Kissimmee

Toledo


La Crosse

Syracuse

Salem

Detroit‐W

arren‐Livonia


Norw

ich‐New London

Peoria

Eau Claire

Baltimore‐Towson

Tulsa

Austin‐Round Rock




arietta

Fort W

ayne


Raleigh

‐Cary


Rochester

Boise City‐Nam

pa


Amarillo


Jacksonville

Richmond


Chattanooga


Pittsburgh

Medford

Wichita


Boulder

Birmingham

‐Hoover

Memphis


Green

Bay

Evansville

Waterloo‐Ced

ar Falls

Billings

Kansas City

Huntsville



Milw

aukee‐W


Bloomington‐Norm

alRen

o‐Sparks

Davenport‐M

oline‐Rock Island

Charleston

Dallas‐Fort W

orth‐Arlington


Columbus


Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington


Appleton

Knoxville


Lexington‐Fayette

Wausau

Boston‐Cam

bridge‐Quincy

Roanoke

Ced

ar Rapids

Gulfport‐Biloxi


Trenton‐Ewing

Durham

‐Chapel Hill

Hartford‐W


Sioux Falls

Winston‐Salem

Green

sboro‐High Point

Bangor

San Antonio


Madison


Lafayette


Des Moines‐W

est Des Moines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

Johnstown

gon‐Norton Shores

a Cruz‐Watsonville

Springfield


Colorado Springs

Utica‐Rome


Gainesville

Jackson

Savannah

‐Len

oir‐M

organton

e Coral‐Fort M

yers

Anderson

Bakersfield

Fresno

Bellingham

Worcester

Tallahassee

lland‐Grand Haven

Cham

paign

‐Urbana

m Bay‐M

elbourne‐…

El Paso

Bloomington

Asheville

Santa Fe

la‐Ferry Pass‐Brent

Chico

Tucson

Flint

ort Collins‐Loveland

Salinas

Ogden

‐Clearfield

Laredo

Stockton

stown‐M

artinsburg

ownsville‐Harlingen

Coeu

r d'Alene

Pueblo

na‐Daytona Beach‐…

Modesto

keep

sie‐Newburgh‐…

de‐San Bernardino‐…

Provo

‐Orem

Punta Gorda

Ocala

Greeley

Yakima

iles‐Ben

ton Harbor

Kalam

azoo‐Portage

‐Richmond County

Bingham

ton

emerton‐Silverdale

York‐Hanover

ntown‐Bethlehem

‐…tteville‐Springdale‐…

rd‐Thousand Oaks‐…

‐Millville‐Bridgeton

Racine

Topeka

Jacksonville

Tuscaloosa

Canton‐M

assillon

ansing‐East Lansing

Fayetteville

Lynchburg

Rockford

Johnson City

Corpus Christi

Duluth

n‐Edinburg‐M

ission

Columbus

Reading

Erie

Janesville

Joplin

anton‐W

ilkes‐Barre

Mobile


Sacram

ento‐Arden

‐…Saginaw

‐Saginaw

…ungstown‐W

arren‐…

Salt Lake City

Diego

‐Carlsbad

‐San

…Iowa City

aples‐Marco Island

ix‐M

esa‐Scottsdale

St. Louis

uffalo‐Niagara Falls

Honolulu

Lancaster

e‐Tacoma‐Bellevue

Spokane

Springfield

untington‐Ashland

Ann Arbor

Percentage

Chan

ge in

Population


‐30%

0%

30%

60%

90%

120%

150%

180%

210%

240%

Columbia

Lubbock


Dayton

Albuquerque

Oklahoma City

Springfield

Akron

Napa

Orlando‐Kissimmee

Toledo


La Crosse

Syracuse

Salem

Detroit‐W

arren‐Livonia


Norw

ich‐New London

Peoria

Eau Claire

Baltimore‐Towson

Tulsa

Austin‐Round Rock




arietta

Fort W

ayne


Raleigh

‐Cary


Rochester

Boise City‐Nam

pa


Amarillo


Jacksonville

Richmond


Chattanooga


Pittsburgh

Medford

Wichita


Boulder

Birmingham

‐Hoover

Memphis


Green

Bay

Evansville

Waterloo‐Ced

ar Falls

Billings

Kansas City

Huntsville



Milw

aukee‐W


Bloomington‐Norm

alRen

o‐Sparks

Davenport‐M

oline‐Rock Island

Charleston

Dallas‐Fort W

orth‐Arlington


Columbus


Baton Rouge

Philadelphia‐Cam

den

‐Wilm

ington


Appleton

Knoxville


Lexington‐Fayette

Wausau

Boston‐Cam

bridge‐Quincy

Roanoke

Ced

ar Rapids

Gulfport‐Biloxi


Trenton‐Ewing

Durham

‐Chapel Hill

Hartford‐W


Sioux Falls

Winston‐Salem

Green

sboro‐High Point

Bangor

San Antonio


Madison


Lafayette


Des Moines‐W

est Des Moines

South Ben

d‐M

ishaw

aka


Fargo


alk

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

Johnstown



Springfield


Colorado Springs

Utica‐Rome


Gainesville

Jackson

Savannah

Hickory‐Len

oir‐M

organton

Cape Coral‐Fort M

yers

Anderson

Bakersfield

Fresno

Bellingham

Worcester

Tallahassee


Cham

paign

‐Urbana

Palm Bay‐M

elbourne‐…

El Paso

Bloomington

Asheville

Santa Fe


Chico

Tucson

Flint


Salinas

Ogden

‐Clearfield

Laredo

Stockton

Hagerstown‐M

artinsburg


Coeu

r d'Alene

Pueblo

Deltona‐Daytona Beach‐…

Modesto

Poughkeepsie‐Newburgh‐…

Riverside‐San

Bernardino‐…

Provo

‐Orem

Punta Gorda

Ocala

Greeley

Yakima

Niles‐Ben

ton Harbor

Kalam

azoo‐Portage


Bingham

ton


York‐Hanover


‐…Fayetteville‐Springdale‐…


Vineland‐M


Racine

Topeka

Jacksonville

Tuscaloosa

Canton‐M

assillon


Fayetteville

Lynchburg

Rockford

Johnson City

Corpus Christi

Duluth


ission

Columbus

Reading

Erie

Janesville

Joplin

Scranton‐W

ilkes‐Barre

Mobile

Virginia Beach‐Norfolk‐…

Sacram

ento‐Arden

‐…Saginaw

‐Saginaw

…Yo

ungstown‐W

arren‐…

Salt Lake City

San Diego

‐Carlsbad

‐San

…Iowa City


Phoenix‐M

esa‐Scottsdale

St. Louis


Honolulu

Lancaster


Spokane

Springfield


Ann Arbor

Percentage

Chan

ge in

Population


Figure A4: Changes in Population Sizes with Average Amenities (with Externalities)

5

‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Amarillo

Orlan

do‐Kissimmee

Youngstown‐W

arren‐Boardman


Columbia

Salt Lake City

Virginia Beach‐Norfolk‐New

port New

sJohnson City

Scranton‐W

ilkes‐Barre

Ban

gor

Erie

Racine

Duluth

La Crosse

Lubbock

Janesville

San Diego

‐Carlsbad

‐San

Marcos

Lansing‐East Lan

sing

Chattanooga

Boise City‐Nam

pa

Corpus Christi

Sacram

ento‐Arden


Mobile

Tuscaloosa


Rockford


Niles‐Ben

ton Harbor

Nap

les‐Marco Island

Lynchburg

Bingham

ton

Lancaster

Topeka

Yakima


Kalam

azoo‐Portage

Eau Claire

York‐Han

over

Anderson

Vinelan

d‐M



Jackson

Springfield

Utica‐Rome

Gainesville


‐Easton

Colorado Springs

Phoen

ix‐M

esa‐Scottsdale


Bakersfield

Joplin

Bloomington

Hickory‐Len

oir‐M

organ

ton

El Paso

Can

ton‐M

assillon

Fresno


Cham

paign

‐Urbana

Savannah


tura

Flint

Worcester

Tallahassee

Tucson

Springfield

St. Louis


Cap

e Coral‐Fort M

yers


Reading

Med

ford

Bellingham

Palm Bay‐M


Chico

Pen


Asheville


Salinas

Laredo


Stockton

Ogden

‐Clearfield

Johnstown

Pueb

loDeltona‐Daytona Beach‐Orm

ond Beach

Santa Fe

Modesto

Riverside‐San Bernardino‐Ontario

Punta Gorda

Provo

‐Orem

Greeley

Poughkeep

sie‐New

burgh‐M

iddletown

Ocala


Coeu

r d'Alene

Hagerstown‐M

artinsburg


ission

Percentage



0%

20%

on


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Amarillo

Orlan

do‐Kissimmee

Youngstown‐W

arren‐Boardman


Columbia

Salt Lake City

Virginia Beach‐Norfolk‐New

port New

sJohnson City

Scranton‐W

ilkes‐Barre

Ban

gor

Erie

Racine

Duluth

La Crosse

Lubbock

Janesville

San Diego

‐Carlsbad

‐San

Marcos

Lansing‐East Lan

sing

Chattanooga

Boise City‐Nam

pa

Corpus Christi

Sacram

ento‐Arden


Mobile

Tuscaloosa


Rockford


Niles‐Ben

ton Harbor

Nap

les‐Marco Island

Lynchburg

Bingham

ton

Lancaster

Topeka

Yakima


Kalam

azoo‐Portage

Eau Claire

York‐Han

over

Anderson

Vinelan

d‐M



Jackson

Springfield

Utica‐Rome

Gainesville


‐Easton

Colorado Springs

Phoen

ix‐M

esa‐Scottsdale


Bakersfield

Joplin

Bloomington

Hickory‐Len

oir‐M

organ

ton

El Paso

Can

ton‐M

assillon

Fresno


Cham

paign

‐Urbana

Savannah


tura

Flint

Worcester

Tallahassee

Tucson

Springfield

St. Louis


Cap

e Coral‐Fort M

yers


Reading

Med

ford

Bellingham

Palm Bay‐M


Chico

Pen


Asheville


Salinas

Laredo


Stockton

Ogden

‐Clearfield

Johnstown

Pueb

loDeltona‐Daytona Beach‐Orm

ond Beach

Santa Fe

Modesto

Riverside‐San Bernardino‐Ontario

Punta Gorda

Provo

‐Orem

Greeley

Poughkeep

sie‐New

burgh‐M

iddletown

Ocala


Coeu

r d'Alene

Hagerstown‐M

artinsburg


ission

Percentage



‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

Bridgeport‐Stamford‐…

Lafayette

Gulfport‐Biloxi

Hartford‐W

est …

San Jo

se‐Sunnyvale‐…

Green

sboro‐High Point

Mad

ison

Winston‐Salem

Tren

ton‐Ewing

Durham

‐Chapel Hill

South Ben

d‐M

ishaw

aka


Fargo

Des M

oines‐W

est Des …

Charlotte‐Gastonia‐…

Charleston

Baton Rouge

Evan

sville

Houston‐Sugar Lan

d‐…

Sioux Falls

Boston‐Cam

bridge‐…

Daven

port‐M

oline‐…

Indianap

olis‐Carmel


Milw

aukee‐Wau

kesha‐…

Bloomington‐Norm

alNew

York‐Northern …

Philadelphia‐Cam

den

‐…Beaumont‐Port Arthur

Green

Bay

Knoxville

Waterloo‐Ced

ar Falls

Boulder

Lexington‐Fayette


l‐…

Ced

ar Rap

ids


tor

Omah

a‐Council Bluffs

Mem

phis

Dallas‐Fort W

orth‐…

San Antonio

Birmingham

‐Hoover

Burlington‐South …

Pittsburgh


Wichita

Huntsville

Columbus

Fort W

ayne

Richmond

Kan

sas City

Appleton

Wau

sau

Ren

o‐Sparks

Rochester

Peo

ria

Nap

aChicago‐Nap

erville‐Joliet

Syracuse

Ann Arbor


Portland‐Van

couver‐…

Dayton

Detroit‐W

arren‐Livonia

Springfield

Tulsa

Norw

ich‐New

London

Las Vegas‐Parad

ise

Toledo


Alban

y‐Schen

ectady‐…

Den

ver‐Aurora‐…

Columbus

Spokane

Roan

oke

Austin‐Round Rock

San Francisco‐Oakland‐…

Billings


Oklah

oma City

Honolulu

Salem

Jacksonville


Baltimore‐Towson

Raleigh

‐Cary

Jacksonville

Portland‐South …

Saginaw

‐Saginaw

…Iowa City

Grand Rap

ids‐Wyoming

Fayetteville

Atlan

ta‐San

dy Springs‐…

Akron

Albuquerque

Percentage



Figure A5: Changes in Population Sizes with Average Effi ciency (with Externalities)

6

700%

800%

900%ulation


0%

100%

200%

300%

400%

500%

600%

700%

800%

900%Percentage

Chan

ge in

Population


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Knoxville

Green

sboro‐High Point

Bakersfield

Bellingham

Bangor

Colorado Springs


alk



Des Moines‐W

est Des Moines

Ren

o‐Sparks

Toledo

Wichita

Dayton

Tallahassee

Lexington‐Fayette


Youngstown‐W

arren‐Boardman

El Paso

Mobile

Syracuse

Winston‐Salem




Salinas

Baton Rouge


Hickory‐Len

oir‐M

organton

York‐Hanover

Flint


Huntsville




Eau Claire

Amarillo

South Ben

d‐M

ishaw

aka

Joplin

Greeley

Lubbock

Corpus Christi

Salt Lake City

Rockford

Appleton


Ced

ar Rapids


Laredo

Punta Gorda

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Billings

Johnstown

Davenport‐M

oline‐Rock Island

Gainesville

Erie


Peoria

Chico

Norw

ich‐New London

Duluth

Spokane

Lynchburg

Trenton‐Ewing

Pueblo

Green

Bay

Topeka

La Crosse

Wausau

Boulder

Ann Arbor

Durham

‐Chapel Hill



Bingham

ton

Vineland‐M


Evansville

Gulfport‐Biloxi

Cham

paign

‐Urbana

Tuscaloosa


Fayetteville

Janesville

Charleston

Yakima


Lafayette

ginaw

‐Saginaw

Township North

Springfield

Johnson City

Jackson

Bloomington

Columbus

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

al

Percentage

Chan

ge in

Population


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Knoxville

Green

sboro‐High Point

Bakersfield

Bellingham

Bangor

Colorado Springs


alk



Des Moines‐W

est Des Moines

Ren

o‐Sparks

Toledo

Wichita

Dayton

Tallahassee

Lexington‐Fayette


Youngstown‐W

arren‐Boardman

El Paso

Mobile

Syracuse

Winston‐Salem




Salinas

Baton Rouge


Hickory‐Len

oir‐M

organton

York‐Hanover

Flint


Huntsville




Eau Claire

Amarillo

South Ben

d‐M

ishaw

aka

Joplin

Greeley

Lubbock

Corpus Christi

Salt Lake City

Rockford

Appleton


Ced

ar Rapids


Laredo

Punta Gorda

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Billings

Johnstown

Davenport‐M

oline‐Rock Island

Gainesville

Erie


Peoria

Chico

Norw

ich‐New London

Duluth

Spokane

Lynchburg

Trenton‐Ewing

Pueblo

Green

Bay

Topeka

La Crosse

Wausau

Boulder

Ann Arbor

Durham

‐Chapel Hill



Bingham

ton

Vineland‐M


Evansville

Gulfport‐Biloxi

Cham

paign

‐Urbana

Tuscaloosa


Fayetteville

Janesville

Charleston

Yakima


Lafayette

Saginaw

‐Saginaw

Township North

Springfield

Johnson City

Jackson

Bloomington

Columbus

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

al

Percentage

Chan

ge in

Population


60%

80%


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Knoxville

Green

sboro‐High Point

Bakersfield

Bellingham

Bangor

Colorado Springs


alk



Des Moines‐W

est Des Moines

Ren

o‐Sparks

Toledo

Wichita

Dayton

Tallahassee

Lexington‐Fayette


Youngstown‐W

arren‐Boardman

El Paso

Mobile

Syracuse

Winston‐Salem




Salinas

Baton Rouge


Hickory‐Len

oir‐M

organton

York‐Hanover

Flint


Huntsville




Eau Claire

Amarillo

South Ben

d‐M

ishaw

aka

Joplin

Greeley

Lubbock

Corpus Christi

Salt Lake City

Rockford

Appleton


Ced

ar Rapids


Laredo

Punta Gorda

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Billings

Johnstown

Davenport‐M

oline‐Rock Island

Gainesville

Erie


Peoria

Chico

Norw

ich‐New London

Duluth

Spokane

Lynchburg

Trenton‐Ewing

Pueblo

Green

Bay

Topeka

La Crosse

Wausau

Boulder

Ann Arbor

Durham

‐Chapel Hill



Bingham

ton

Vineland‐M


Evansville

Gulfport‐Biloxi

Cham

paign

‐Urbana

Tuscaloosa


Fayetteville

Janesville

Charleston

Yakima


Lafayette

Saginaw

‐Saginaw

Township North

Springfield

Johnson City

Jackson

Bloomington

Columbus

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

al

Percentage

Chan

ge in

Population


‐40%

‐20%

0%

20%

40%

60%

80%

ntage Chan

ge in

Population


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Knoxville

Green

sboro‐High Point

Bakersfield

Bellingham

Bangor

Colorado Springs


alk



Des Moines‐W

est Des Moines

Ren

o‐Sparks

Toledo

Wichita

Dayton

Tallahassee

Lexington‐Fayette


Youngstown‐W

arren‐Boardman

El Paso

Mobile

Syracuse

Winston‐Salem




Salinas

Baton Rouge


Hickory‐Len

oir‐M

organton

York‐Hanover

Flint


Huntsville




Eau Claire

Amarillo

South Ben

d‐M

ishaw

aka

Joplin

Greeley

Lubbock

Corpus Christi

Salt Lake City

Rockford

Appleton


Ced

ar Rapids


Laredo

Punta Gorda

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Billings

Johnstown

Davenport‐M

oline‐Rock Island

Gainesville

Erie


Peoria

Chico

Norw

ich‐New London

Duluth

Spokane

Lynchburg

Trenton‐Ewing

Pueblo

Green

Bay

Topeka

La Crosse

Wausau

Boulder

Ann Arbor

Durham

‐Chapel Hill



Bingham

ton

Vineland‐M


Evansville

Gulfport‐Biloxi

Cham

paign

‐Urbana

Tuscaloosa


Fayetteville

Janesville

Charleston

Yakima


Lafayette

Saginaw

‐Saginaw

Township North

Springfield

Johnson City

Jackson

Bloomington

Columbus

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

al

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

Anderson

Jacksonville

es‐Ben

ton Harbor

Napa

Iowa City

town‐M

artinsburg

Angeles‐Long …

ork‐Northern New …

verside‐San …

x‐Mesa‐Scottsdale

o‐Naperville‐Joliet

nta‐Sandy Springs‐…

t Worth‐Arlington

adelphia‐Cam

den

‐…pa‐St. Petersburg‐…

e‐Sunnyvale‐Santa …

Sioux Falls

Cam

bridge‐Quincy

eepsie‐Newburgh‐…

it‐W

arren‐Livonia

uston‐Sugar Land‐…

nneapolis‐St. Paul‐…

Baltimore‐Towson

rancisco‐Oakland‐…

Salem

‐Edinburg‐M

ission

rlando‐Kissimmee

Aurora‐Broomfield

acramen

to‐Arden

‐…San Antonio

Kansas City

Columbus

iego

‐Carlsbad

‐San

…rtland‐Vancouver‐…

as Vegas‐Paradise

St. Louis

Jacksonville

Pittsburgh

on‐Norton Shores

nnati‐Middletown

Medford

Austin‐Round Rock

d‐Thousand Oaks‐…

e Coral‐Fort M

yers

Santa Fe

Reading

Modesto

Luis Obispo‐Paso …

dianapolis‐Carmel

Raleigh

‐Cary

and‐Elyria‐Mentor


Worcester

Springfield

Boise City‐Nam

pa

Chattanooga

Ocala

Coeu

r d'Alene

Provo

‐Orem

harlotte‐Gastonia‐…

Memphis

d‐South Portland‐…

Oklahoma City

Asheville

town‐Bethlehem

‐ …aukee‐Waukesha‐…

Tucson

Savannah

rmingham

‐Hoover

Springfield

Richmond

Stockton

Ogden

‐Clearfield

Schen

ectady‐Troy

m Bay‐M

elbourne‐…

rd‐W

est Hartford‐…

Albuquerque

Lancaster

Rapids‐Wyoming

Fresno

ffalo‐Niagara Falls

Madison

Rochester

Akron

Columbia

Tulsa

Fargo

a‐Daytona Beach‐ …

Roanoke

aha‐Council Bluffs

Canton‐M

assillon

Honolulu

Percentage

Chan

ge in

Population


‐100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

Knoxville

Green

sboro‐High Point

Bakersfield

Bellingham

Bangor

Colorado Springs


alk



Des Moines‐W

est Des Moines

Ren

o‐Sparks

Toledo

Wichita

Dayton

Tallahassee

Lexington‐Fayette


Youngstown‐W

arren‐Boardman

El Paso

Mobile

Syracuse

Winston‐Salem




Salinas

Baton Rouge


Hickory‐Len

oir‐M

organton

York‐Hanover

Flint


Huntsville




Eau Claire

Amarillo

South Ben

d‐M

ishaw

aka

Joplin

Greeley

Lubbock

Corpus Christi

Salt Lake City

Rockford

Appleton


Ced

ar Rapids


Laredo

Punta Gorda

Scranton‐W

ilkes‐Barre

Utica‐Rome

Kalam

azoo‐Portage

Fort W

ayne

Billings

Johnstown

Davenport‐M

oline‐Rock Island

Gainesville

Erie


Peoria

Chico

Norw

ich‐New London

Duluth

Spokane

Lynchburg

Trenton‐Ewing

Pueblo

Green

Bay

Topeka

La Crosse

Wausau

Boulder

Ann Arbor

Durham

‐Chapel Hill



Bingham

ton

Vineland‐M


Evansville

Gulfport‐Biloxi

Cham

paign

‐Urbana

Tuscaloosa


Fayetteville

Janesville

Charleston

Yakima


Lafayette

Saginaw

‐Saginaw

Township North

Springfield

Johnson City

Jackson

Bloomington

Columbus

Waterloo‐Ced

ar Falls

Racine

Bloomington‐Norm

al

Percentage

Chan

ge in

Population


‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

Anderson

Jacksonville

Niles‐Ben

ton Harbor

Napa

Iowa City

Hagerstown‐M

artinsburg


New York‐Northern New …

Riverside‐San

…Phoenix‐M

esa‐Scottsdale


Atlanta‐Sandy Springs‐ …

Dallas‐Fort W

orth‐Arlington

Philadelphia‐Cam

den

‐ …Tampa‐St. Petersburg‐…

San Jose‐Sunnyvale‐Santa …

Sioux Falls

Boston‐Cam

bridge‐Quincy

Poughkeepsie‐Newburgh‐ …

Detroit‐W

arren‐Livonia

Houston‐Sugar Land‐ …

Minneapolis‐St. Paul‐…

Baltimore‐Towson

San Francisco‐Oakland‐ …

Salem


ission

Orlando‐Kissimmee


Sacram

ento‐Arden

‐ …San Antonio

Kansas City

Columbus

San Diego

‐Carlsbad

‐San

…Portland‐Vancouver‐…


St. Louis

Jacksonville

Pittsburgh



Medford

Austin‐Round Rock


Cape Coral‐Fort M

yers

Santa Fe

Reading

Modesto



Raleigh

‐Cary


Virginia Beach‐Norfolk‐ …

Worcester

Springfield

Boise City‐Nam

pa

Chattanooga

Ocala

Coeu

r d'Alene

Provo

‐Orem

Charlotte‐Gastonia‐ …

Memphis

Portland‐South Portland‐ …

Oklahoma City

Asheville


‐ …Milw

aukee‐W

aukesha‐…

Tucson

Savannah

Birmingham

‐Hoover

Springfield

Richmond

Stockton

Ogden

‐Clearfield


Palm Bay‐M

elbourne‐ …

Hartford‐W

est Hartford‐…

Albuquerque

Lancaster


Fresno


Madison

Rochester

Akron

Columbia

Tulsa

Fargo

Deltona‐Daytona Beach‐ …

Roanoke


Canton‐M

assillon

Honolulu

Percentage

Chan

ge in

Population


Figure A6: Changes in Population Sizes with Average Excessive Frictions (with

Externalities)

7

Without Differences in Amenities: Without Differences in Efficiency:

Without Differences in Excessive Frictions:

Figure A7: Maps of Changes in Population Sizes

8

Without Differences in Amenities: Without Differences in Efficiency:

Without Differences in Excessive Frictions:

Figure A8: Maps of Changes in Population Sizes with Externalities, ω = 0.02

9

11 12 13 14 15 16 17-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> p

opul

atio

n)

Model Utility = 10

ActualModeled

11 12 13 14 15 16 17-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> p

opul

atio

n)

Counterfactual Utility = 10.0644, Reallocation = 0.18491

ActualAvg. Efficiency

6 8 10 12 14 16 18 20-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> p

opul

atio

n)


ActualAvg. Amenities

0 5 10 15 20-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> p

opul

atio

n)


ActualAvg. Exc. Frictions

Figure A9: Elasticity of Commuting Costs with Respect to Population Equal to 0.25

10

11 12 13 14 15 16 17-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> po

pula

tion)

ActualModeled

10 12 14 16 18-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> po

pula

tion)

Average Efficiency

10 12 14 16 18-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> po

pula

tion)

Average Amenities

9 10 11 12 13 14 15 16 17-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> po

pula

tion)

Average Excessive Frictions

ActualAll Ages: U = 10.089, R = 0.44Age <= 70: U = 10.079, R = 0.44Age <= 65: U = 10.076, R = 0.44

ActualAll Ages: U = 10.122, R = 0.37Ages <= 70: U = 10.159, R = 0.45Ages <= 65: U = 10.174, R = 0.49

ActualAll Ages: U = 10.019, R = 0.20Ages <= 70: U = 10.023, R = 0.21Ages <= 65: U = 10.028, R = 22

Figure A10: Equalizing Differences in One City Characteristic with Age Cutoffs for Hours

Worked

11

10 11 12 13 14 15 16 17-6

-5

-4

-3

-2

-1

0

ln(population)

ln(p

rob

> p

opul

atio

n)

Actual Distribution, Utility = 10Optimal Allocation, Utility = 10.0578

Figure A11: Optimal Allocation with Externalities, ω = 0.02

12

APPENDIX B: DATA APPENDIX

B.1 United States

This section provides a detailed description of the U.S. metropolitan data we use.

Unit of observation. The unit of observation is the metropolitan statistical area (MSA).

A metropolitan statistical area is a collection of counties with at least one urbanized area

of 50,000 or more inhabitants. We use data from 2005 to 2008. Going further back in time

is complex, since the definition of MSAs changed in 2003, and there was a subsequent lag

in the adoption of the new definitions. More recent data (for 2009) are not available yet for

some of the relevant variables.

Production. Measured by Gross Domestic Product by Metropolitan Area. Source: Bu-

reau of Economic Analysis, Regional Economic Accounts.

Population. Data on population come from the Bureau of Economic Analysis, Regional

Economic Accounts.

Private consumption. The measure of consumption used to compute the labor wedge

is private consumption. There are no ready-to-use data on private consumption at the

metropolitan area level. We start by decomposing private consumption into private con-

sumption net of housing services and private consumption of housing services.

We proxy private consumption net of housing services by retail earnings. In particular,

we use retail earnings (at the MSA level) multiplied by private consumption net of housing

(at the U.S. level) divided by retail earnings (at the U.S. level). Data on retail earnings

are defined as personal earnings from retail trade and come from the Bureau of Economic

Analysis Regional Economic Accounts, Table CA05. Data on private consumption net of

housing come from the Bureau of Economic Analysis National Income and Product Accounts

(NIPA), Table 2.3.5. In using this proxy, we follow the literature on interregional risk sharing,

which has used retail sales as a proxy for private consumption (Asdrubali et al., 1996, Hess

and Shin, 1997, Lustig and Van Nieuwerburgh, 2010). Note that those papers use retail

13

sales, rather than retail earnings, by using data from the Survey of Buying Power published

by Sales & Marketing Management. However, that survey was interrupted during the period

2006-2008. Both proxies are very similar, though. For 2005 and 2008 the correlation between

retail earnings and retail sales at the MSA level is about 0.80. In addition, the correlation

between private consumption and retail earnings at the U.S. level for the years 2001-2008 is

0.99.

We proxy private consumption of housing services by taking the sum of aggregate gross

rent of renter-occupied housing and the rental value of the aggregate value of owner-occupied

housing. Both variables are available at the MSA level and come from the annual American

Community Survey run by the U.S. Census Bureau. To compute the rental value of owner-

occupied housing, we assume that the aggregate value of owner-occupied housing is the

discounted sum of future rental flows, taking into account depreciation. The rental value of

owner-occupied housing is then computed as the aggregate value of owner-occupied housing

multiplied by r+δ and divided by 1+r. For the benchmark calculation, we take r = δ = 0.02,

as in the rest of the paper.

Capital stock. Again, there are no ready-to-use capital stock data at the MSA level. We

start by decomposing the capital stock into non-residential and residential capital.

We proxy non-residential capital at the MSA level by using sectoral non-residential capital

stock data at the U.S. level and allocating it to the different MSAs as a function of their

sectoral weights. This is similar to the approach taken by Garofalo and Yamarik (2002) when

estimating state capital stocks. Sectoral non-residential capital stock data come from the

National Economic Accounts from the BEA. We take the sum of private and public capital.

For private non-residential capital we take current-cost net stock of private fixed assets by

industry (Table 3.1ES); for public non-residential capital we take current-cost net stock of

government fixed assets at both the federal level and the state and local level (Table 7.1B).

We then allocate the sectoral capital stock to the MSAs as a function of their shares of

sectoral earnings. In particular, capital stock in sector s in MSA i is computed as the capital

stock in sector s in the U.S. multiplied by earnings in sector s in MSA i divided by earnings

in sector s in the U.S. Data on sectoral earnings both at the MSA and the U.S. levels come

14

from the Regional Economic Accounts of the Bureau of Economic Analysis (Table CA05N).

Residential capital at the MSA level is easier to come by. We take the sum of the aggregate

value of renter-occupied and owner-occupied housing. In the case of owner-occupied housing,

that information is available from the American Community Survey of the U.S. Census

Bureau. In the case of renter-occupied housing, the same data source gives information on

the aggregate gross rent. This allows us to compute the value of rental-occupied housing as

the aggregate gross rent multiplied by 1 + r divided by r + δ. This assumes, as before, that

the value of housing is equal to the discounted sum of future rental streams, where future

rental streams are the same as today’s rental stream corrected for depreciation.

Hours worked. To compute average hours worked we take the total hours worked divided

by the population age 16 years old and above. We use data from the Current Population

Survey (CPS) and compute total hours worked at the MSA level by summing up the total

hours worked by individuals (weighted by their representativeness in the sample) and then

dividing them by all individuals age 16 and above in the sample (weighted by their represen-

tativeness in the sample). To limit errors due to small sample problems, we leave out MSAs

that have information on less than 50 individuals. The share of time worked is then equal

to the average hours worked per day divided by 14.

Wages. Data on average wages per job come from the Bureau of Economic Analysis.

Housing rental prices. As a measure for housing rental prices, we take the median gross

rent of rental-occupied housing. Data at the MSA level are available from the American

Community Survey from the U.S. Census Bureau.

Labor tax rate. We measure the labor tax rate as the federal and state income tax

liability (after credits) divided by total income. Data come from the Current Population

Survey (CPS). As with the case of hours worked, we aggregate federal and state income tax

liability across individuals at the MSA level and divide it by the sum of total income across

those same individuals. As before, individuals are weighted by their representativeness in

the CPS.

15

Consumption tax rate. We take the sum of (i) the sales tax revenue by all local gov-

ernments within the MSA and (ii) the sales tax revenue by the state which we assign to the

different MSAs in function of their weights in the state’s retail income. Data on sales tax

revenue come from State & Local Government Finance, U.S. Census Bureau. This dataset

gives detailed information on all types of revenue for all types of local and state governments.

To compute the sales tax revenue by all local governments at the MSA level, note that

the data are available at the county level, which we then aggregate up to the MSA level. We

select all revenue categories that correspond to sales tax revenue (T09, T10, T11, T12, T13,

T14, T15, T16, and T19). One issue is that the dataset only has information on counties that

respond, but not all do. For those counties that did not respond, the U.S. Census Bureau

imputes values, but only makes those imputed values available at the state level, without

providing a breakdown at the county level. For counties with missing records, we impute the

data by taking the sales tax revenue at the local level in revenue category x (at the state level)

multiplied by retail personal income (at the MSA level) divided by retail personal income

(at the state level). Retail personal income comes from the Regional Economic Accounts of

the BEA. Once we have sales revenue data (reported or imputed) in all categories at the

county level, we aggregate them to the MSA level. For sales tax revenue by states, we assign

it to the MSAs within states using their relative weights in retail personal earnings from

the Regional Economic Accounts of the BEA. The consumption tax rate is then sales tax

revenue divided by private consumption.

Places. Population data of U.S. places come from the 2010 Census.

Amenities. To validate our estimates of amenities, we collect data on 23 observed ameni-

ties at the MSA level. First, we use data on 8 climate variables from www.weatherbase.com

typically used in the literature on amenities: average low in January, annual days in ex-

cess of 90 degrees, annual days below 32 degrees, annual precipitation, number of days with

precipitation, number of days with precipitation > 0.2 inch, relative humidity in July, and

July heat index (average of relative humidity and high temperature). Second, we use the

quality-of-life variables in Rappaport (2008). These come from the Places Rated Almanac

by Savageau (2000) and the Cities Ranked & Rated by Sperling and Sander (2004). From

16

the Places Rated Almanac we take 6 variables (transport, education, crime, arts, health,

and recreation) and from the Cities Ranked & Rated we use 7 variables (education, health,

crime, transport, leisure, arts & culture, and quality of life). All of the 13 variables from the

Places Rated Almanac and Cities Ranked & Rated are constructed such that lower values

correspond to “better”amenities. Third, from Rappaport and Sachs (2003) we use 2 vari-

ables on proximity to water: closeness (< 20 km) to a body of water (Ocean, Gulf, Great

Lakes) and closeness (< 20 km) to a body of water or major river. In Table B.1 we report

the correlations between our estimate of amenities and those 23 measures.

Government spending. We split up government spending into federal government spend-

ing and state and local government spending. To estimate federal spending by MSA we take

earnings by federal civilian government and military at the MSA level, and multiply it by

the ratio of federal government spending in the state of the MSA to the earnings by federal

civilian government and military in the state of the MSA. This way of proceeding is reason-

able, given that the correlation between federal spending and federal earnings at the state

level ranges between 0.95 and 0.98. Federal civilian and military earnings come from the

Regional Economic Accounts of the BEA. Federal government spending by state comes from

the Consolidated Federal Funds Report of the U.S Census Bureau. It measures the sum of

procurement contracts, salaries and wages, and grants, and thus leaves out transfers and

loans. To estimate state and local government spending, we follow a similar procedure. We

take state and local government wages at the MSA level, and multiply it by the ratio of state

and local government spending in the state of the MSA to the state and local government

wages in the state of the MSA. Once again, the rationale is based on the high correlation at

the state level between state and local spending and state and local wages (0.99). State and

local government wages come from the Regional Economic Accounts of the BEA. State and

local government spending by state comes from the State and Local Government Finance

data of the U.S. Census Bureau. It measures the sum of current operations and capital

outlays, thus leaving out transfers and subsidies.

Other measures of excessive frictions. When validating our estimates of excessive

frictions, we use a number of additional variables. Time spent commuting comes from the

17

American Community of the U.S. Census Bureau. Data on unionization come from the

Union Membership and Coverage Database constructed by Hirsch and Macpherson, and

is available online at www.unionstats.com. Finally, to measure land regulation, we use the

Wharton Residential Land Use Regulatory Index. For a description of the data, see Gyourko,

Saiz and Summers (2008).

B.2 China

This section provides a detailed description of the Chinese city-level data we use.

Unit of observation. The unit of observation is Districts under Prefecture-Level Cities.

This corresponds to the urban part of Prefecture-Level Cities, sometimes referred to as the

city proper. Note that Prefecture-Level Cities cover the entire Chinese geography and include

both the urban parts (proxied for by Districts under Prefecture-Level Cities) and the rural

hinterlands. We focus on 2005 and have data on 212 cities.

Production. Measured by Gross Domestic Product at the level ofDistricts under Prefecture-

Level Cities. Source: China City Statistics, China Data Center.

Private consumption. To compute private consumption at the level of Districts under

Prefecture-Level Cities, we multiply retail sales of consumer goods at the level of Districts

under Prefecture-Level Cities by the ratio of final consumption expenditure to total retail

sales of consumer goods at the national level. Source: China City Statistics and National

Statistics, China Data Center.

Population. Population and Population age 15 years and above. Source: China City

Statistics, China Data Center.

Hours worked. To compute average hours worked we take the total hours worked divided

by the population 15 years old and above. We use the 2005 1% Population Survey to get

data on the population age 15 years and above, population employed, and average hours

worked by the employed. We then multiply population employed by average hours worked

18

by employed and divide it by the population age 15 years and above. These data are available

for most Prefecture-Level Cities but often not for Districts under Prefecture-Level Cities. In

order not to lose too many observations, we use the data at the level of Prefecture-Level

Cities.

Parameter values. Bai et al. (2006) provide time series estimates for the capital share

of income and for the real interest rate. Taking the average for 2000-2005, we set θ = 0.5221

and r = 0.2008. To be consistent with the case of the U.S., where we took ψ from McGrattan

and Prescott (2010), we use the same formula as they do: ψ = (1−τh)(1−θ)(1−h)1+τc)ch

, where c is

consumption per capita (data defined above), h are hours worked as share of total hours

(average hours worked per year as defined above divided by 5110), τh is the tax rate on

labor (defined as personal income tax as a share of personal income) and τ c the tax rate on

consumption (defined as the sum of consumption tax and value added tax as a share of total

consumption expenditures). The data needed to compute these tax rates come from the

National Bureau of Statistics of China and are for 2004. This gives us a value ψ = 1.5247.

Appendix References

Asdrubali, Pierfederico, Bent E. Sorensen, and Oved Yosha. 1996. “Channels of

Interstate Risk Sharing: United States 1963-1990.”Quarterly Journal of Economics, 111(4):

1081-1110.

Garofalo, Gasper A., and Steven Yamarik. 2002. “Regional Convergence: Evidence

from a New State-by-State Capital Stock Series.”Review of Economics and Statistics, 84(2):

316-323.

Gyourko, Joseph, Albert Saiz, and Anita Summers. 2008. “A New Measure of the

Local Regulatory Environment for Housing Markets: The Wharton Residential Land Use

Regulatory Index.”Urban Studies, 45(3): 693-729.

Hess, Gregory D., and Kwanho Shin. 1997. “International and Intranational Business

Cycles.”Oxford Review of Economic Policy, 13(3): 93-109.

Lustig, Hanno, and Stijn Van Nieuwerburgh. 2010. “How Much Does Household Col-

19

lateral Constrain Regional Risk Sharing?”Review of Economic Dynamics, 13(2): 265-294.

Savageau, David. 2000. Places Rated Almanac, with Ralph d’Agostino. Foster City, CA:

IDG Books Worldwide, Inc.

Sperling, Bert, and Peter Sander. 2004. Cities Ranked & Rated, First Edition. Hobo-

ken, NJ: Wiley Publishing, Inc.

20

Table B.1: Correlation between Estimated Amenities and Standard Measures of Amenities

Correlation P-value Expected Sign

Weather

Average low in January 0.2306 0.0015 Yes

Annual days in excess of 90 degrees 0.2744 0.0001 No

Annual days below 32 -0.1628 0.0276 Yes

Annual precipitation -0.1581 0.0285 Yes

Number of days with precipitation -0.2523 0.0005 Yes

Number of days with precipitation > 0.2 inch -0.2640 0.0058 Yes

Relative humidity in July -0.2006 0.0103 Yes

July heat index -0.0942 0.2315 Yes

Places Rated Almanac

Transport -0.4950 0.0000 Yes

Education -0.4923 0.0000 Yes

Crime -0.0542 0.4616 Yes

Arts -0.4950 0.0000 Yes

Health -0.5252 0.0000 Yes

Recreation -0.3075 0.0000 Yes

Cities Ranked & Rated

Education -0.3996 0.0000 Yes

Health -0.1310 0.0739 Yes

Crime -0.0824 0.2625 Yes

Transport -0.3489 0.0000 Yes

Leisure -0.0377 0.6084 Yes

Arts & Culture -0.3879 0.0000 Yes

Quality of Life -0.2258 0.0019 Yes

Distance to Water

< 20 km to a body of water 0.0957 0.1867 Yes

< 20 km to a body of water or major river 0.1195 0.0988 Yes

21

Table B.2: Correlation between Effi ciency and Standard Measures of Productivity


Wages 0.7925 0.0000 Yes

Labor productivity 0.9003 0.0000 Yes

Table B.3: Correlation between Labor Wedges and Standard Measures of Frictions


Taxes

Labor tax rate 0.1915 0.0078 Yes

Consumption tax rate 0.2032 0.0047 Yes

Joint income & consumption tax* 0.2667 0.0002 Yes

Government spending

Federal spending per person 0.2589 0.0003 Yes

State & local spending per person 0.1472 0.0416 Yes

Total government spending per person 0.2821 0.0001 Yes

Commuting costs

Share of work time spent commuting 0.3947 0.0000 Yes

Unionization

Percentage unionization 0.1038 0.1702 Yes

Percentage unionization private sector 0.1523 0.0436 Yes

Percentage unionization public sector 0.0229 0.7633 Yes

Land regulation

Wharton index (WRLURI) -0.0728 0.3711 No

*This is the correlation between (1− τ) and (1− τh)/(1− τ c). All other correlations are with τ .

22

Table B.4: Data and Estimated City Characteristics

Data City Characteristics

Name Pop GDP Cons Cap Hours lnAi ln γi ln gi(’000s) (m$) (m$) (m$) (Annual)

Akron, OH 699 26777 19525 101675 1210 7.518 1.013 -0.112

Albany-Schenectady-Troy, NY 851 36605 25750 153250 1209 7.563 0.995 -0.176

Albuquerque, NM 823 33174 23325 138000 1215 7.519 1.019 -0.163

Allentown-Bethlehem-Easton, PA-NJ 797 27946 21375 131250 1188 7.400 1.070 -0.209

Amarillo, TX 241 8763 6776 30900 1260 7.492 0.990 0.220

Anderson, IN 131 3214 2313 13600 942 7.354 1.151 0.976

AnnArbor, MI 346 17743 9458 97525 1219 7.583 1.030 0.471

Appleton, WI 217 8953 6674 29525 1324 7.558 0.955 0.290

Asheville, NC 400 12730 11450 60025 1228 7.314 1.067 -0.149

Atlanta-SandySprings-Marietta, GA 5174 258875 153500 1160000 1361 7.562 0.998 -1.066

Augusta-RichmondCounty, GA-SC 525 16913 11800 71100 1114 7.425 1.090 0.164

Austin-RoundRock, TX 1559 73118 45450 322000 1292 7.561 1.004 -0.443

Bakersfield, CA 777 25476 17825 130500 1126 7.363 1.118 -0.045

Baltimore-Towson, MD 2659 125750 83800 630500 1215 7.562 1.005 -0.701

Bangor, ME 148 5062 5459 19775 1152 7.470 0.950 0.108

BatonRouge, LA 759 35904 19375 110450 1151 7.765 0.939 0.119

Beaumont-PortArthur, TX 377 13840 10121 42575 1021 7.675 0.965 0.372

Bellingham, WA 191 6958 6212 35925 1278 7.352 1.028 0.077

Billings, MT 149 6159 5310 28825 1213 7.502 0.972 0.386

Binghamton, NY 246 7093 5535 28400 1034 7.423 1.085 0.515

Birmingham-Hoover, AL 1104 51878 33750 213000 1179 7.647 0.970 -0.211

Bloomington, IN 182 5381 3538 23100 1172 7.328 1.136 0.687

Bloomington-Normal, IL 162 7698 4268 24575 1348 7.653 0.958 0.712

BoiseCity-Nampa, ID 574 23479 18875 101075 1290 7.479 0.992 -0.245

Boston-Cambridge-Quincy, MA-NH 4484 280550 153750 1382500 1205 7.759 0.930 -0.813

Boulder, CO 287 16504 9513 87000 1270 7.650 0.970 0.470

Bremerton-Silverdale, WA 238 8128 6813 51525 950 7.431 1.088 0.567

Bridgeport-Stamford-Norwalk, CT 892 78101 45500 380500 1183 7.999 0.769 -0.029

Brownsville-Harlingen, TX 382 6836 6532 30400 903 7.163 1.203 0.263

Buffalo-NiagaraFalls, NY 1131 41466 27225 158000 1094 7.557 1.035 -0.160

Burlington-SouthBurlington, VT 207 9724 7929 40925 1324 7.561 0.931 0.186

Canton-Massillon, OH 408 12708 10700 48100 1213 7.384 1.048 -0.042

CapeCoral-FortMyers, FL 573 21443 21100 136500 1149 7.365 1.041 -0.328

CedarRapids, IA 251 11535 8286 42150 1313 7.599 0.945 0.292

Champaign-Urbana, IL 222 7530 4575 37275 1291 7.306 1.139 0.548

Charleston, WV 304 13520 7628 40900 1113 7.750 0.946 0.572

Charlotte-Gastonia-Concord, NC-SC 1612 112575 52075 332500 1343 7.934 0.826 -0.285

Chattanooga, TN-GA 511 19778 17050 84250 1231 7.479 0.985 -0.228

Chicago-Naperville-Joliet, IL-IN-WI 9474 494675 272750 2340000 1204 7.653 0.991 -1.190

Chico, CA 218 5717 6061 37450 936 7.255 1.137 0.412

Cincinnati-Middletown, OH-KY-IN 2130 94574 53300 348750 1232 7.615 1.003 -0.478

Cleveland-Elyria-Mentor, OH 2101 101402 53800 388000 1151 7.703 0.978 -0.381

Coeurd’Alene, ID 132 3899 4360 22925 1190 7.212 1.071 -0.024

ColoradoSprings, CO 604 22581 16600 127750 1184 7.383 1.087 -0.022

Columbia, SC 709 28344 20225 118750 1223 7.507 1.020 -0.111

Columbus, GA-AL 288 10265 6191 51500 928 7.556 1.075 0.679

Columbus, OH 1743 86089 56300 341750 1292 7.629 0.955 -0.548

CorpusChristi, TX 413 14643 10731 64825 1136 7.459 1.058 0.217

23

Table B.4: Data and Estimated City Characteristics (cont’d)



Dallas-FortWorth-Arlington, TX 6067 349575 195000 1565000 1295 7.689 0.951 -1.041

Davenport-Moline-RockIsl, IA-IL 375 15734 10488 49375 1131 7.688 0.952 0.351

Dayton, OH 840 33268 18975 123250 1125 7.598 1.029 0.054

Deltona-Daytona-Ormond, FL 495 12019 12625 78025 987 7.172 1.165 -0.070

Denver-Aurora-Broomfield, CO 2430 141075 75125 754500 1387 7.588 1.000 -0.617

DesMoines-WestDesMoines, IA 540 32351 18150 104425 1418 7.765 0.879 0.064

Detroit-Warren-Livonia, MI 4466 199875 119250 934750 1081 7.627 1.014 -0.778

Duluth, MN-WI 274 9359 6959 39150 1124 7.461 1.056 0.421

Durham-ChapelHill, NC 473 29531 11675 95050 1215 7.894 0.895 0.463

EauClaire, WI 157 5645 4742 20850 1331 7.425 0.998 0.248

ElPaso, TX 726 23903 16250 163250 1026 7.332 1.154 0.090

Erie, PA 279 8941 6948 32375 1116 7.469 1.046 0.381

Eugene-Springfield, OR 341 10888 9882 55850 1069 7.379 1.074 0.179

Evansville, IN-KY 349 15175 8434 48050 1096 7.733 0.962 0.523

Fargo, ND-MN 191 9104 7030 31625 1470 7.567 0.898 0.025

Fayetteville, NC 350 14065 7462 77925 1060 7.514 1.090 0.559

Fayetteville-Springd-Rog, AR-MO 427 16421 10020 66975 1396 7.404 1.069 0.112

Flint, MI 435 11748 9961 54575 1044 7.319 1.123 0.164

FortCollins-Loveland, CO 284 10246 8405 62275 1222 7.314 1.091 0.180

FortWayne, IN 407 16263 9900 52875 1184 7.613 1.000 0.333

Fresno, CA 889 27071 21800 142250 1044 7.355 1.114 -0.150

Gainesville, FL 254 9035 6540 44600 1221 7.378 1.084 0.379

GrandRapids-Wyoming, MI 773 32458 22150 134500 1248 7.530 1.011 -0.140

Greeley, CO 238 6649 4890 39525 1341 7.091 1.194 0.237

GreenBay, WI 300 13655 8099 45250 1255 7.656 0.966 0.448

Greensboro-HighPoint, NC 690 31718 23100 106250 1151 7.716 0.913 -0.042

Gulfport-Biloxi, MS 237 9128 6650 31723.089 1090 7.707 0.930 0.538

Hagerstown-Martinsburg, MD-WV 257 7474 7335 34325 1316 7.217 1.065 -0.391

Harrisburg-Carlisle, PA 526 26279 15900 108850 1292 7.622 0.975 0.124

Hartford-W and E Hartford, CT 1185 70031 40075 285750 1227 7.774 0.909 -0.190

Hickory-Lenoir-Morganton, NC 358 11617 8316 44850 1275 7.369 1.081 0.167

Holland-GrandHaven, MI 257 9101 5217 46050 1321 7.312 1.141 0.499

Honolulu, HI 902 45003 28425 281500 1175 7.545 1.032 -0.083

Houston-SugarLand-Baytown, TX 5529 359325 149250 1605000 1228 7.804 0.941 -0.790

Huntington-Ashland, WV-KY-OH 284 8774 6708 28625 1009 7.551 1.027 0.485

Huntsville, AL 382 17655 11250 61950 1348 7.598 0.965 0.174

Indianapolis-Carmel, IN 1680 91450 50500 346750 1267 7.721 0.939 -0.370

IowaCity, IA 146 6497 4036 31075 1299 7.497 1.034 0.722

Jackson, MI 162 4741 3612 20050 1103 7.375 1.097 0.666

Jacksonville, FL 1284 57619 41575 258250 1258 7.543 0.989 -0.465

Jacksonville, NC 162 6008 3065 39750 931 7.519 1.107 1.012

Janesville, WI 159 4856 4441 19425 1046 7.451 1.040 0.584

JohnsonCity, TN 192 5575 4466 20850 997 7.472 1.064 0.660

Johnstown, PA 146 3710 3264 15025 1219 7.224 1.118 0.391

Joplin, MO 170 5161 4725 18125 1220 7.390 1.025 0.261

Kalamazoo-Portage, MI 322 11246 8018 54800 1152 7.411 1.083 0.342

KansasCity, MO-KS 1970 95462 59225 360250 1298 7.630 0.964 -0.566

Knoxville, TN 675 27894 24025 113750 1028 7.655 0.944 -0.052

LaCrosse, WI-MN 131 4910 3732 17775 1341 7.455 1.005 0.493

Lafayette, LA 254 16222 8403 58200 1261 7.861 0.866 0.616

24




Lancaster, PA 496 18342 14775 75575 1244 7.450 1.018 -0.107

Lansing-EastLansing, MI 455 17650 11350 89775 1150 7.462 1.078 0.268

Laredo, TX 229 5573 5192 25225 1090 7.232 1.136 0.315

LasVegas-Paradise, NV 1792 91456 59575 458000 1263 7.586 0.986 -0.530

Lexington-Fayette, KY 443 21499 14625 83150 1284 7.631 0.946 0.103

Los Angeles-L Beach-S Ana, CA 12817 683025 433750 3972500 1207 7.596 0.998 -1.441

Lubbock, TX 267 8788 8033 35175 1105 7.466 1.015 0.231

Lynchburg, VA 241 7897 5695 28250 1209 7.435 1.059 0.428

Madison, WI 551 31061 20325 127750 1404 7.651 0.914 -0.103

McAllen-Edinburg-Mission, TX 696 12419 13850 54400 1078 7.041 1.190 -0.510

Medford, OR 198 6091 8251 34200 982 7.386 0.983 -0.164

Memphis, TN-MS-AR 1272 61145 39400 258750 1163 7.660 0.967 -0.264

Milwaukee-Waukesha-W Allis, WI 1541 78537 40800 276500 1206 7.735 0.952 -0.248

Minneapolis-St.Paul-Bl., MN-WI 3181 182875 103200 797500 1315 7.686 0.947 -0.740

Mobile, AL 402 14009 10853 56200 1180 7.456 1.039 0.130

Modesto, CA 506 14674 13750 91375 1186 7.182 1.131 -0.281

Muskegon-NortonShores, MI 174 4635 5132 23025 1151 7.224 1.066 -0.150

Napa, CA 132 6876 4618 46325 1169 7.557 1.014 0.828

Naples-MarcoIsland, FL 311 14415 12000 102625 1186 7.449 1.025 0.160

New York-N NJ-L Isl, NY-NJ-PA 18897 1166000 609000 5540000 1164 7.784 0.930 -1.479

Niles-BentonHarbor, MI 160 5207 3442 24400 1095 7.421 1.098 0.787

Norwich-NewLondon, CT 265 12835 8498 66950 1243 7.548 1.007 0.422

Ocala, FL 317 7339 8088 41375 1066 7.143 1.136 -0.166

Ogden-Clearfield, UT 511 15400 11975 73375 1291 7.240 1.125 -0.136

OklahomaCity, OK 1181 51753 32475 227250 1225 7.554 1.019 -0.256

Omaha-CouncilBluffs, NE-IA 824 42101 24550 159000 1325 7.652 0.959 -0.098

Orlando-Kissimmee, FL 2003 98517 63775 514250 1299 7.530 1.011 -0.616

Oxnard-Th Oaks-Ventura, CA 793 34443 26950 224500 1295 7.376 1.053 -0.371

PalmBay-Melbourne-Titusv, FL 532 16982 14175 86725 1205 7.301 1.098 -0.134

Pensacola-FerryPass-Brent, FL 450 12945 10575 67700 1138 7.265 1.133 0.055

Peoria, IL 370 15944 8647 54125 1291 7.594 1.009 0.374

Philadel-Cam-Wil, PA-NJ-DE-MD 5813 314925 178750 1302500 1157 7.750 0.939 -0.932

Phoenix-Mesa-Scottsdale, AZ 4089 179325 142750 1029250 1257 7.443 1.022 -1.164

Pittsburgh, PA 2360 107675 65500 418750 1131 7.671 0.977 -0.506

Portland-S Portl-Biddeford, ME 512 23439 18675 110400 1294 7.520 0.969 -0.183

Portland-Vanc-Beav, OR-WA 2147 105034 62375 451500 1272 7.606 0.992 -0.549

Poughkeepsie-Newb-Middlet, NY 667 19865 19650 124000 1204 7.187 1.114 -0.540

Provo-Orem, UT 505 12487 10836 58375 1241 7.144 1.158 -0.218

Pueblo, CO 153 3612 3589 18725 1066 7.177 1.154 0.460

PuntaGorda, FL 152 3461 4492 27675 895 7.135 1.157 0.341

Racine, WI 198 6753 4027 24925 1202 7.455 1.084 0.694

Raleigh-Cary, NC 1021 49081 30550 212750 1379 7.540 0.995 -0.327

Reading, PA 399 13992 12075 61225 1300 7.368 1.023 -0.266

Reno-Sparks, NV 405 19833 14475 93025 1271 7.579 0.963 0.083

Richmond, VA 1201 58495 33100 235500 1260 7.633 0.987 -0.216

Riverside-S Bernard-Ontario, CA 4009 109250 108250 741000 1086 7.169 1.155 -1.194

Roanoke, VA 295 12060 9356 42050 1362 7.515 0.955 -0.012

Rochester, NY 1034 43501 26400 150500 1206 7.614 0.995 -0.150

25



Name Pop GDP Cons Capital Hours lnAi ln γi ln gi(’000s) (m$) (m$) (m$) (Annual)

Rockford, IL 347 11746 8171 42100 1227 7.446 1.056 0.262

Sacramento-Arden-Arc-Rosev, CA 2070 90391 69250 581250 1111 7.485 1.042 -0.599

Saginaw-Sag Township N, MI 204 6461 5181 25950 991 7.509 1.047 0.642

St.Louis, MO-IL 2797 120875 73275 460500 1269 7.566 1.010 -0.695

Salem, OR 382 11301 9027 63600 1160 7.244 1.145 0.147

Salinas, CA 406 17801 13150 116000 1108 7.496 1.040 0.231

SaltLakeCity, UT 1083 57691 40825 245250 1382 7.611 0.925 -0.509

SanAntonio, TX 1957 74146 53200 323000 1155 7.496 1.039 -0.555

SanDiego-Carlsbad-S Marcos, CA 2957 158650 104000 1045000 1177 7.575 1.008 -0.712

SanFrancisco-Oakl-Frem, CA 4206 293150 181750 1627500 1227 7.778 0.891 -0.883

SanJose-Sunnyvale-StaClara, CA 1778 137975 74900 730000 1273 7.841 0.869 -0.390

SanLuisObispo-PasoRobles, CA 261 10093 9309 83125 1109 7.327 1.082 0.206

SantaCruz-Watsonville, CA 251 9547 10199 76625 1186 7.278 1.051 -0.202

SantaFe, NM 142 6360 6021 36250 1004 7.621 0.939 0.612

SantaRosa-Petaluma, CA 463 19457 17275 141000 1193 7.373 1.049 -0.141

Savannah, GA 325 12295 8880 55500 1167 7.477 1.045 0.322

Scranton-Wilkes-Barre, PA 549 18089 15200 68675 1070 7.502 1.024 0.024

Seattle-Tacoma-Bellevue, WA 3274 202025 128000 992500 1258 7.723 0.914 -0.802

SiouxFalls, SD 224 13177 7905 48325 1584 7.636 0.902 0.235

SouthBend-Mishawaka, IN-MI 316 11807 7571 41625 1187 7.541 1.029 0.416

Spokane, WA 452 16456 14875 74225 1219 7.426 1.005 -0.226

Springfield, IL 206 8049 5224 33025 1138 7.551 1.030 0.657

Springfield, MA 687 21010 18075 106400 1107 7.335 1.096 -0.174

Springfield, MO 414 13598 12750 53375 1255 7.383 1.010 -0.320

Stockton, CA 665 18548 17275 121250 1052 7.218 1.152 -0.162

Syracuse, NY 645 25158 16350 93400 1123 7.592 1.012 0.106

Tallahassee, FL 350 12234 8531 60250 1301 7.335 1.088 0.118

Tampa-St.Petersb-Clearwater, FL 2693 107575 83950 500500 1187 7.491 1.017 -0.836

Toledo, OH 652 25597 16875 95175 1159 7.571 1.014 0.062

Topeka, KS 228 8136 5129 30000 1323 7.423 1.067 0.466

Trenton-Ewing, NJ 364 22767 12400 102475 1238 7.774 0.913 0.413

Tucson, AZ 983 29976 23750 168500 1068 7.319 1.130 -0.211

Tulsa, OK 898 41612 24175 168250 1303 7.577 1.005 -0.122

Tuscaloosa, AL 203 7598 4863 32550 1269 7.434 1.070 0.566

Utica-Rome, NY 294 8256 7021 33325 1061 7.381 1.087 0.332

Vineland-Millville-Bridgeton, NJ 155 4726 4226 20825 1085 7.395 1.061 0.549

VirginiaBeach-Norf-Newp, VA-NC 1657 72609 42475 362500 1204 7.520 1.053 -0.356

Visalia-Porterville, CA 416 10626 8933 58875 1042 7.228 1.164 0.171

Waterloo-CedarFalls, IA 163 7018 4393 21725 1271 7.632 0.967 0.697

Wausau, WI 129 5379 4010 17800 1360 7.543 0.955 0.498

Wichita, KS 593 25810 16400 88275 1235 7.624 0.976 0.071

Winston-Salem, NC 457 21390 14225 64750 1269 7.697 0.916 0.128

Worcester, MA 781 26898 22275 143250 1178 7.352 1.078 -0.289

Yakima, WA 231 6877 5143 30400 1028 7.410 1.101 0.592

York-Hanover, PA 416 14247 10681 65325 1169 7.406 1.075 0.159

Youngstown-War-Boardm, OH-PA 573 17030 15000 66275 948 7.504 1.044 0.097

26

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