Lindahl Lecture 1: The Economics of Cities

Edward L. Glaeser

Harvard University

Cities Result from Three Forces

• Agglomeration Economies and Social Interactions– These are the magic of urban areas

• Urban Technologies: Bricks and Mortar, Trains and Cars

• Government Policies– Both local and national

The Plan of these Lectures

• In lecture 1, I will focus on agglomeration economies– what makes cities productive and attractive.

• In lecture 2, I will focus on social interactions and other effects of proximity

• In lecture 3, I will address both the urban technologies and the role of government

Plan of this Lecture

• Overview of Urban Economics

• Measuring Agglomeration based on location patterns

• Lessons from Urban Growth

• Urban Labor Markets

• Learning, Information and Cities– some theory

The Heart of Urban Economics: Spatial Equilibrium

• Workers must be indifferent across space:U(Wages, Amenities, Prices)=U

Higher wages must be offset be either lower amenities or higher prices.

• Firms must be as well: Profits(Wages, Prices, Productivity)=0

– Higher wages must be offset by either higher productivity or higher prices.

• There is also a housing supply equilibrium that will be addressed in lecture 3.

An Easy Example

• Assume wages are fixed at w– and that commuting costs equal t*distance from the city– then spatial equilibrium implies that rents must decline by t*distance from city.

• Rents or housing values will be higher in areas with higher amenities or better schools.

Housing Prices and Temperature 1990

January mean temperature

Median house value Fitted values

4.3 71.4

35600

328900

Abilene,

Akron, O Albany,

Albany-S

Albuquer

Alexandr

Allentow

Altoona,Amarillo

Anchorag

Ann Arbo

AnnistonAppleton Ashevill

Athens,

Atlanta,

Atlantic

Auburn-OAugusta-Austin-S

Bakersfi

Baltimor

Bangor, Baton Ro

Beaumont

Bellingh

Bergen-P

BillingsBiloxi-G

Binghamt

BirminghBismarck BloomingBloomingBoise Ci

Boston-W

Boulder-

Bremerto

Brownsvi

Bryan-CoBuffalo-

Burlingt

Canton-MCasper, Cedar Ra

Champaig Charlest

Charlest

Charlott

Charlott

ChattanoCheyenne

Chicago,

Chico-Pa

Cincinna

Clarksvi

ClevelanColorado

ColumbiaColumbia

Columbus

Columbus

Corpus C

CorvalliDallas,

DanvilleDavenpor

Dayton-SDaytona

Decatur,Decatur,

Denver,

Des MoinDetroit,

Dothan,

Dover, D

Dubuque,Duluth-S

Dutchess

Eau ClaiEl Paso,

Elkhart-Elmira,

Enid, OK

Erie, PAEugene-S

EvansvilFargo-Mo Fayettev

Fayettev

Flagstaf

Flint, M FlorenceFlorence

Fort ColFort Lau

Fort MyeFort Pie

Fort SmiFort Way

Fort WorFresno,

Gadsden,

GainesviGalvestoGary, IN GoldsborGrand Fo

Grand JuGrand RaGreat Fa

Greeley,Green Ba

GreensboGreenvil

Greenvil

HagerstoHamiltonHarrisbu

Hartford

HattiesbHickory-

Honolulu

Houma, LHouston,

Hunting t

HuntsvilIndianap

Iowa Cit

Jackson,Jackson,

Jackson,

JacksonvJacksonv

JamestowJanesvil

Jersey C

Johnson Johnstow

JonesborJoplin,

KalamazoKankakeeKansas CKenosha,

Killeen-KnoxvillKokomo,

La CrossLafayett

Lafayett Lake ChaLakeland

Lancaste

Lansing-

Laredo,

Las Cruc

Las Vega

Lawrence

Lawton,

Lewiston

Lexingto

Lima, OHLincoln, Little R

Longview

Los Ange

Louisvil Lubbock,Lynchbur Macon, G

Madison,

Mansfiel

McAllen-

Medford- MelbournMemphis,

Merced, Miami, F

Middlese

MilwaukeMinneapo

MissoulaMobile,

Modesto,

Monmouth

Monroe, Montgome

Muncie,

Nashvill

Nassau-SNew Have

New Lond

New Orle

New York

Newark,

Newburgh

Norfolk-

Oakland,

Ocala, FOdessa-MOklahoma

Olympia,

Omaha, N

Orange C

Orlando,

Owensbor

Panama CParkersb

PensacolPeoria-P

Philadel

Phoenix-

Pine BluPittsbur

Pittsfie

Pocatell

Portland

Portland

Providen

Provo-Or

Pueblo,

Racine,

Raleigh-

Rapid Ci

Reading,Redding,

Reno, NV

Richland

Richmond

Riversid

Roanoke,Rocheste

Rocheste

RockfordRocky Mo

Sacramen

Sag inaw-

St. Clou

St. Jose

St. LouiSalem, O

Salinas,

Salt Lak

San AngeSan Anto

San Dieg

San Fran

San Jose

San Luis

Santa BaSanta Cr

Santa Fe

Santa Ro

Sarasota

SavannahScranton

Seattle-

Sheboyga

Sherman-Shrevepo

Sioux Ci

Sioux FaSouth Be

Spokane,Springfi Springfi

Springfi

State Co

Stockton

Sumter,

SyracuseTacoma,

Tallahas Tampa-St

Terre HaTexarkan

Toledo, Topeka,

Trenton,

Tucson,

Tulsa, O TuscalooTyler, TUtica-Ro

Vallejo-

Ventura,

Victoria

Vineland Visalia-

Waco, TX

Washing t

WaterlooWausau,

West Pal

Wheeling

Wichita,Wichita

Williams

Wilmingt

Wilmingt

Yakima,

Yolo, CA

York, PA

Youngsto

Yuba Cit

Yuma, AZ

Why locate together?

• Cities can come in principle for two reasons

• First, a desire to be next to some exogenous attribute, like a mine or a port

• Second, a desire to be next to the other inhabitants of the city

Why Cities?

• As such, cities are defined as the absence of physical space between people and firms

• They always occur in an attempt to eliminate transportation costs for goods, people and ideas

• The empirical questions revolve around which are these are more important

Moving Goods, People and Ideas

• Cities are originally about moving goods

• Every large city in the U.S. before 1880 is on a river and most are where the river meets the sea.

• Local Feedback where producers move to be close to consumers (Krugman).

Moving People

• Modern big cities specailize in business services– these require fact to face contact.

• Cities allow works to switch employers and industires, which provides insurance and better search.

• Proximity to other people isn’t just productive, it’s also fun (city as marriage market).

Moving Ideas

• Ideas, like everything else, move better over short distances (face-to-face)

• Jaffe, Trajtenberg and Henderson show that patent citations are geographically localized.

• Idea-intensive industries (finance, the arts) remain core parts of urban growth.

• Urban edge in idea production makes cities important (Athens, Florence).

The Impact of Proximity

• While city location is a choice, it is also interesting because it shapes outcomes

• Firms may be more productive in dense areas (Ciccone and Hall, 1996)

• Workers may learn more quickly in dense areas

• It may be easier to steal in dense areas

• Our beliefs are formed by our neighbors

Measuring Agglomeration (Ellison, Glaeser, JPE, 1997)

• How should we measure the amount of agglomeration or people or industry?

• I think measures should generally be model-driven, i.e. reflect a parameter in some sort of a model.

• Assume profits have the following form:

kikl

likliki ueLogLog

))(1()()(

Where we assume• Individual shocks follow a weibull distribution• The spillover effect takes on a value of 1 with

probability gs

• The mean and variance of profits are:

)1( and iina

j

i

i

i

j

i

ixxVarxE

Together these assumptions give us that:

and

)1( 1 222

snasna

jj

ii

iii zxxsE

Properties of the Index

• Easy to compute with available data• Easy benchmark with no spillover/natural

advantage version• Comparable across industries with

different sizes of firms• Comparable across different levels of

aggregation• Not good at dealing with issues of actual

location

Facts on agglomeration

• Median estimate of gamma is .026; mean is .051.

• A few industries are extremely concentrated– fur goods (.6), costume jewelry (.3)

• Many are not– cane sugar refining,

• A few really change when we correct for plant size (vaccuum cleaners)

Does Natural Advantage Explain Agglomeration (AER, 1999)

• To extend the JPE paper, we try to control for local characteristics

''

10

and

)()()(

sis

isi

llilsli

ssis

xEzy

mfgLogpopLogLog

What do the variables mean?

• The delta is industry specific and allows different industries to respond differently to costs

• The beta is the coefficient to estimate that is “cost” specific (i.e. electricity, labor, etc.)

• The y variables are state cost specific (i.e. price of electricity in kansas)

• The z variables are industry cost specific (i.e. how much does that industry use that input)

The Empirical Strategy

• Regress state/industry shares on characteristics and then ask how much is explained

• Characteristics include energy prices, labor costs, proximity to the coast, proximity to consumers, etc.

• Some are quantities; some are prices.

Overall Results

• Controlling for all of these variables reduces the mean gamma across industries from .051 to .048.

• When we allow 2 and 3 digit industry dummies (we are using 4 digit industries) concentration falls to .045 and .041

• Since many industries aren’t very concentrated, this explains some portion of those industries, but little of the highly concentrated industries.

The Dynamics of Industrial Concentration (REStat, 2002)

• How permanent are concentrations of industries– Krugman (1991), e.g.

)ˆ()ˆ2ˆ(

ˆˆˆˆ

21

2

11

istititit

s sis

iststststististist

VarGGG

ssG

ssssss

Empirical Results

• Use the Census Longitudinal Research Database with plant level data for all manufacturing

• Estimates of beta are around -.06 for 5 year patterns

• Mean reversion would cause concentration to decline by 12 percent every 5 years,

• But this is made up for by the concentration of new firms

An Extension: New Births, Closures, Etc.

• We can extend the methodology to look at what sort of changes create mean reversion

• Closures are more likely in places with initial concentration

• New Openings are more likely in places with less initial concentration (equally of affiliated and unaffiliated plants)

Co-Agglomeration (NBER Working Paper, 1997, Dumais + E/G)

• To add to our knowledge of the sources of agglomeration, we look at which industries colocate near one another.

• Changes specification: regress growth in employment on presence of other industries in initial period.

• Levels specification; regress employment on presence of other industries (BUT THERE IS A REFLECTION PROBLEM)

• All measures of colocation are normalized to have standard deviation of 1

Suppliers and Consumers

• Use Input-Output matrices to calculate the extent that an industry buys to or sells from other industries.

• Use that matrix to calculate the extent that a state or MSA is supplier or customer

• In levels, .06 for customers, .01 for suppliers.

• In State changes, .04 and .03• In MSA changes, .02 and 0

Labor Supply

• Use occupation data to figure out who uses the same type of workers

• Calculate a similarity index across and ask which places have industries that use similar workers

• In state levels, the coefficient is .41

• In MSA changes, the coefficient is.43

• In State Changes, the coefficient is .18

Idea Flows

• Option 1: Use the Scherer input output matrix for patent flows

• Option 2: Use patents of co-ownership, excluding those firms with supply/demand relationship

• In levels, the coefficients are .04 and .03

• State change coefficients are -.01 and .06

• MSA changes coefficients are 0 and .08

Urban Growth Underpinnings

typroductiviA

p)(price capital untradedZ

w)(wagelabor

r)(price capital traded

L

K

pZwLrKZLAK 1Profits Firm

Worker utility equals CN-mw = U, where C is a consumption amenity index.

There is a fixed supply of Z in the city denoted Z.

Using the first order condition for firms, and these two conditions then gives us, using the notation that lnx=x~

)1)(1(

~)1(

)1)(1(

)~~(

)1)(1(

~1

~

)1)(1(

)1)(~~

(~

)1)(1(

)~~~

)(1(

)1)(1(

)~~(

)1)(1(

~

1

~~

m

m

m

rm

m

Am

m

Zm

m

mCUw

m

UC

m

r

m

A

m

ZL

)1)(1(

~)1(

)1)(1(

)~~(

)1)(1(

~1

~

)1)(1(

)1)(~~

(~

)1)(1(

)~~~

)(1(

)1)(1(

)~~(

)1)(1(

~

1

~~

m

m

m

rm

m

Am

m

Zm

m

mCUw

m

UC

m

r

m

A

m

ZL

This implies that

• City size will be a function of consumer amenities, fixed factors of production, reservation utility levels and so forth.

• Wages will also rise with productivity– this is being offset by lower amenity levels.

• These equations are then first differenced to provide estimating equations

)1)(1(

)1(~

)1)(1(

~~

)1)(1(

~)1(

)1)(1(

~~

m

mC

m

Amkw

m

C

m

AkL

L

L

To close the model

• Assume changes in A and changes in C are functions of initial characteristics and then regress changes in population (or employment) and income on initial conditions.

• Higher employment means either more productivity or better amenities

• Higher wages means either more productivity or worse amenities.

Testing the New Growth Theory (GKSS, JPE, 1992)

• Under what conditions are new ideas created?

• Marshall/Arrow/Romer– high concentration, big firms

• Jacobs– diversity, lots of little firms

• Michael Porter– high concentration– little firms

The Empirical Test

• Using city-industry (i.e. steel in Pittsburgh) growth between 1956 and 1987, GKSS look at what predicts growth:– Concentration of the industry (share in city

relative to share in U.S.)– Initial Employment in the City-Industry– Competition (or firm size relative to national

average)– Diversity in other industries

Results

• Average firm size (competition) always predicts more growth– what does it mean?

• There is substantial mean reversion

• Relative size is sometimes good/sometimes bad (no clear pattern)

• Diversity is good in our paper (not obvious how robust)

Subsequent Work: Climate and the Consumer City

• In 1900, cities had to locate in places where firms had a productive advantage.

• In 2000, cities increasingly locate in places with attractive amenities.

• The move to warm, dry places.

• The continued resilience of a few big consumer cities (NYC, Chicago, Boston, San Francisco).

Climate is the most reliable predictor of city growth

jan mean temp of major city, 199

Population Growth 1980-2000/MSA Fitted values

4.3 71.4

-.193765

1.96076

Best thought of as a regional effect

Figure 7: Temperature and State Growth 1920-1980Mean January Temp.

Population Change 20-80 .

0 20 40 60 80

0

1

2

3

alabama

alaskaarizona

arkansas

californ

colorado

connecti

delaware

florida

georgia

hawaii

idaho

illinoisindiana

iowakansas

kentucky

louisian

maine

maryland

massachu

michigan

minnesot

mississimissourimontana

nebraska

nevada

new hampnew jers

new mexi

new york

north ca

northdak

ohio

oklahoma

oregon

pennsylv

rhode is

south ca

south da

tennesse

texasutah

vermont

virginia

washingt

west vir

wisconsi

wyoming

Other correlations between pop. growth and consumer amenities:

• 35 percent correlation with temperature and 12 percent with dryness

• 24 percent correlation with proximity to ocean (Rappaport + Sachs)

• 14 percent correlation with theaters• In France, 45 percent correlation with

restaurants and 33 percent with hotel rooms• In UK, 31 percent correlation with tourist nights

Other facts

• Real wages used to decline with city size, now they rise (to be discussed later)

• Amenities (high housing prices relative to wages) strongly predict later population growth

• Housing price growth in central cities has boomed

• Reverse Commuting has increased

Urban Growth is Very Persistent

Population Growth in the Eightie

Population Growth in the Nineti Fitted values

-.232355 .889704

-.143171

.852277

The Rise of the Skilled City (JME, 1995, BWPUA, 2004)

• One fact that is regularly observed is the more skilled cities grow more quickly (Cityscape, 1994)

• Simon and Nardinelli show this going back to 1880.

• Are skilled cities more innovative?

• Is the productive value of being around skilled workers rising?

What does the rise of the skilled city mean?

• Or, perhaps are skilled cities become more attractive places to live?

• Test using wage changes, housing price changes and income changes

• The skill premium (i.e. the extra wages associated with being around skilled people) are rising quickly

• Housing prices rise almost enough to keep real wages constant

Skills and City Growthcoef = .00911216, se = .00214286, t = 4.25

e(

dp

op

| X

)

Population Growth and College Graduatese( bagrad90 | X )

-9.84 24.6555

-.438987

.86184

Steubenv

Houma, L

DanvilleGadsden,

Johnstow

Altoona,

Lima, OHMansfiel

Laredo,

Hickory-

Rocky Mo

McAllen-

Cumberla

Ocala, F

Visalia-

Fort Smi

BrownsviMerced,

Youngsto

Wheeling

WilliamsLewiston

Huntingt

Yuba Cit

Yuma, AZ

GoldsborTexarkan

Lakeland

Jackson,

Modesto,

Joplin,

StocktonBakersfi

Janesvil

Las Vega

Decatur,

Parkersb

Jacksonv

Punta Go

Wausau,

St. JoseKokomo, Sharon, Scranton

Yakima,

Redding,

Beaumont

SheboygaJohnson

Canton--

York, PASherman-

Pueblo,

Clarksvi

Owensbor

Victoria

AnnistonJamestow

Elkhart-

Florence

Dothan,

AlexandrPine Blu

Lake ChaLongview

Decatur,

Florence

EvansvilColumbus

Sumter,

Daytona

Dover, D

Reading,

Saginaw-

El Paso,

LafayettBiloxi--

Terre HaElmira,

Jackson,

Glens FaRockford

Panama CKilleen-

Mobile, Macon, G

Chattano

Utica--R

Sioux Ci

Myrtle B

Corpus C

Fort Pie

Fort WayLynchbur

Erie, PA

Wichita

Fresno,

Jonesbor

Fort Mye

Muncie,

Albany,

Waco, TXFayettevGreenvilLancaste

Shrevepo

Benton HDubuque,Duluth--

Eau Clai

CharlestPeoria--

St. ClouAppletonSan AngeSavannah

Louisvil

Fayettev

Tampa--S

WaterlooEnid, OK

Allentow

Davenpor

Grand Ju

Toledo,

Medford-Green Ba

Bangor,

Augusta-Grand Ra

Wilmingt

HarrisbuRoanoke,

Pensacol

Great FaLawton,

Ashevill

Detroit-

Odessa--

Jacksonv

Springfi

Memphis,

Pittsbur

Clevelan

AmarilloGreensbo

Miami--F

Buffalo-

Monroe,

Charlest

Dayton--

South Be

Sarasota

San Anto

New Orle

Knoxvill

Chico--P

Charlott

BirminghCincinna

Norfolk-Tyler, T

PocatellLa CrossTuscalooHattiesb

Binghamt

Indianap

Kalamazo

Tulsa, O

Casper,

Orlando,

Melbourn

Little R

ProvidenSt. Loui

Spokane,Cheyenne

Sioux Fa

Abilene,

Reno, NV

Milwauke

SyracuseSpringfi

Pittsfie

Fort Wal

Richland

Boise Ci

MontgomeRapid Ci

Grand Fo

Nashvill

Phoenix-

BillingsWichita,

Cedar Ra

Salinas,

Oklahoma

New LondSpringfi

Las Cruc

Bismarck

GreenvilBellingh

Los Ange

Philadel

West Pal

Eugene--

Topeka,

Naples,

Baton RoOmaha, N

Portland

Des Moin

Rocheste

Salt Lak

San Luis

Kansas CColumbus

Tucson, Sacramen

Chicago-Lubbock,

Albany--

Richmond

FlagstafHouston-

Lexingto

Honolulu

Lansing-

Albuquer

Fargo--MJackson,

San Dieg

Columbia

Dallas--

New York

Colorado

Atlanta,

Provo--O

Lafayett

Seattle-

Hartford

Santa Ba

Anchorag

MinneapoHuntsvil

PortlandLincoln,Missoula

Boston--

Barnstab

BloomingBurlingt

New Have

Rocheste

Denver--

Austin--

Athens,

San FranWashingt

Raleigh-

Fort Col

State Co

Tallahas

Blooming

Charlott

Champaig

Madison,

Gainesvi

Santa FeBryan--C

Columbia

Lawrence

Iowa Cit

Also predicts growing income

coef = .31334537, se = .0368799, t = 8.5

e(

pca

p8

9 |

X)

e( bagrad90 | X )-8.87 10.781

-5.3193

7.50575

YoungstoLakeland

Stockton

Bakersfi

Las Vega

Scranton

Johnson

Daytona

El Paso,

Mobile,

Chattano

Fort Way

Fresno,

Greenvil

Lancaste

Louisvil

Tampa--SAllentow

Toledo,

Augusta-

Grand Ra

Harrisbu

Detroit-

Jacksonv

Memphis,

Pittsbur

ClevelanGreensboMiami--F

Buffalo-

Charlest

Dayton--

Sarasota

San AntoNew Orle

Knoxvill

Charlott

Birmingh

Cincinna

Norfolk-

Indianap

KalamazoTulsa, O

Orlando,Melbourn

Little R

ProvidenSt. LouiMilwauke

SyracuseSpringfi

NashvillPhoenix-Wichita,

Oklahoma

Los AngePhiladel

West Pal

Baton Ro

Omaha, N

Portland

Rocheste

Salt Lak

Kansas CColumbus

Tucson,

Sacramen

Chicago-

Albany--

Richmond

Houston-

Lexingto

Honolulu

Lansing-

Albuquer

San Dieg

Columbia

Dallas--

New York

Atlanta,Seattle-

Hartford

Minneapo

Boston--

New Have

Denver--

Austin--

San FranWashingt

Raleigh-

Interpretation

• The natural interpretation of this is that skills are working through labor demand, not labor supply.

• But it is true that the skills effect still works within metro areas (which are common labor markets)

• One startling fact is that skills matter for older, colder places, not newer warmer cites: the reinvention hypothesis

Declining Regionscoef = .01169126, se = .00161765, t = 7.23

e(

dp

op

| X

)

e( bagrad90 | X )-10.4384 24.0572

-.310423

.311965

Steubenv

Johnstow

Altoona,

Lima, OH

Mansfiel

Youngsto

Williams

LewistonJackson,

Joplin,

Janesvil

Wausau,

St. Jose

Kokomo,

Sharon, Scranton

Sheboyga

Canton--

York, PA

Jamestow

Elkhart-

Decatur,

Evansvil

Reading,

Saginaw-Terre Ha

Elmira,

Glens FaRockford

Utica--R

Sioux Ci

Fort Way

Erie, PA

Muncie,

Lancaste

Benton HDubuque,

Duluth--

Eau Clai

Peoria--

St. ClouAppleton

Waterloo

Allentow

Davenpor

Toledo,

Green Ba

Bangor,

Grand Ra

Harrisbu

Detroit-

Springfi

Pittsbur

Clevelan

Buffalo-

Dayton--

South Be

CincinnaLa Cross

Binghamt

Indianap

Kalamazo

Providen

St. Loui

Sioux Fa

Milwauke

Syracuse

Springfi

Pittsfie

Rapid Ci

Grand Fo

Wichita,

Cedar Ra

New LondSpringfi

Bismarck

PhiladelTopeka,

Omaha, N

Des Moin

Rocheste

Kansas C

Columbus

Chicago-

Albany--Lansing-

Fargo--M

New York

Lafayett

Hartford

Minneapo

Portland

Lincoln,

Boston--

Barnstab

BloomingBurlingt

New Have

Rocheste

State CoBlooming

Champaig

Madison,Columbia

Lawrence

Iowa Cit

Growing Region (the West)coef = -.00320705, se = .00492356, t = -.65

e(

dp

op

| X

)

e( bagrad90 | X )-9.24169 14.6463

-.460293

.70212

Visalia-Merced,

Yuba Cit

Yuma, AZ

Modesto,StocktonBakersfi

Las Vega

Yakima,

Redding,

Pueblo,

Fresno,

Grand JuMedford-

Great Fa

Chico--P

Pocatell

Casper,

Spokane,Cheyenne

Reno, NV

Richland

Boise Ci

Phoenix-

Billings

Salinas,

Las Cruc

Bellingh

Los Ange

Eugene--

PortlandSalt Lak

San LuisTucson, Sacramen

Flagstaf

Honolulu

AlbuquerSan Dieg

ColoradoProvo--O

Seattle-

Santa Ba

Anchorag

Missoula

Denver--

San Fran

Fort Col

Santa Fe

The Reinvention Hypothesis

• An alternative interpretation– skills matter in times of shock (Schultz, Welch).

• Skilled cities excel because they permit innovation.

• As such, the key to reinvention is to keep skilled people from leaving.

Boston’s Growth is one of Reinvention

• In 1630, Winthrop comes to Boston for consumption, not production reasons.

• City on the Hill-- a religious community.

• All other colonies are about production.

• Original export industry is some fishing and selling goods to new immigrants.

American’s first city

• Boston is founded in 1630 with 150 settlers.

• Location is determined by the Charles river and clean water.

• Population rises to 7,000 in 1690.

• Population is 17,000 is 1740 when the city is overtaken by Philadelphia.

The 1640 Crisis and Its Resolution

• In the early 1640s, the flow of immigrants subsides.– English revolution

• Bostonians respond by reinvention, not exit.

• Respond by selling basic foodstuffs and wood, but now to other colonies.

The Colonial Model for Boston

• New England exported to other colonies– 73 percent to the Southern Colonies and

Caribbean (1770)– 13 percent to England

• Goods were basic commodities– 35 percent is fish (to West Indies 1770)– 32 percent livestock– 21 percent woodstock

Basic Model

• Land in Virginia and Haiti is worth more growing tobacco and sugar

• The North has little it can export to Europe, so its land is worth less and it grows commodies.

• North is poorer than South in the 1700s.

The 19th Century Reinvention

• But after 1790, Boston begins to grow again.– Growth from 18,000 in 1790 to 90,000 in 1840– Kept pace with national population growth.

• It is maritime, not manufacturing.– 10,000 in maritime trades– 5,000 in manufacturing (less than Lowell)

Boston as a Share of the U.S.B

osto

n P

opul

atio

n/U

.S. P

opu

latio

Figure 2: Boston's Share of Total U.S. PopulationYear

1800 1850 1900 1950 2000

.002

.004

.006

.008

1790 1800 18101820

1830

1840

18501860

1870

18801890

1900 19101920

1930

1940

1950

1960

1970

19801990

2000

What Happened?

• Boston’s port is still inferior to NYC.– Between 1821 and 1841, Boston’s share of trade

drops from 21 percent to 10 percent.

• But Bostonians increasingly own and man the ships.– Boston’s share of registered tonnage rises from 45 to

58 percent between 1811 and 1851.

• “Yankees captures New York Port around 1820 and dominated its activity until the Civil War” (Albion, 1931).

What Happened, Continued

• Boston’s comparative advantage was in human capital– both at the high end (merchants) and in sailers.

• Over the 1790-1840 period, technology and politics increased globalization of trade. – China trade and South Africa– Whaling far from New England– Clipper Ships

• The human capital became more important than the port location.

Live by the Clipper Ship, Die By …

• In the 1840s, steam ships start becoming more important than sail.

• Boston’s human capital becomes far less valuable.

• Boston’s loses it maritime dominance, never to regain it.

But Reinvention Once Again

• Over the 1840-1920 period, Boston would continue to boom.

• Manufacturing replaced maritime.

• Improvements in engine technology helped the city in two ways– Freed Manufacturing form river power– Created Rail Networks

And then there’s the Irish

• Boston starts becoming Irish in the 1840s.

• The Potato famine coincides with last era of Boston maritime dominance.

• As a result, its cheaper for the Irish to go from Liverpool to Boston than to NYC– this will not be true for later migrants.

The Twentieth Century

• Manufacturing left cities

• Car cities replaced higher density areas

• People fled cold places

• The rich fled redistributive cities.

In the 1970s, Boston was in bad shape

• Population had been declining for decades

• The economy was in shambles

• Housing cost less than new construction in most of the area.

But since 1980, the city has surged

• Population has grown modestly

• The economy has grown robustly

• Housing prices have soared.

Economic Growth since 1980

• In 1980, per capita income is the Boston Metro Area was $7547 which meant it ranked 61st in the nation.

• In 1994, personal income was 26,093 tenth in the nation.

• In 1996, average annual pay was 34,383, sixth in the nation.

Middlesex County Employment

• Professional, Scientific and Technical Services– 110,000 jobs or 13 percent

• Educational Services– 64,000 jobs or 7 percent

• Administrative and Support Services– 64,00 jobs or 7 percent

• Computer and Electronic Manufacturing– 58,000 jobs or 7 percent/

Urban Wages (JOLE, 2001)

• Wages are higher in big cities than in small towns

• This is a nominal wage difference, not a real wage difference

• There is no labor supply puzzle, but there is a labor demand puzzle. b

Nominal Wages and City Size(Slope=.073, R-Squared=.3)

lpop

ln(avg w eekly w age from IPUMS) Fitted values

10.9623 16.0717

6.28487

7.16802

Abilene,

Akron, O

Albany,

Albany-S

Albuquer

Alexandr

Allentow

Altoona,

Amarillo

Anchorag

Ann Arbo

Anniston

Appleton

Ashevill

Athens,

Atlanta,

Atlantic

Auburn-O

Augusta-

Austin-S

Bakersfi

Baltimor

Baton Ro

Beaumont

Bellingh

Benton H

BillingsBiloxi-G

Binghamt

Birmingh

Blooming

Blooming

Boise Ci

Bremerto

Brownsvi

Bryan-Co

Buffalo-

Canton-M

Cedar Ra

Champaig

Charlest

Charlott

CharlottChattano

Chicago,

Chico-Pa

Cincinna

Clarksvi

Clevelan

Colorado

Columbia

Columbia

Columbus

Columbus

Corpus C

Dallas,

Danville

Davenpor

Dayton-S

Daytona

Decatur,Decatur,

Denver,

Des Moin

Detroit,

Dothan, Dover, D

Duluth-S

Dutchess

Eau Clai

El Paso,

Erie, PAEugene-S

Evansvil

Fargo-MoFayettev

Fayettev

Flagstaf

Flint, MFlorence

Fort Col

Fort Lau

Fort MyeFort Pie

Fort Smi

Fort Wal

Fort Way

Fresno, Gadsden,

Gainesvi

Galvesto

Goldsbor

Grand Ju

Grand Ra

Greeley,

Green Ba

Greensbo

Greenvil

Greenvil

Hagersto

Hamilton

Harrisbu

Hickory-

HonoluluHouma, L

Houston,

Huntsvil

Indianap

Iowa Cit

Jackson, Jackson,Jackson,

Jacksonv

Jamestow

Janesvil

Johnson

JohnstowJoplin,

KalamazoKankakee

Kansas C

Kenosha,

Killeen-

Knoxvill

Kokomo,

La Cross

Lafayett

Lafayett

Lake Cha

Lakeland

Lancaste

Lansing-

Laredo, Las Cruc

Las Vega

Lexingto

Lima, OH Lincoln,

Little RLongview

Los AngeLouisvil

Lubbock,

Lynchbur

Macon, G

Madison,

Mansfiel

McAllen-

Medford-

Melbourn

Memphis,

Merced,

Miami, F

Milwauke

Minneapo

Mobile, Modesto,

Monmouth

Monroe, Montgome

Muncie,

Myrtle B

Nashvill

New Orle

New York

Newburgh

Norfolk-

Ocala, F

Odessa-M

Oklahoma

Olympia,Omaha, N

Orlando,

Pensacol

Peoria-P

Philadel

Phoenix-Pittsbur

Portland

Provo-Or

Pueblo,

Punta Go

Racine,

Raleigh-

Reading,

Redding,

Reno, NVRichland

Richmond

RiversidRoanoke,

Rocheste

RochesteRockford

Rocky Mo

Sacramen

Sag inaw-

St. Clou

St. Jose

St. Loui

Salem, O

Salinas,

Salt Lak

San Anto

San Dieg

San Fran

San Jose

San Luis

Santa Ba

Santa Fe

Santa Ro

Sarasota

Savannah

Scranton

Seattle-

Sharon,

Sheboyga

ShrevepoSioux CiSioux Fa

South Be

Spokane,

Springfi

Springfi

State Co

Stockton

Sumter,

SyracuseTacoma,

Tallahas

Tampa-St

Terre Ha

Toledo,

Topeka,

Trenton,

Tucson,

Tulsa, O

Tuscaloo

Tyler, T

Utica-Ro

Vineland

Visalia-

Waco, TX

Washing t

Waterloo

Wausau,

West Pal

Wichita,

Wichita

Williams

Wilmingt

Wilmingt

Yakima,

Yolo, CA

York, PA

Youngsto

Yuba CitYuma, AZ

Real Wages and City Size 1970coef = .04216037, se = .00929214, t = 4.54

e( lo

wre

al |

X)

e( lpop | X )-1.54732 1.79423

-.127177

.104913

Lancaste

Des Moin

Baton Ro

Wichita,

Orlando,

Nashvill

Dayton-S

Denver,

Indianap

Buffalo-San Dieg

Kansas C

Milwauke

Cincinna

San Fran

Atlanta,

Houston,

Minneapo

Baltimor

St. Loui

Pittsbur

Washingt

Philadel Los Ange

Chicago,

New York

Real Wages and City Size Todaycoef = -.06963045, se = .04110073, t = -1.69

e( lo

wre

al |

X)

e( lpop | X )-1.51985 1.4879

-.624834

.168197

Lancaste

Wichita,

Baton Ro

Dayton-S

Buffalo-

Raleigh-

Nashvill

MilwaukeIndianapCincinna

Orlando,

San Fran

Kansas C

Denver,

Pittsbur

Baltimor

St. Loui

San Dieg

Minneapo

Atlanta,

Houston,

Washingt

Philadel

Chicago,

New York

Los Ange

Is it Selection?

• Wage Premium for metropolitan area residence .2-.35 depending on source

• What about the real wage facts today

• Controlling for standard omitted factors (education, industry, occupation) makes little difference

• Controlling for AFQT in the NLSY makes no difference

More on selection

• Parent’s location when used as an instrument predicts higher wages today.

• But individual fixed effects regressions do generally eliminate much of the city effect

• .28 to .05 in PSID

• .24 to .1 in NLSY

• What’s going on here?

The Learning Hypothesis

• If cities increase human capital only slowly, then this can explain the individual fixed effect results without selection

• Urban dummy is small for young workers (under 10 percent)

• But rises more than 15 percent over time

• Also true in fixed effect regressions– a 15 percent increase over time

Analysis of Movers

• Ashenfelter dip before leaving or moving to cities

• 7 percent gain or so within a few years

• Increasing wage gains over time

• The NLSY results show somewhat quicker wage growth

• People who leave cities don’t face wage losses.

Learning in Cities (Journal of Urban Economics, 1999)

• To understand the previous section, a brief model with two skill levels (the paper does a more general distribution).

• Your probability of becoming skilled involves (1) meeting a skilled person in your industry and (2) imitating that person (with prob. C)

• If the share of skilled people in an area is “s,” then the probability of becoming skilled from each interaction is cs/I.

More on learning

• The key assumption is that the number of meetings is a function of city size or density, or D(N) where N is population.

• The probability of becoming skilled in a period equals 1-(1-cs/I)D(N)

• If city rent+transports=aN/2, and unskilled wages=w and the gain from being skilled is V then

Closing the learning model

• Spatial equilibrium requires

(1-(1-cs/I)D(N) )V-aN/2=(1-cs/I)D(N’) V-aN’/2

The gains from extra learning are offset by higher rents.

If there are just two locations– one with no learning and the other, a city then

Comparative Statics

• City size rises with returns to learning, discount factor and falls with A.

• The skill level of the city will itself also be a function of the learning parameters.

• With multiple skill levels, the skill distribution is uniform.

Information Technology and the Future of Cities (Journal of Urban

Economics, 1998)• So cities exist in part to speed information flows

• Doesn’t that mean that information technology will kill cities?

• Not so fast– the key question is whether face-to-face interactions and electronic interactions are complements or substitutes

A Simple Model

• Step 1– learn reservation value (denoted j with cumulative distribution R(j) and choose whether or not to collaborate

• Step 2– learn match quality a, which means match returns are af(i) where “i” is intensity

• Step 3– produce intensity using elecronic media (phones) or face-to-face

Phones vs. Face-to-Face

• Two technologies differ in their fixed costs and in their power

• iP=BPT and if=Bf(T-T), where Bf>Bp

• Phones don’t have fixed costs, but they are worse at creating intimacy.

• Use phones whenever desired “i” is low.

Solving the model

• There are two cutoff values for “a”– The lower values determines a level of a at

which is makes sense to end the relationship– A higher values above which people use face

to face interactions

• Better electronic technologies are increases in BP, which impacts several margins

Improvements in Technology

• First it decreases the cutoff of “a” at which you interact at all.

• Second, it increases the cutoff at which you switch from phones to face-to-face.

• Third, it lowers the cutoff for the initial participation decision.

What does this mean?

• First, improvements in technology may actually increase the amount of face-to-face contact, by increasing the number of people who work together.

• Second, if cities are a technology for lowering the fixed costs of face-to-face, then demand for cities will rise if improvements in technology raise face-to-face contact.

• Third, the key condition for this to hold is that people in cities use phone technology more.

What does the data say?

• Fact # 1: Phones and cities go together across countries, and over time.

• Fact # 2: Business travel has risen over the past 20 years (face to face)

• Fact # 3: Co-authorship and other forms of interaction are rising steadily.

• Fact # 4: High tech industries are particularly likely to urbanized

More on the data

• Fact # 5: Silicon Valley is clustered

• Fact # 6: People in cities often use electronic forms of interaction more, not less.

• Overall– there is no compelling case that cities and technology are complements, but none that they are substitutes either.