Urban Economics in the U.S. and India
Ed Glaeser(joint work with J-P Chauvin and Kristina Tobio)
Harvard University
The Knowledge Mismatch
• The most important things in cities are happening in Asia and the most important things in Asia are happening in cities.
• The most significant economic, environmental and social battles of the 21st century may relate to urbanization in India and China.
• But much of our knowledge about cities comes from the developed world. We don’t know the extent to which our wisdom translates.
Key Urban Insights
• The Spatial Equilibrium is our organizing concept.• Agglomeration economies– productivity rises with density
and area size. • Human capital matters both in levels (wages and
productivity) and for growth (population).• There are significant diseconomies of density: disease,
congestion, crime. • People increasing move to areas that are pleasant as well as
productive (rise of the consumer city).• Housing policy, especially constraints on supply, matters for
quality of life and city growth.
Why might these facts not hold in the developing world?
• Spatial equilibrium’s implications breaks down if mobility is too difficult– Indeed, the US may be exceptional in this regard.
• Agglomeration economies may be even more important if cities are the conduits across continents and civilizations.
• Human capital externalities might be less important if skills matter more in the developed world or more if skills are needed to transfer knowledge from the developed world.
The Spatial Equilibrium
• The central tool of the urban economist is the idea of a spatial equilibrium – there is at least someone on the margin across space.
• This equilibrium condition implies that U(Income, Amenities, Prices)
is constant over space. High income is offset by high prices or low amenities.
A large US/Europe literature (Rosen-Roback) confirms various implications of this approach.
Why Might Spatial Equilibrium Results Fail to Carry Over to India?
• The theory’s just wrong because of immobility.• The theory’s right and works for income, but
people are too poor to care about amenities.• Housing supply is essentially elastic in slums. • Measurement of rent is a disaster because of
omitted housing and area attributes and rent contracts.
• Individual human capital differs widely and is hard to observe.
United States
Lived in a
different home 5 years ago
Lived in different county(3) but same state 5
years ago
Lived in different state five years
ago Born in a
different state
Total Population 46% 19% 9% 40%25-34 72% 31% 14% 48%35-54 42% 18% 7% 48%55-over 25% 10% 5% 49% India
Live in current locality for 5 years or less
Lived in different locality but same state 5 years or
more before
Lived in different state five years or more before
Lived in a different
location at some point in
their lives
Total Population 3.0% 2.6% 0.4% 3.6%25-34 4.2% 3.5% 0.7% 4.1%35-54 2.9% 2.6% 0.3% 3.9%55-over 1.5% 1.3% 0.2% 2.8%
-2-1
01
2Av
erag
e log
rent
resid
uals
-1 -.5 0 .5 1Average wage residual in district
NOTE: Using data from the Indian Human Development Survey (2005) and the General Census (2001) Size of the circle denotes district density
District-level observations for India restricted to districts in population quartile 4Average rent and wage residuals in India
Conclusions on Spatial Equilibrium
• There is some rent connection with wages, but little with amenities.
• Incomes differ wildly across space and are strongly associated with satisfaction.
• I think this suggests that (1) unobserved human capital gaps are enormous, (2) probably there is great taste demand for home locales, which limits mobility.
• Both of this are compatible with the spatial equilibrium, but not in the simple way it is used typically in the U.S.
The Power of Agglomeration
• A key issue in the urban role in economic recovery is the extent to which urbanization increase productivity.
• Cities are the absence of physical space between people and firms.
• They thrive by eliminating transport costs for goods, people and ideas.
• But to what extent is the link selection or reverse causality?
-.05
0.0
5.1
.15
Ave
rage
Po
pula
tion
Cha
nge
, 200
0-20
10
3000
035
000
4000
045
000
5000
0A
vera
ge M
edia
n In
com
e, 2
000
0 2 4 6 8 1010 quantiles of popdens2000
Average Median Income, 2000 Average Population Change
Table 4: City size
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Dependent variable
Log wage
Log wage
Log wage
Log house price
Log house price
Log house price
Log real wage
Log real wage
Log real wage
Regression type OLS
IV population
IV geography OLS
IV population
IV geography OLS IV population IV geography
Log population, 2000
0.04 0.08 0.04 0.16 0.06 0.39 -0.024 0.025 -0.09
[0.01] [0.03] [0.02] [0.03] [0.06] [0.09] [0.019] [0.054] [0.03]
N1,591,1
401,521,5
991,590,4
672,343,0
542,220,2
492,333,00
21,591,14
01,521,59
91,590,46
7
R2 0.22 0.40 0.20
Note: Individual-level data are from the Census Public Use Microdata Sample, as described in the Data Appendix. Real wage is controlled for with median house value, also from the Census as described in the Data Appendix. Individual controls include sex, age, and education. Population IV is from 1880. Geography IV includes latitude and longitude, January and July temperatures and precipitation.
Urban Only(1) (2) (3) (4) (5) (6) (7) (8) (9)
VARIABLES Earnings Earnings Earnings Earnings Earnings Earnings Earnings Earnings Earnings
Log of District Density 0.121*** 0.118*** 0.117*** 0.0873*** -0.0156 0.117*** 0.119*** -0.0144 -0.0149(0.0217) (0.0209) (0.0222) (0.0180) (0.0790) (0.0205) (0.0202) (0.0780) (0.0785)
Average minimun temperature in district -0.00381 -0.00476(0.00358) (0.00358)
Average maximum temperature in district -0.00348* -0.00407**(0.00192) (0.00167)
Average rainfall in district 0.000599 -2.48e-05(0.000735) (0.000597)
Average schooling in district 0.0703*** -0.0237 -0.0257 -0.0264(0.0147) (0.0661) (0.0658) (0.0660)
Int. Avg. Schooling in District-Log of Density 0.0153 0.0155 0.0157(0.0108) (0.0107) (0.0108)
Recent Migrant Dummy 0.160*** 0.253** 0.114 0.111(0.0245) (0.124) (0.118) (0.120)
Int. Migrant-Log of Density -0.0144 -0.0167 -0.0144(0.0181) (0.0163) (0.0167)
Int. Migrant-Schooling 0.0158*** 0.0147***(0.00464) (0.00460)
Geographic (state) Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual Age Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual Education Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual "Social Group" Controls No Yes Yes Yes Yes Yes Yes Yes Yes
Constant 9.051*** 9.240*** 9.556*** 8.955*** 9.580*** 9.226*** 9.216*** 9.574*** 10.11***(0.132) (0.127) (0.317) (0.116) (0.461) (0.126) (0.125) (0.456) (0.525)
Observations 10,605 10,605 10,395 10,605 10,605 10,605 10,605 10,605 10,395R-squared 0.348 0.356 0.355 0.362 0.362 0.358 0.358 0.366 0.365Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
"Social Groups" are defined as caste and/or religion
Agglomeration Economies in India: Earnings and district density
Note: Regression restricted to prime-age malesEarnings = LN of annual wage and salary earnings (in rupees)
City-Skill Complementarity
• Skilled People and Industries seem to select into larger cities/denser areas.
• This suggests a complementarity between cities and skills which is natural if cities enable the spread of ideas.
• This complementarity also shows up in the cross-effect on wages.
• And it shows up in steeper urban age earnings profiles– and the migrants data.
24
68
10Av
erag
e ye
ars o
f sch
oolin
g in
Distr
ict
4 6 8 10Log of District Density
NOTE: Using data from the Indian Human Development Survey (2005) and the General Census (2001)
District-level observations for IndiaAverage schooling and Density
Human Capital Externalities
• The impact of area skills on earnings is associated with Rauch (1993) and Moretti (2003).
• Survives numerous controls and identification strategies (e.g. historic land grant colleges).
• Might work through learning (mysteries of the trade) or entrepreneurship and innovation
• Typical US number is 7 percent higher wages with 10 percent more college graduates
Figure 42000 Share of Skilled Workers
Log Wage Residual 2000 Fitted values
.05 .1 .15
9.6
9.8
10
10.2
Albany-S
AlbuquerAllentow
Atlanta-
Austin-R
Bakersfi
Baton RoBirmingh
Buffalo-Canton-M
Charlest
Charlott
Chicago-
Columbia
Columbus
Dayton,
Fort Way
Fresno,
Grand Ra
GreensboHarrisbu
Honolulu
Indianap
Jackson,
Kansas C
Knoxvill
Lancaste
Las Vega
Little R
Louisvil
Memphis,
Minneapo
Nashvill
New Orle
New York
Oklahoma
Omaha-Co
Orlando,
Phoenix-
Pittsbur
Richmond
Rocheste
Salt Lak
San Anto
San Dieg
San Fran
Spokane,
St. Loui
Stockton
SyracuseTampa-St
Toledo,
Tucson,
Tulsa, O
West Pal
Wichita,
Youngsto
-1.5
-1-.5
0.5
1Av
erag
e wa
ge re
sidua
l in d
istric
t, ur
ban
only
0 .05 .1 .15People with BA degree in district
NOTE: Using data from the Indian Human Development Survey (2005) and the General Census (2001)
District-level observations for IndiaWage residuals and Population with a BA degree, urban only
0.2
.4.6
.8Di
strict
Lev
el Gi
ni Co
effici
ent
4 6 8 10Log of District Density
NOTE: Using data from the Indian Human Development Survey (2005) and the General Census (2001)
District-level observations for IndiaGini Coefficient and Population Density
The Growth of Cities in the U.S.
• The Skills-Growth Connection– Reflects productivity increases in skilled areas
• The Rise of the Consumer City– Much of this reflects the rise of warmth
• The Connection between Small Firms/Start-up Employment and growth– Instrument with mines
0.0
5.1
.15
Ave
rage
Po
pula
tion
Gro
wth
by
Cou
nty,
200
0-20
10
1 2 3 4 5
Average Population Growth by Share with BA in 2000(Quintiles)
0.0
2.0
4.0
6.0
8.1
Ave
rage
Po
pula
tion
Gro
wth
by
Cou
nty,
200
0-20
10
1 2 3 4 5
Average Population Growth by Average January Temperature(Quintiles)
(1) (2) (3) (4) (5)
VARIABLES 1961-1971 1971-1981 1981-1991 1991-2001 1961-2001
Number of Universities -0.00504 0.0361* 0.0797*** 0.0161 0.131***(0.0195) (0.0191) (0.0201) (0.0150) (0.0327)
Number of Engineering Colleges 0.0697*** 0.0397*** 0.0559*** 0.0219*** 0.138***(0.00822) (0.00848) (0.00910) (0.00680) (0.0138)
Maximum Average Temperature (F) 0.00167 0.000674 -0.00215 -0.00126 -0.00248(0.00158) (0.00154) (0.00160) (0.00118) (0.00264)
Log of Rainfall (Inches) 0.00639 -0.0341 0.00193 0.0117 -0.0286(0.0286) (0.0285) (0.0299) (0.0216) (0.0479)
Log Pop 1961 -0.134*** -0.558***(0.0173) (0.0290)
Log Pop 1971 -0.170***(0.0179)
Log Pop 1981 -0.288***(0.0195)
Log Pop 1991 -0.0956***(0.0174)
Constant 1.577*** 2.330*** 3.872*** 1.525*** 7.694***(0.263) (0.270) (0.297) (0.252) (0.440)
Observations 392 393 401 415 392R-squared 0.210 0.197 0.367 0.078 0.513Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Sample is cities with population of 100,000 or moreNumber of universities and engineering colleges are continuous variables.Data from the 2001 Census of India
Log Population Change
Prices and Permits across Larger Metropolitan Areas
AkronAlbany AlbuquerqueAllentown
Ann Arbor
AtlantaAustin
BakersfieldBaltimore
Baton RougeBirminghamBoston
Buffalo
CharlestonCharlotte
Chicago
CincinnatiCleveland
ClevelandColorado Springs
ColumbiaColumbus
DallasDayton
Denver
Detroit
El PasoFort Wayne
Fresno
Grand RapidsGreensboroGreenvilleHarrisburg
Hartford CT
Honolulu
HoustonIndianapolisJacksonvilleKansas City
Knoxville
Las Vegas
Little Rock
Los Angeles
Louisville
McAllen
Memphis
Miami
Milwaukee
Minneapolis
Mobile
Nashville
New Haven
New Orleans
New York
Oklahoma CityOmaha
OrlandoPhiladelphia Phoenix
Pittsburgh
Portland
Providence
RaleighRichmond
Riverside
Rochester
Sacramento
Salt Lake City
San Antonio
San Diego
San FranciscoSan Jose
Sarasota
Scranton
Seattle
SpringfieldSt. Louis
Stockton
Syracuse
TampaToledo
Tucson
Tulsa
Vallejo
Washington
Wichita
Worcester
Youngstown Little Rock
020
0000
4000
0060
0000
8000
00M
edia
n H
ousi
ng V
alue
, 200
5
0 .1 .2 .3Permits 2000-5/Stock in 2000
Source: U.S. Census Bureau
Figure 13:Median Housing Values in 2005
and Permits 2000-2005 Across MSAs
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