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Prof Ed Glaeser- "Urban Economics in the US and India"

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Professor Ed Glaeser of Harvard University during World Bank's Special Session during Economic Geography Conference in Seoul, Korea_June 29, 2011

Text of Prof Ed Glaeser- "Urban Economics in the US and India"

Urban Economics in the U.S. and IndiaEd 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 different county(3) but same state 5 years ago

Lived in a different home 5 years ago

Lived in different state five years ago

Born in a different state

Total Population 25-34 35-54 55-over

46% 72% 42% 25%

19% 31% 18% 10%

9% 14% 7% 5%

40% 48% 48% 49%

India 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 3.6% 4.1% 3.9% 2.8%

Live in current locality for 5 years or less

Total Population 25-34 35-54 55-over

3.0% 4.2% 2.9% 1.5%

2.6% 3.5% 2.6% 1.3%

0.4% 0.7% 0.3% 0.2%

Average rent and wage residuals in IndiaDistrict-level observations for India restricted to districts in population quartile 42 -2 -1 Average log rent residuals -1 0 1

-.5

0 Average wage residual in district

.5

1

NOTE: Using data from the Indian Human Development Survey (2005) and the General Census (2001) Size of the circle denotes district density

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?

30000 0 Average Median Income, 2000 Average Population Change 2 4 6 10 quantiles of popdens2000 8 10 -.05

Average Median Income, 2000 35000 40000 45000

50000

0 .05 .1 .15 Average Population Change, 2000-2010

Table 4: City size (1) (2) (3) (4) (5) Log house price Log house price (6) (7) (8) (9)

Dependent variable

Log wage

Log wage

Log Log house wage price

Log real Log real Log real wage wage wage

Regression type Log population, 2000 N R2

OLS 0.04 [0.01]

IV population

IV geography

OLS

IV population

IV geography

OLS IV population IV geography

0.08 [0.03]

0.04 [0.02]

0.16 [0.03]

0.06 [0.06]

0.39 [0.09]

-0.024 [0.019]

0.025 [0.054]

-0.09 [0.03]

1,591,1 1,521,5 1,590,4 2,343,0 2,220,2 2,333,00 1,591,14 1,521,59 1,590,46 40 99 67 54 49 2 0 9 7 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.

Agglomeration Economies in India: Earnings and district density Urban OnlyVARIABLES Log of District Density Average minimun temperature in district Average maximum temperature in district Average rainfall in district Average schooling in district Int. Avg. Schooling in District-Log of Density Recent Migrant Dummy Int. Migrant-Log of Density Int. Migrant-Schooling (1) (2) (3) (4) (5) (6) (7) (8) (9) Earnings Earnings Earnings Earnings Earnings Earnings Earnings Earnings Earnings 0.121*** (0.0217) 0.118*** (0.0209) 0.117*** 0.0873*** -0.0156 (0.0222) (0.0180) (0.0790) -0.00381 (0.00358) -0.00348* (0.00192) 0.000599 (0.000735) 0.0703*** -0.0237 (0.0147) (0.0661) 0.0153 (0.0108) 0.117*** (0.0205) -0.0149 (0.0785) -0.00476 (0.00358) -0.00407** (0.00167) -2.48e-05 (0.000597) -0.0257 -0.0264 (0.0658) (0.0660) 0.0155 0.0157 (0.0107) (0.0108) 0.253** 0.114 0.111 (0.124) (0.118) (0.120) -0.0144 -0.0167 -0.0144 (0.0181) (0.0163) (0.0167) 0.0158*** 0.0147*** (0.00464) (0.00460) Yes Yes Yes Yes 9.216*** (0.125) 10,605 0.358 Yes Yes Yes Yes 9.574*** (0.456) 10,605 0.366 Yes Yes Yes Yes 10.11*** (0.525) 10,395 0.365 0.119*** (0.0202) -0.0144 (0.0780)

0.160*** (0.0245)

Geographic (state) Controls Individual Age Controls Individual Education Controls Individual "Social Group" Controls Constant

Yes Yes Yes No 9.051*** (0.132)

Yes Yes Yes Yes 9.240*** (0.127)

Yes Yes Yes Yes 9.556*** (0.317) 10,395 0.355

Yes Yes Yes Yes 8.955*** (0.116) 10,605 0.362

Yes Yes Yes Yes 9.580*** (0.461) 10,605 0.362

Yes Yes Yes Yes 9.226*** (0.126) 10,605 0.358

Observations 10,605 10,605 R-squared 0.348 0.356 Robust standard errors in parentheses *** p

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