Cities in Bad Shape: Urban Geometry in India

Cities in Bad Shape: Urban Geometry in India Mariaflavia Harari presented by Federico Curci September 15, 2015 Harari Cities in Bad Shape September 15, 2015 1 / 19

Research question Estimate effect of city shape on several city-year level outcome and welfare in India Idea: bad city shape lower commuting efficiency effect on productivity and welfare Harari Cities in Bad Shape September 15, 2015 2 / 19

Research question, cntd Bad city shape especially problematic for fast urban growing developing countries " Leapfrog" development: fast population growth expand in chaotic and unplanned fashion less compact cities Lack of transport infrastructure Majority of population cannot afford individual means of transportation Harari Cities in Bad Shape September 15, 2015 3 / 19

Contributions Question Assess causal impact spatial layout of cities Harari Cities in Bad Shape September 15, 2015 4 / 19

Contributions Question Assess causal impact spatial layout of cities Empirical work grounded on theoretical model Evaluate welfare impact of city shape using a spatial equilibrium framework à la Roback-Rosen Identification Instrument geometric indicator of city shape using potential city shape due to geographical obstacles Data Use night-time satellite imagery to map urban expansion over time Panel dataset of 450 Indian cities for 1950 and from 1992 to 2010 Harari Cities in Bad Shape September 15, 2015 4 / 19

Contributions Question Assess causal impact spatial layout of cities Empirical work grounded on theoretical model Evaluate welfare impact of city shape using a spatial equilibrium framework à la Roback-Rosen Identification Instrument geometric indicator of city shape using potential city shape due to geographical obstacles Data Use night-time satellite imagery to map urban expansion over time Panel dataset of 450 Indian cities for 1950 and from 1992 to 2010 Result + compact cities + population, - wages and + housing rents Harari Cities in Bad Shape September 15, 2015 4 / 19

Contributions Question Assess causal impact spatial layout of cities Empirical work grounded on theoretical model Evaluate welfare impact of city shape using a spatial equilibrium framework à la Roback-Rosen Identification Instrument geometric indicator of city shape using potential city shape due to geographical obstacles Data Use night-time satellite imagery to map urban expansion over time Panel dataset of 450 Indian cities for 1950 and from 1992 to 2010 Result + compact cities + population, - wages and + housing rents Policy Adverse effect of topography are exacerbated by building height restrictions and mitigated by road infrastructure Harari Cities in Bad Shape September 15, 2015 4 / 19

Theoretical framework Rosen-Roback model Problem agent max C,H s.t. θc 1 α H α C = w p H H Spatial indifference: log ( W ) αlog ( p H ) + log (θ) + κ = log ( v) Harari Cities in Bad Shape September 15, 2015 5 / 19

Theoretical framework Rosen-Roback model Problem agent max C,H s.t. θc 1 α H α C = w p H H Spatial indifference: log ( W ) αlog ( p H ) + log (θ) + κ = log ( v) Firm in production sector max N,K AN β K γ Z 1 β γ wn K Labour demand condition Harari Cities in Bad Shape September 15, 2015 5 / 19

Theoretical framework Rosen-Roback model Problem agent max C,H s.t. θc 1 α H α C = w p H H Spatial indifference: log ( W ) αlog ( p H ) + log (θ) + κ = log ( v) Firm in production sector max N,K Firm in construction sector AN β K γ Z 1 β γ wn K Labour demand condition max H,L p H H C(H) s.t. H = h L C(H) = c 0 h δ p L L Housing price equation Harari Cities in Bad Shape September 15, 2015 5 / 19

Theoretical framework Rosen-Roback model Using firms labour demand, equality indirect utility in the town and reservation utility, and housing price equation: ln (N) = K N + ln (w) = K w + ln ( p H ) = KP + (δ+α αδ)ln(a)+(1 γ)(δln(θ)+α(δ 1)ln( L)) δ(1 β γ)+αβ(δ 1) (δ 1)αln(A) (1 β γ)(δln(θ)+α(δ 1)ln( L)) δ(1 β γ)+αβ(δ 1) (δ 1)(ln(A)+βln(θ) (1 β γ)ln( L)) δ(1 β γ)+αβ(δ 1) Harari Cities in Bad Shape September 15, 2015 6 / 19

Theoretical framework Rosen-Roback model Theoretical predictions Harari Cities in Bad Shape September 15, 2015 7 / 19

Theoretical framework Rosen-Roback model Theoretical predictions If shape is consumption disamenity but not affect productivity Harari Cities in Bad Shape September 15, 2015 7 / 19

Theoretical framework Rosen-Roback model Theoretical predictions If shape is consumption disamenity but not affect productivity If shape is consumption and production disamenity Harari Cities in Bad Shape September 15, 2015 7 / 19

Theoretical framework Rosen-Roback model To see welfare effect of exogeneous variable S on A and θ Assume log (A) = K A + λ A S + µ A log (θ) = K θ + λ θ S + µ θ Harari Cities in Bad Shape September 15, 2015 8 / 19

Theoretical framework Rosen-Roback model To see welfare effect of exogeneous variable S on A and θ Assume log (A) = K A + λ A S + µ A log (θ) = K θ + λ θ S + µ θ Regress population, wages and prices on S log (N) = B N S + D N log ( L ) + K N log (w) = B W S + D W log ( L ) + K W log ( p H ) = BP S + D P log ( L ) + K P Harari Cities in Bad Shape September 15, 2015 8 / 19

Theoretical framework Rosen-Roback model To see welfare effect of exogeneous variable S on A and θ Assume log (A) = K A + λ A S + µ A log (θ) = K θ + λ θ S + µ θ Regress population, wages and prices on S log (N) = B N S + D N log ( L ) + K N log (w) = B W S + D W log ( L ) + K W log ( p H ) = BP S + D P log ( L ) + K P Retrieve ˆλ A = ( 1 β γ ) ˆB N + ( 1 γ ) ˆB W ˆλ θ = α ˆB P ˆB W Harari Cities in Bad Shape September 15, 2015 8 / 19

Empirical model Model Challenges How to measure shape? log ( Y c,t ) = αsc,t + µ c + ρ t + η c,t Endogeneity shape: shape determined by interaction local geographic conditions, city growth, and policy Harari Cities in Bad Shape September 15, 2015 9 / 19

Measurement shape Urban footprint Use Night-time lights dataset Consider spatially contiguous lighted areas surrounding a city coordinate that have luminosity above a particular threshold Harari Cities in Bad Shape September 15, 2015 10 / 19

Measurement shape Shape metric indicators Remoteness: average distance between all interior points and the centroid Proxy for average length of commutes to the urban center Spin: average of the squared distances between interior points and the centroid More weight to polygon s extremities Disconnection: average distance between all pairs of interior points Proxy for commutes within the city Range: maximum distance between two points on the shape perimeter Proxy for longest possible commute trip within the city Harari Cities in Bad Shape September 15, 2015 11 / 19

Measurement shape Shape metric indicators Remoteness: average distance between all interior points and the centroid Proxy for average length of commutes to the urban center Spin: average of the squared distances between interior points and the centroid More weight to polygon s extremities Disconnection: average distance between all pairs of interior points Proxy for commutes within the city Range: maximum distance between two points on the shape perimeter Proxy for longest possible commute trip within the city Normalized by Equivalent Area Circle Circle with an area equal to that of the polygon Separate effect city shape from city size Harari Cities in Bad Shape September 15, 2015 11 / 19

Identification Potential shape Combine geography with mechanical model of city expansion in time Harari Cities in Bad Shape September 15, 2015 12 / 19

Identification Potential shape Combine geography with mechanical model of city expansion in time Idea: cities are predicted to expand in circles of increasing sizes but geographical obstacles prevent expansion in some of the possible directions Potential footprint: largest contiguous patch of developable land, i.e. not occupied by water nor by steep terrain, within a given predicted radius around each city Predicted radius log ( ) area c,t = θc + γ t + ε c,t areac,t ˆ ˆr c,t = π Harari Cities in Bad Shape September 15, 2015 12 / 19

Identification Potential shape Combine geography with mechanical model of city expansion in time Idea: cities are predicted to expand in circles of increasing sizes but geographical obstacles prevent expansion in some of the possible directions Potential footprint: largest contiguous patch of developable land, i.e. not occupied by water nor by steep terrain, within a given predicted radius around each city Predicted radius log ( ) area c,t = θc + γ t + ε c,t areac,t ˆ ˆr c,t = π Isolate variation in urban geometry induced by geography, excluding the variation due to policy or other endogeneous choices Harari Cities in Bad Shape September 15, 2015 12 / 19

Identification Potential shape Harari Cities in Bad Shape September 15, 2015 13 / 19

Results Effect on population Consumer location choice is affected by city shape Harari Cities in Bad Shape September 15, 2015 14 / 19

Results Effect on population Less compact cities have lower population (density) Harari Cities in Bad Shape September 15, 2015 14 / 19

Results Effect on population Elasticity productivity to firms density of 0.04, no account for sorting Harari Cities in Bad Shape September 15, 2015 14 / 19

Results Effect on prices City shape is consumption amenity Harari Cities in Bad Shape September 15, 2015 15 / 19

Results Effect on prices Consumers are paying a premium to live in cities with better shape: lower wages... Harari Cities in Bad Shape September 15, 2015 15 / 19

Results Effect on prices... and higher housing rents Harari Cities in Bad Shape September 15, 2015 15 / 19

Results Welfare analysis Impact shape on amenities: ˆλ θ = 0.14 Increase in one-way commutes of 360 meters entails a welfare loss equivalent to a 0.05 log points decrease in income Impact shape on firmsâ productivity: ˆλ A = 0.003 One s.d. deterioriation in city shape leads to 0.0001 log points productivity loss City shape is not affecting firms productivity Harari Cities in Bad Shape September 15, 2015 16 / 19

Channel Road infrastructure If transit times is the main channel through which urban shape matters, then road infrastructure should mitigate the adverse effect of poor geometry Interact proxy of road infrastructure with city shape Number of motor vehicles Urban road density Infrastructure mitigates the negative effect of poor geometry Harari Cities in Bad Shape September 15, 2015 17 / 19

Heterogeneous effect Poverty and city shape Compact cities favorable to poor Reduce distances for people that cannot afford individual means of transportation Compact cities unfavorable to poor Housing more expansive Less compact cities have fewer slum dwellers and have houses of marginally better quality Harari Cities in Bad Shape September 15, 2015 18 / 19

Concluding remarks Use geographically-driven variation in city shape to investigate implication of intra-urban commute length Poor urban connectivity has sizeable welfare cost City compactness affects spatial equilibrium across cities Harari Cities in Bad Shape September 15, 2015 19 / 19