CS计算机代考程序代写 assembly Präsentationsvorlage

Präsentationsvorlage

FIRM LOCATION CHOICE

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

the idea

I COURSE COMPONENTS

▪ Block I

▪ Introduction to Urban and Regional Economics and Course Overview

▪ Topic I: Regional and urban concentration forces

▪ Topic II: The empirics of agglomeration

▪ Topic III: Costs and benefits of agglomeration

▪ Block 2

▪ Topic IV: Monocentric city I (household location choice)

▪ Topic V: Monocentric city II (household location choice)

▪ Topic VI: Firm location choice

▪ Topic VII: The urban economy in general equilibrium

▪ Block 3

▪ Topic VIII: The vertical dimension of cities

▪ Topic IX: Suburbanization and gentrification

▪ Topic X: Hedonic analysis

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

roadmap

I INTRODUCTION

▪ Last time: The monocentric city model II

▪ 1) Equilibrium conditions

▪ Additional restrictions

▪ 2) Comparative statics: Income

▪ What happens if people get richer?

▪ Where do the rich and the poor live in cities?

▪ 3) Comparative statics: Transport cost

▪ What happens if transport gets cheaper?

▪ Did cities decentralize over time?

▪ 4) Other predictions

▪ Distinguishing between open-city and closed-city case

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

roadmap

I INTRODUCTION

▪ This time: Firm location choice

▪ 1) Firms in the monocentric city model

▪ Firm bid-rent

▪ Land-use segregation

▪ 2) Agglomeration and decentralization

▪ Endogenous agglomeration

▪ Multiple equilibria

▪ 3) Emergence of new clusters

▪ Sub-centres

▪ Edge cities

▪ Historic anchoring

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

LSE © Gabriel M Ahlfeldt5

firms in the MCM

II THE CBD IN THE MCM

Q: Why do firms concentrate in the CBD?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

roadmap

II INTRODUCTION

▪ This time: Firm location choice

▪ 1) Firms in the monocentric city model

▪ Firm bid-rent

▪ Land-use segregation

▪ 2) Agglomeration and decentralization

▪ Endogenous agglomeration

▪ Multiple equilibria

▪ 3) Emergence of new clusters

▪ Sub-centres

▪ Historic anchoring

▪ Edge cities

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

firms in the MCM

II FIRM BID-RENT FUNCTION

▪ To rationalize firm concentration in the CBD in the MCM, we need a
force that attracts firms to the CBD

▪ High cost of distance, so that firms outbid residents

▪ Some fundamental location factor

▪ Firms need to be close to a natural harbour (trading cities)

▪ Firms sell goods at a market place (von Thünen)

▪ An agglomeration effect declining in distance from the CBD

▪ Being located centrally in a labour pool (MAR, (topic I)

▪ Knowledge spillovers (MAR, topic I)

Transport cost for goods decreased over time (topic I): Plausible in the past

Knowledge spillovers very localized (topic II): More plausible today

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

firms in the MCM

II FIRM PROFITS

▪ Use the Ahlfeldt & Wendland (2013), henceforth AW, framework
to illustrate the firm bid rent

▪ Identical services firms at location i maximize profits 𝜋𝑖

▪ Firms occupy land directly: No developers

▪ Capital broadly defined, includes workers, machines, computers,
and building structure

▪ Could introduce developers providing space in perfect analogy
to household bid-rent model

𝜋𝑖 = 𝜒𝑖 − 𝐾𝑖 − 𝜓𝑖𝐿𝑖

Output = revenue, since

price of good set to 1

Cost of land

L, Ψ is the

land rentCost of capital K, price

of capital set to 1

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

firms in the MCM

II PRODUCTION FUNCTION

▪ The production technogloy is Cobb-Douglas

▪ Similar results as long as (diminishing MPs)

▪ Productivity spillovers 𝑨𝒊 decline in distance from CBD

𝜒𝑖 = 𝐴𝑖𝐾𝑖
𝛼𝐿𝑖

1−𝛼 0<α<1 is capital share at inputs (constant in Cobb-Douglas) 𝐴𝑖 = 𝑎𝑖𝑒 −𝜃𝐷𝑖 Fundamental amenity, let’s assume it is “flat” Distance from the CBD Θ>0 Determines

the decay of the

spillover

Exponential function 𝑒−𝜃𝐷𝑖

returns one for D=0 and

approaches zero for D=∞

Productivity shifter

𝝌′ > 𝟎, 𝝌′′ < 𝟎 I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary 10 firms in the MCM II PROFIT MAXIMIZATION ▪ Firms choose inputs to maximize profits ▪ First-order conditions give ▪ Recall Micro 101 𝜋𝑖 = 𝐴𝑖𝐾𝑖 𝛼𝐿𝑖 1−𝛼 − 𝐾𝑖 − 𝜓𝑖𝐿𝑖 Combined profit and production function 𝐾𝑖 𝐿𝑖 = 𝛼 1 − 𝛼 𝜓𝑖 Capital density increases in land rent Similar to developers‘s problem, topic IV 𝜕𝜋𝑖 𝜕𝐾𝑖 𝜕𝜋𝑖 𝜕𝐿𝑖 = 1 − 𝛼 𝛼 𝐾𝑖 𝐿𝑖 = 𝜓𝑖 𝑝𝑘 =1 Check a micro textbook if unclear I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary 11 firms in the MCM II SPATIAL EQUILIBRIUM ▪ In spatial equilibrium profits are equalized ▪ Zero profits due to perfect competion, free entry and exit ▪ Plug in first-order condition: 𝜋𝑖 = 𝐴𝑖𝐾𝑖 𝛼𝐿𝑖 1−𝛼 − 𝐾𝑖 − 𝜓𝑖𝐿𝑖 = 0 𝜓𝑖 = 𝛼 𝛼𝐴 𝑖 1 1−𝛼 = 𝑐𝑒 − 𝜃 1−𝛼 𝐷𝑖 Corresponds to fixed utility in household bid-rent model ln𝜓𝑖 = 𝑐 − 𝜃 1 − 𝛼 𝐷𝑖 𝐾𝑖 = 𝛼 1−𝛼 𝜓𝑖 𝐿𝑖 𝜕𝜓𝑖 𝜕𝐷𝑖 < 0 Firm bid-rent decreases in distance from the CBD Higher office rents compensate for greater productivity near CBDc summarizes constants I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary 12 firms in the MCM II LAND PRICE GRADIENTS BY USE x=distance from CBD Bid rent for land Residential land price gradient (topics IV & V) Commercial land price gradient with large Θ Manufacturing land price gradient with small Θ Q: Land use pattern? I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary 13 firms in the MCM II LAND PRICE GRADIENTS BY USE x=distance from CBD Bid rent for land ManufacturingResidentialCBD URBAN RURAL Residential land price gradient (topics IV & V) Commercial land price gradient with large Θ Manufacturing land price gradient with small Θ Land goes to the highest bidder Land price gradient is the envelope of price gradients by use => convexity due to sorting

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

II PRIME LOCATION GRADIENTS IN 125 CITIES

Ahlfeldt et al. (2018)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

II PRIME LOCATION GRADIENTS IN 125 CITIES

Ahfleldt et al. (2020)

Largest prime location in city: Identified as

concentration of office buildings held by REITS &

Starbucks franchises:

Note: SNL Data AVAILABLE to REEF students

Commercial used

dominates

Office rents decline!

Structural density declines !

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

LSE © Gabriel M Ahlfeldt16

agglomeration and decentralization

III EMPLYOMENT CONCENTRATION

Q: Can the spatial structure of a city be changed?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

roadmap

III AGGLOMERATION AND DECENTRALIZATION

▪ This time: Firm location choice

▪ 1) Firms in the monocentric city model

▪ Firm bid-rent

▪ Land-use segregation

▪ 2) Agglomeration and decentralization

▪ Endogenous agglomeration

▪ Multiple equilibria

▪ 3) Emergence of new clusters

▪ Sub-centres

▪ Historic anchoring

▪ Edge cities

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III CBDs DOMINATED BY PRIME SERVICES

Knowledge-basedtradable

services highly

concentrated in CBDs

Manufacturing

employment much more

decentralized

Fundamentals not

obviously relevant for

prime services

Ahlfeldt et al. (2020)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III TRADABLE SERVICES

▪ An agglomeration economy emerging from the CBD is a
simplfying assumption

▪ More realistically firms benefit from each other and, therefore,
cluster in the CBD

▪ To capture the idea AW introduce a bidirectional spillover

CBD is endogenous!

𝐴𝑖 = 𝑎𝑖Λ𝑖Γ𝑖

Λ𝑖 = 𝑒
−𝜃𝐷𝑖

Γ𝑖 = 𝑒
𝛽𝑍𝑖; 𝑍𝑖 = σ𝑗 𝜒𝑗𝑒

−𝜏𝑑𝑖𝑗

Productivity shifter

Exogenous „CBD spillover“

Endogenous bidirectional

agglomeration force. Depends

on nearby output, weighted by

distance
Agglomeration elasticity

(strength of the effect)

Agglomeration

decay

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III BID-RENT WITH BILATERAL AGGLOMERATION

▪ Commercial bid rent

𝐴𝑖 = 𝑎𝑖Λ𝑖Γ𝑖

Λ𝑖 = 𝑒
−𝜃𝐷𝑖

Γ𝑖 = 𝑒
𝛽𝑍𝑖; 𝑍𝑖 = σ𝑗 𝜒𝑗𝑒

−𝜏𝑑𝑖𝑗

Commercial bid rent decline in distance from CBD

𝜓𝑖 = 𝛼
𝛼𝐴

𝑖

1−𝛼

Commercial bid rent inreases in local economic density

If bilateral agglomeration economies are strong,

commercial land prices will be highly spatially correlated!

𝜒𝑗 =
1

1−𝛼
𝜓𝑗𝐿𝑗

Zero-profit condition and

first-order condition at location j give

Market potential measure

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III BID-RENT WITH BILATERAL AGGLOMERATION

▪ Commercial bid rent

𝐴𝑖 = 𝑎𝑖Λ𝑖Γ𝑖

Λ𝑖 = 𝑒
−𝜃𝐷𝑖

Γ𝑖 = 𝑒
𝛽𝑍𝑖; 𝑍𝑖 = σ𝑗 𝜒𝑗𝑒

−𝜏𝑑𝑖𝑗

𝜓𝑖 = 𝛼
𝛼𝐴

𝑖

1−𝛼 𝜒𝑗 =
1

1−𝛼
𝜓𝑗𝐿𝑗

Market potential measure

ln𝜓𝑖 = c +
𝛽

1−𝛼 2
σ𝑗𝜓𝑗𝐿𝑗𝑒

−𝜏𝑑𝑖𝑗 −
𝜃

1−𝛼
𝐷𝑖

Distance-weighted land value

Q: Changes over time?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III CBD VS. BILATERAL SPILLOVERS IN BERLIN I

▪ AW analyse a commercial land values in 1890-1936 Berlin

▪ Rapid urban growth: Population tripled from 1.5M to 4.5M

▪ Transition from “historic” to “modern” CBD / economy

Transition resembles many cities in

developing world today
Bilateral agglomeration should be

increasingly important

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III CBD VS. BILATERAL SPILLOVERS IN BERLIN I

▪ AW provide a multivariate estimation of strength of CBD
spillover and bidirectional agglomeration forces over time

𝜓𝑖 = 𝑐𝑒
−

𝜃

1−𝛼
𝐷𝑖

ln𝜓𝑖 = ln𝑐 −
𝜃

1−𝛼
ln𝐷𝑖

Exogenous „CBD spillover“

becomes less important

Transition towards “knowledge” economy

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III CBD VS. BILATERAL SPILLOVERS IN BERLIN II

▪ AW provide a multivariate estimation of strength of CBD
spillover and bidirectional agglomeration forces over time

Exogenous „CBD spillover“

becomes less important

1890 1936Transition to “knowledge” economy

Agglomeration elasticity 𝛽
increases from 3.5% to 8.3%

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III IMPLICATION FOR URBAN STRUCTURE I

With strong CBD spillover

New cluster emerges in

periphery (e.g. start-ups)

Or shock relocates existing

firms (e.g. disaster)

Exogenous CBD spillover

pulls firms into the CBD

City structure remains stable

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III IMPLICATION FOR URBAN STRUCTURE II

With bidirectional

agglomeration force

New cluster emerges in

periphery (e.g. start-ups)

Or shock relocates existing

firms (e.g. disaster)

New cluster generates own

agglomeration economies

City structure can change

=> Multiple equilibria!

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

agglomeration and decentralization

III CAUSES OF EMPLOYMENT DECENTRALIZATION

▪ Manufacturing should decentralize given change in transport,
production, and inventory technologies

▪ Tradable services could decentralize since bidirectional
agglomeration forces imply multiple equilibria

▪ Driving forces for employment decentralization

▪ Change in transport technology

▪ Suburbanization population (jobs follow people)

▪ Fiscal and social problems (e.g. US)

▪ Ageing/redundant building stock

▪ Regulation (e.g. height constraints)

▪ …
Q: Is there evidence?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

III GLAESER & KAHN (2001)

▪ US cities are mostly decentralized (NY is an exception)

▪ Share of employment within three miles 29% or less

▪ Employment share of central MSA county falling since 1950

▪ Hard to predict decentralization, but

▪ Manufacturing particularly decentralized

▪ Suburbanization associated with decentralized employment

▪ Cities specialized in services are relatively centralized

▪ Knowledge-based industries more likely in city centre

Evidence supports theoretical expectations

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

III BAUM-SNOW ET AL (2017)

▪ Transport infrastructure in 1990-2010 China decentralizes
population and employment

▪ Radial highways decentralize services activity

▪ Radial railroad decentralize industrial activity

▪ 20% of industrial activity

▪ Ring roads decentralize both

▪ 50% of industrial activity

▪ Radial / ring roads displace 4% / 20% of central city
population

Evidence supports theoretical expectations

Identification strategy similar to

Baum-Snow (2006), see topic V

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

roadmap

IV AGGLOMERATION AND DECENTRALIZATION

▪ This time: Firm location choice

▪ 1) Firms in the monocentric city model

▪ Firm bid-rent

▪ Land-use segregation

▪ 2) Agglomeration and decentralization

▪ Endogenous agglomeration

▪ Multiple equilibria

▪ 3) Emergence of new clusters

▪ Sub-centres

▪ Historic anchoring

▪ Edge cities

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV DECENTRALIZED VS. DISPEARSED EMPLOYMENT

▪ Employment decentralized in many cities

▪ Not necessarily the same as the dispearsed employment

Decentralised and dispearsed Decentralised and clustered

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

LSE © Gabriel M Ahlfeldt32

emergence of new clusters

IV SUBCENTERS

Q: Why, when, and where do cities develop

commercial clusters outside the CBD?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV MONOCENTRIC VS POLYCENTRIC CITIES

▪ (Dis)advantage of large monocentric cities

▪ Large agglomeration economies in CBD

▪ Long commuting cost and higher wages as compensation

▪ High land prices

▪ Large polycentric cities

▪ Have one or multiple subcentres

▪ Subcentres are clusters of employment that resemble the CBD

▪ Offer some agglomeration benefits

▪ Reduce commuting cost and land prices

Polycentric cities combine benefits of large and small cities!

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV LARGE MONOCENTRIC CITY

x=distance from CBD

Bid rent

for land

CBDx=distance from CBD

Residential land price gradient

Commercial land price gradient

with large Θ

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV LARGE POLYCENTRIC CITY

x=distance from CBD

Bid rent

for land

CBDx=distance from CBD

Residential land price gradient

Commercial land price gradient

with large Θ

SubcentreSubcentre

In AW model, every location j in

the cits is treated as a subcentre

𝜓𝑖 = 𝑓 ෍
𝑗
𝜒𝑗𝑒

−𝜏𝑑𝑖𝑗

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV WHEN DO CITIES DEVELOP SUBCENTRES?

▪ Fujita & Ogawa (1982) model: Number of subcentres increases

▪ in population and commuting cost

▪ Subcentres emerge endogenously to keep the city efficient

▪ McMillen & Smith (2003) test the prediction for US cities

▪ Identify subcentres as peaks in local employment densities

▪ McMillen (2001) locally weighted regression approach

▪ Run Poisson regressions (count models)

▪ Anayse determinants of number of subcentres per city

Q: How do city size, congestion levels, and other factors affect the

number of subcentres?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV DETERMINANTS OF SUBCENTRE FORMATION

Low-congestion city develop

1st / 2nd subcenter at a

population of 2.68M / 6.74M

High congestion cities develop

subcentres earlier

Older housing stock also make

subcentre formation more

likely, but effect smaller

Evidence supports theory!McMillen & Smith (2003)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV WHERE DO SUBCENTERS EMERGE?

▪ Subcentres tend to be emerge:

▪ McMillen & Smith (2003)

▪ close to highway intersections

▪ In old satalite suburbs

▪ Garcia-López et al (2017)

▪ close to regional express rail

▪ Ahlfeldt & Wendland (2013)

▪ close to the CBD if it is
historically strong

Evidence from the US

Evidence from Paris

Evidence from Berlin

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

IV EMPLOYMENT DENSITY IN NYC

Q: Why did some cities (not) decentralize?

Ahlfeldt et al (2020)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

IV LA VS. NYC NYC
Ahlfeldt et al (2020)

LA has many

more remote PLs

than NYC,

despite similar

population

Role for history?

NYC 1900 pop: 3.4M

LA 1900 pop: 100k

NYC built a much

larger transit network

much earlier (1868)

Does the evidence

generalize?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

IV EVIDENCE FROM 125 GLOBAL CITIES
Ahlfeldt et al (2020)

Historic density and subways achhor CBDs Disasters promote transition

Caloric potential

Subway potential

Role of agglomeration- and transport-induced persistence

shown in simulations within granular spatial model

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV EDGE CITIES

Q: Where would a profit-maximizing developer

strategically develop a satellite city?

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV EDGE CITIES

▪ Term coined by Washington Post journalist and author Joel Garreau
in his 1991 book Edge City: Life on the New Frontier.

▪ New cities in urban periphery built by one developer

▪ Built to host knowledge based tradable services

▪ Dominated by class A office stock (shiny offices)

▪ Net commuting destination (unlike a residential suburb)

▪ Proper city with employment, recreation, entertainment

▪ Popping up since 1965: A “new” urban lifestyle (?)

▪ 123 edge cities and 83 up-and-coming places listed by Garreau

▪ 24 in greater LA, 23 in metro D.C., and 21 in greater NY

Edge city developed by a single developer (unlike a subcentre)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV WHY EDGE CITIES?

▪ Developer aims at creating an attractive edge city

▪ internalizing agglomeration spillovers

▪ avoiding social-fiscal problems (poverty, crime…)

▪ reducing capacity constraints (congestions).

▪ Typical „instruments“

▪ Mixed use space to minimize commuting

▪ Optimal road layout and parking capacity (no sunk cost)

▪ Non-distorionary taxes (little redistribution)

Developer solves a “coordination problem” inherent to agglomeration

Value of land is low before and high after development ⇒ Profits!

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV TYSON’S CORNER

„Archetypal “ Edge City

Developed by Til Hazel (Hazel & Peterson)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV TYSON’S CORNER

Largest retail area on the area on the east coast

south of NY, 3,400 hotel rooms, over 100,000 jobs,

over 28 million square feet of A-level office space

(relative to 17 million in Washington core city)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV IRVINE

Developed by Irvine Co. (Hazel & Peterson)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV IRVINE

Largest Edge City, over 160,000 jobs, over 33

million square feet of A-level office space

(relative to 25 million in LA core city)

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

▪ Edge city developed by one profit-maximizing developer

▪ Returns from producing a tradabe services good

▪ Faces cost of providing space (constant captial cost pk)

▪ Faces cost of hiring labour: Wages

▪ increase in residential rent (workers require compensaiton)

▪ increase in number of workers in the metro (congestion)

▪ Developers chooses

▪ capacity K1 (floor space)

▪ workforce B

▪ location y defined in terms of distance from the port city (CBD)

emergence of new clusters

IV HENDERSON AND MITRA (1996)

Developer has monopsony power

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

Edge city spillover

Port city spillover

emergence of new clusters

IV HENDERSON AND MITRA (1996)

▪ Port city and edge city generate agglomeration economies

▪ Depend on population (A for port city, B for edge city)

▪ Spillover to each other, declining in distance

Historic city (port)

Distance y

Edge city

A
g
g
lo

m
e

ra
ti
o
n

e
c
o
n
o
m

Agglomeration effect

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV HENDERSON AND MITRA (1996)

▪ Edge city developed by one profit-maximizing developer

▪ Must choose the location of the edge city

Historic city (port) Distance y

Edge city

Productivity increases

(CBD spillover)

Developer faces a trade-off when choosing distance from the CBD

But CBD also benefits

(increases competition for workers)

Land cost increases (MCM), higher

compensations to workers

Result: Small changes in CBD capacity K0 can trigger large effects on y

Location choice of developer can appear „chaotic“

I Introduction II Firm bid rent III Decentralization IV Subcenters V Edge cities VI Summary

emergence of new clusters

IV HENDERSON AND MITRA (1996)

▪ Edge city developed by one profit-maximizing developer

▪ Must choose capacity K1 and employment B of edge city

▪ Higher CBD capacity K0 reduces monopsony power

▪ CBD pays higher wages (greater agglomeration effect)

Developer needs to consider K0 when choosing edge city capacity

Result: Smaller edge city (K1 and B) with larger port city, but

K1 and B may even increase if distance y changes in response to K0

Model highly sensitive to parameter values („everything goes“)

„Chaotic“ patterns resemble Garreau‘s notion of „randomness

(but „randomness“ and „chaos“ are not the same!)

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