4. Cities
• What is a city?
• City – a “densely” populated urban
area of “reasonably” large size
– How dense and minimum size up for
debate
– Frequent contact between different
economic activities, “functional areas”
– Dense population means agriculture will
not be a prominent activity
5. Cities
• Conditions for city to develop
1. Agricultural surplus
• Rural areas must produce enough food to
support rural and urban areas
2. Urban production
• City dwellers must produce something to
exchange with rural dwellers for food.
3. Transportation for exchange
• Transportation network must exist to facilitate
exchange of food urban products
11. Policy Implications
• Rural pop (globally) likely to peak in
next few years
• Urban will continue to grow
• Distribution of urbanization benefits
– 2016 election?
• Manage internal migration
– Slums…
• Sustainability
12. Drivers
• Growth being driven by:
– Increased agricultural surplus
– Increased productivity of urban workers
– Increased efficiency of exchange and
transportation
13. Axioms of Urban Economics
Five Axioms to Understand
Urban Dynamics and
Equilibrium
14. Axioms of Urban Economics
1. Prices adjust to achieve locational
equilibrium
– No reason to move, indifferent
– i.e. more expensive to live at Wrightsville
Beach
2. Self reinforcing effects generate
extreme outcomes
– Self reinforcing – a change in something
leading to additional changes in same
direction
– Auto sellers, artistic communities, etc...
17. Axioms of Urban Economics
3. Externalities Cause Inefficiency
– Buyers and sellers consider private benefits
and costs, not social costs
– Costs (benefits) falling on third party
inefficient outcomes
– Example:
• Cost – pollution, loss of greenspace, traffic...
• Benefit – beautification, education...
4. Production (firm level) is subject to
economies of scale
– Double inputs, production more than doubles
– Only design product once, spread cost across
large quantities Large firms!
18. Economies of Scale
• Two reasons for economies of scale
– Indivisible inputs
• Capital inputs are lumpy and do not scale well
– i.e. metal stamping machine, needed to make 1 or
1,000,000
– Cannot be scaled down for small operations
– Factor Specialization
• Factors of production (units of K or L) are used for
specific tasks
• “A jack of all trades is a master of none”
• Increase firm size divide production into smaller
tasks
19. Axioms of Urban Economics
5. Competition generates zero
economic profit
– No barriers to entry firms enter until
economic profit is zero
• Recall: zero economic profit means profit equals
opportunity cost
• i.e. suppose you have a $50k job, offered a
different $50k job
– Still make $50k but no “economic profit”
• Entrepreneurship – be careful not to “buy
yourself a job”
20. Axioms - Takeaways
• Cities tend toward a
“spatial” equilibrium
• “Economic Profit”
firm entry or land
price adjustments
• Increasing returns to
production, co-
location etc. density
and clustering
22. Thought exercise…
• With Amazon Prime (really the
internet), is there any reason to live
near stores anymore?
• Can trade over the internet…
23. Decentralized Shopping
• Richard Warren Sears (1863-
1914)
– Railroad station agent
– Box of watches from a jeweler
– Sold to station agents for
profit
– Ordered another batch
– Started selling via mail order
catalogs
– Moved to Chicago and added
other products...
26. Decentralized Markets
• Notice that “Sears” or warehouse needs
employees
– Employees live nearby
• Bid up land prices
– Residents economize on land density
increases
– City is forming…
• Not fully decentralized markets (yet)
– Transportation
– Touching products
• Produce, clothes…
27. Restrictive Assumptions
• 1. Households are equally
productive
– No need to trade
• 2. Constant Returns to
Exchange
– Fixed Exchange cost per item
– i.e. One trip = one item
• 3. Constant Returns to
Production
– Twice as many shirts means
twice as many employees and
looms
Too
Strong!!!
Will
Relax
36. Backyard Production Model
Assume:
Exchange is costly
1. Households
equally productive
2. Constant
returns to
exchange
3. Constant
returns to scale in
production
Makes sense to
trade!
Trade a lot trading
firms/towns
Produce more factories
and factory towns
37. Towns
• Historically:
– Trading cities formed first
• Think outposts, ports, trade route intersections
– Factory towns followed
• Assembly line
• Sewing Machine
– Early 19th century, 80% of clothing hand sewn
in home
– 1846, sewing machine patent
– Late 19th century, 90% of clothing made in
factories
38. System of Towns
• Each firm has a local monopoly on
market area
– Outside market area, opportunity for
another firm to enter
Even distribution of firms
• May be reasons for firms to cluster together
though…
– (More on this later)
49. Agglomeration Economies
Localization economies –
forces acting on firms in a
single industry (local to
industry)
Urbanization economies –
forces acting on firms
across industries (firms in
one industry attract those
of other industries)
Leads to development
of large diverse cities
Agglomeration
economies –
Location forces
acting on
multiple firms
• Localization
• Urbanization
55. Shared Inputs
• Intermediate goods – inputs
• i.e. shirt makers use buttons locate near
button factory
– Natural resources
– Transport costs lead “down stream”
firms to cluster
– Proximity – easy to visit suppliers
– Clustering/input demand allows for
variety (think colors and size of buttons)
56. Aside on Buttons
• Originally by hand
– Button hole added in 13th century
– Knights brought back from crusades…
• Today:
http://www.madehow.com/Volume-2/Button.html
58. Sharing a Labor Pool
$16
$4
12
Supply of
Labor
High D
Low D
$10
3
Supply
of Labor
High D
Low D
Isolated Firm Clustered Firm
Wage
($)
Wage
Wage
Number of Workers Number of Workers
21
Wage
($)
59. Sharing a Labor Pool
• Film industry – Hollywood vs. ILM
• Isolated firms:
– Only game in town
– Must keep labor on board or it moves
– idle time and fluctuating wages
• Clustered
– Wages fixed by market
– Firms hire and fire from market
• Boom – hire more workers
• Bust – let them move to another firm
60. Labor Matching
• Firms need specific skills
– Hire as well as possible and train
• Training costs = f(skills gap)
• Total labor cost = wage + training
• minimize training costs
• Localization version of matching
– Locate near similar firms to hire trained
workers
• Lower training costs higher wages
attracting more skilled workers
more firms…
61. Knowledge Spillovers
• Industry over time
– Skills created and passed down
• Parents’ path?
– Ideas
• Improved upon by others
• Mixed with other ideas new ideas
– Small firms tend to cluster
• Network of interfirm relationships
• Self-reinforcing effects cause extreme
outcomes (clusters)
63. Urbanization Economies
• Input Sharing
– Some inputs used across industries
– i.e. HQs share legal services
• Labor Pooling
– Some industries expand while others
contract
– “Small” firms that don’t affect price
64. Urbanization Economies
• Matching
– Increase in skill density reduces costs for
skills across industries
• i.e. programmers moving b/n industries
• Knowledge Spillovers
– Cross-pollination
65. Urbanization Economies
• Other benefits
– Critical mass for entertainment
– Employment opportunities for dual
income households
– Learning opportunities
• Critical mass for educational institutions
• Role models and experience sharing
– Social interaction!
68. Large-Small City Wage Gap
• Observations:
– There appears to be a wage gap between
large cities and other areas (~38% w/ exp)
• Some debate about size, etc.
• Potential explanations:
– Sorting: skilled workers sort to cities
• May be unobservable skills
– Matching Higher Productivity
– Knowledge Spillovers Faster Human
Capital Accumulation
70. Wage Gap Decomposition
(Martellini)
• Sorting – 12% of premium
• Matching – 11% of premium
• Peer Effects – learning from
your peers
• Surrounding human
capital matters!
• Starts small and grows
• ~15% after 20 years
• Flow of Ideas – the rate of
knowledge diffusion
• Knowledge
everywhere…
72. Diseconomies of Agglomeration
• Resident costs
– Sprawling city – commute times/transport
costs
• Hour a day = 200 hours/year = 5 work weeks
– Dense (vertical) city – higher rents
• ~30% of HH expenditures
• Pollution levels
– Less CO2 per person, but more people
• Increased wages (good and bad)
• Crime
73. Policy
• Growth Pole Model
– From 1960s
– Investment by large firm or community
attracts other investment
• Think major industrial plant attracting suppliers
74. Policy
• Growth Pole Model
– From 1960s
– Investment by large firm or community
attracts other investment
• Think major industrial plant attracting suppliers
• NYC investing in Subway extention
• Short-run
– bids up local resource prices (land and labor)
– May crowd out existing bus. or res. – “backwash effect”
– Longer run – spread effect should dominate
– Many communities trying to incent large
investment
• Efficacy?
75. Policy
• Incubators or “Nursery” cities
– Large number of diversified small to
medium firms
• Create demand for professional and business
services which can also support other start-ups
– Think professional development
– Large firms often have services in house
• Porter model
– “Competitiveness” of region
– Clusters of small firms
• Allows visibility of competitors innovation
• Allows transfers of knowledge
77. Outcomes
Characteristics Pure Agglomeration Industrial Complex
(Vert Int.)
Firm size Small Large
Relations Informal
Unstable
Formal
Stable
Membership Open Closed
Spatial Effect Rents Bid Up No effect
Agglomeration Type Urban Local
Industrial Mix Diverse Concentrated
78. Clustering
• Benefits > Costs Cluster
• Empirical support, in clusters:
– More productive
– More firms starting up
– Quicker employment growth
81. Few Big, Many Small
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
NumberofCities
Number of Cities With a Pop of At Least ____ Million
Metro and Micro Areas by Size
85. Migration and City Size
• Suppose six million people and three
options
– Six cities: 1 million each
– Three cities: 2 million each
– Two cities: 3 million each
• What is the Equilibrium?
– Is it always optimal?
88. Not too small
• Start with six cities, 1m each
– If too small (S):
• One person moves agglomeration in new city
• Self-reinforcing effect
• Migration continues (S to G)
– # cities declines
– Remaining reach optimal size
89. Maybe Too Large
• Three cites, 2m each
– Grass is always greener or some shock and some people
move
– 2 cities of 2.5m and one of 1m
– U(2.5m) > U(1m) Res1m move to City2.5m
– Leads to 2 cities, 3m each
– Too large relative to optimal
– One person moves because city is too large
• From one city to other
– New location now worse because larger now than before
• One more car clogging the roads and resident bidding up rent
• “You aren’t sitting in traffic you are traffic”
– Moves back...
90. Maybe Too Large
• Could someone move out on their own?
– If moved on own:
• No agglomeration economies
• No critical mass
• Utility likely falls
• Moves back
• Cities larger than optimal is a stable
equilibrium
– Could be different sizes, but each larger
than optimal is a stable eqm
92. Transport Costs
• Utility = Income – (transport costs + rent)
• In mountains, transport costs increase rapidly
Chattanooga, TN
93. City Size
• No reason all cities must be same size
• Differences in:
– Localization forces
• Some exhausted quicker than others
– i.e. input supplier may only attract some firms
– Urbanization forces
• Tend to encourage larger cities
– Distribution of resources is uneven
– Geography commute times
96. Mega Cities
• New York is large but only 6.3% of pop
• But:
– Seoul is 10m of SK’s 50m pop, 20%
– Tokyo is 33.2m of Japan’s 127m pop, 26%
– Sao Paulo is 21.1m of Brazil’s 200m, 10.5%
97. Mega Cities
• Why such large cities?
– Economies of scale in trade
• Infrastructure
• Ports
• Airport
– Capital Cities
• Public investment centered on capital
• Especially true for dictators
– Appease those closest to avoid coup
100. Cities
• Why?
– Location for markets
• Factors of production – workers close to work
• Goods and services – intermediate inputs, etc.
– With Specialization
• Increasing returns to exchange
• Increasing returns to production
– Agglomeration Economies
• Localization economies (industry specific)
• Urbanization economies (across industries)
101. Cities
• Where?
– Trading towns – near intersections of
transportation or trade routes
– Factory towns
• Near resources
• Near customers/market area
Editor's Notes
Industries share hotels, restaurants, maintenance, insurance, etc.
Cross pollination – economists with psychologists started behavioral economics...
Industries share hotels, restaurants, maintenance, insurance, etc.
Cross pollination – economists with psychologists started behavioral economics...
