Transcript Chapter -7 cities and congestion:the economics of Zipf`s Law

Chapter -7
cities and congestion: the
economics of Zipf`s Law
Group three - members:
Galina
Juan
Vlastimil
Mitiku
Introduction
 The long run equilibrium by:
 Complete
agglomeration
 Even spread
 Depends on:
 Initial distribution of MLF
 A few structural parameters like: TC,
ε, δ
Objectives
 Analyzes the extension of the core model
 Show how the inclusion of congestion
changes the nature of LRE
 Apply the core model with congestion to
the ultimate empirical regularity of citysize distribution
Structure
• How congestion can be introduced in the core
model& how alerts the working of the model
• City-size distribution to measure Zipf`s Law
• The core model with congestion – use of
simulation to city size distribution in accordance
with the empirical facts of Zipf`s low
• Conclusion
The relevance of urbanization & congestion
• Urbanization is a highly relevant phenomenon
• As world bank development indicators: 46% lived in
urban area, out of this :
63.5% lived in small & medium sized cities(<1m),21.4%
lived in large cities(1-5m); 15.1% in mega cities(>5m )
• middle &high income countries around 75%
• Use to explain why in the reality the main spread force in
the core model of GE
• The major draw back of urban agglomeration to be in
congestion such as EP, heavy usage of roads,
communication channels& storage facilities
• Specific example –traffic congestion- indicates Î in urban
agglomeration went along with no. vehicles & per km
Table 7.1 Urban population as % of total population, 1998
Argentina
89
Australia
85
Belgium
97
Brazil
80
Canada
77
Chile
85
Cuba
75
Czech Republic
75
Denmark
85
France
75
Gabon
79
Germany
87
Israel
91
Japan
79
S-Korea
80
Kuwait
97
Lebanon
89
Libya
87
Netherlands
89
New Zealand
86
Norway
75
Oman
81
Russian Federation
77
Saudi Arabia
85
Spain
77
Sweden
83
United Arab Emirates
85
UK
89
USA
77
Uruguay
91
Venezuela
86
Table 7.2 Congestion: number of motor vehicles, selected countries
Vehicles per 1000 people
Vehicles per kilometer road
1980
1998
1980
1998
Belgium
349
485
28
33
Finland
288
448
18
30
France
402
530
27
35
Germany
399
522
51
69
Italy
334
591
65
108
Netherlands
343
421
--
57
Poland
86
273
10
28
Spain
239
467
120
54
UK
303
439
50
67
Congestion as an additional spreading force
• Urban agglomeration driven by positive external
economies give rise to external diseconomies of
scale
• EDS also arise from EP, or drawbacks of
crowdedness in general
• The direct consequence of congestion is straight
forward since it provides incentive for Firms
&MW
• How it affects the balance between
agglomeration & spread force
The modeling of congestion
• The production structure of the core model can
be easily adapted to introduce Congestion cost
• Congestion costs for each firm depends on the
over all size of location of production.
• The size of the cityr is measured by the total no.
of manufacturing firms, Nr in that city
• The case of negative location specific external
economies arising from congestion are relevant,
in which 0<Τ<1
Figure 7.1 Total and average labor costs with congestion*
4
3
2
1
0
0
1
2
3
4
5
output
total N = 100
average N = 100
total N = 400
average N = 400
total no cong.
average no cong.
* Parameter values:  = 1,  = 0.2;  = 0.1 for N = 100 and N = 400,  = 0 for "no cong."
.
• .Location
decision has an impact on
production function
• Income equation is not affected by
congestion parameters
• How ever, congestions results from
Wage rate & price index
To assess the relevance of
congestion
• Relay on simulation on two steps:
• 1St illustrate relevance of in the two cities modelallow as to apply core model in congestion with
empirical phenomenon of city –size distribution
• 2Nd introduce many cities & congestion racetrack
economy of the core model
Two location and congestion
• To determine the direction of MLF & stability LE
• Focus on the real wage of city 1 relative city2
• Plot the welfare achieved in two cities together
• *Re-4 No congestion cost for SE is stable when
high T, where as full agglomeration in either city
is stable for low T
• However, this is not satisfactory out come from
the empirical point of views ,
Five different stages –for possibility
of LE by using congestion
1. very high T, spreading is the only stable (&
welfare maximizing) equilibrium
2. As T decrease still stable& allowing partial
agglomeration rather than complete as SE
3. Complete agglomeration in either city is SE as T
continue to fall
4. As T becomes very small , their impact relative
to congestion is limited.
5.For very low T , spreading is again the only
stable
From this three conclusions
emerge
1st . the rage of possible LRE outcomes with
congestion is wider than with out congestion
2nd . The phenomenon of partial agglomeration
establishes the possibility of small and large
economic centers as a stable LE out come
3rd . The welfare implication of the GE model have
tendency to coincide with SLE
b. T = 1.7
a. T = 1.9
w 1/w 2
w 1/w 2
w elfare
c. T = 1.61
w elfare
1.02
1.035
1.08
1.00
1
0.98
1
0.96
0.965
0.94
0.93
0.92
0
0.5
1
0
0.5
1
w 1/w 2
w elfare
Many location &congestion
• Two city model in congestion allow as for
viability of small economic centers of
Manufacturing extend to many cities
 The results of such simulation with congestion:
* many cities still have manufacturing production
(MP) in the LE
* these cities vary in economic size from
empirical point of view
* the final distribution of MP is well structured
around two center of economic in cities 3&15
* the individual city economic size in LE largely
depends on the relative place in the initial
distribution of city sizes (cities20&23)
Figure 7.3 The racetrack economy with congestion ( = 5;  = 0.7;  = 0.1)
initial
a. T = 1.2
final
initial
b T = 1.3
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