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Economic Geography as seen from Economics:
Neglect, (Re)Discovery & (Missed) Opportunities
Harry Garretsen
(Spatial Economic Analysis (SEA) Lecture, RSA 2012 Delft)
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SEA journal: “… methods of
spatial economics”
Central theme RSA 2012 conference: spatial interactions
Why me?
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Spatial interactions or dependencies……..
› ……..central to the field of economic geography (EG)..
› …....aims to explain (uneven) spatial development…..
› ………various analytical approaches………
› This lecture: what does economics have to offer to the
analysis of spatial interactions and hence to EG?
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OUTLINE
1.
2.
3.
4.
5.
6.
7.
Central theme: EG & spatial interactions….
…….. in economics prior to 1991
…….. 1991: New Economic Geography (NEG)
Krugman’s NEG: his 3 key ”Nobel” insights
20 years on: missed opportunities??
Example: Urbanization in China
How to proceed with (N)EG?
The Litmus Test of EG
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Does it matter where Delft
is located in The Netherlands?
YES:
space & location matter
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Spatial interactions in economics pre-1991
› International economics?
› Regional economics?
› Urban Economics?
› CONCLUSION: Neglect or, at best, partial analysis
Krugman:
Nobelprize 2008
Something changed between 1991 and 2008!!!
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New Economic Geography/Geographical Economics
› Three main insights make for core NEG model
› NEG what’s in a name?
NEG vs geographical economics
› This model sets the scene for remainder lecture
NEG 3 key insights (I)
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› NEG’s core model: Krugman (1991, JPE)
› NEG originates in international trade theory, not in
urban/regional economics
› We proceed in 3 steps: Krugman (1979, 1980, 1991)
› International trade theory in 1979: old (=18th
century) theory (Ricardo) at odds with facts
› Theory: inter-industry trade; facts: intra-industry
trade (it’s not “cloth for wine” anymore)
Manufacturing intra-industry trade; 1988-2000, selected countries
Manufacturing intra-industry trade (% of total manufacturing); 1988-2000
Mexico
% intra-industry trade
75
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Germany
USA
Hungary
South Korea
50
Japan
Australia
25
1991
1994
1997
year
2000
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NEG 3 Key Insights (II)
› Krugman (1979): introduce internal increasing
returns to scale
li    xi
› Model of imperfect competition (Dixit and Stiglitz,
1977)
› Rationale for (intra-industry) trade, but no role for
geography yet……..
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Average
costs
Average costs under increasing returns to scale
Output
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NEG 3 Key Insights (III)
› Krugman 1980: add transport costs to IRS
› Assume two countries, a and b: Sa >Sb (market size S
for A larger than B);
› And assume transport costs T>0;
if α >(T x Sb), then locate firm in larger market
› “Home market effect”: geography matters
› But: why should Sa>Sb to begin with????
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NEG 3Key Insights (IV)
› Krugman (1991): 1st NEG model:
add factor (=labour) mobility to T and IRS
› Also external IRS (pecuniary or market size
externality)
› Big Q: where will footloose firms&workers locate?
› Answer: it depends……………
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NEG 3 Key Insights (V)
› ……..it depends on relative strength of agglomeration
and spreading forces
› Agglomeration forces: home market effect, price
index effect
› Spreading forces: competition effect
› “Tug of War”: key model parameters, notably level of
transp. costs, T
[Where’s the novelty of Krugman 1991?]
Panel a
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S1
1
1
B
0.5
0
1
S0
Transport costs T
Sustain points
Stable equilibria
Break point
Unstable equilibria
Basin of attraction for spreading equilibrium
Basin of attraction for agglomeration in region 1
Basin of attraction for agglomeration in region 2
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So basically, α+T+λ give us……….
………….a very happy economist on October 13th 2008
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NEG after Krugman (1991)
› Reception of NEG in- and outside economics
› NEG after 1991:
Theory?
Extensions of core 1991 model?
Empirics? Real test of underlying model?
Policy Relevance? General vs specific policies?
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Reception…….
› Economics: initial wave of research; NEG has done
its job, no longer separate sub-field of research?
› Outside economics: Not new, bad economics and
real lack of geography
› Krugman (2011): Middle-aged NEG does not look too
well??
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What happened? (I)
› THEORY: focus on mix of agglomeration and
spreading forces, but too little progress? (n-region
problem?, simulations?)
› EMPIRICS: outburst of NEG inspired empirical
research, but where’s the real test of NEG?
1, 2
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1.0
1
1 = 2
1 = 2
0.5
2
1.0
1.2
1.4
T
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What happened? (II)
› Empirical research: focus on short run instead of
long run version of NEG!!!
› “Krugman (1980) beats Krugman (1991)”
› Market potential/access: not relevant on regional
level??

1  1 
Wr   YsTsr I s 
 s 1

R
1/ 
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What happened (III)?
›
POLICY RELEVANCE? (see THEORY+EMPIRICS):
1.
General conclusion: policy in a lumpy world, role of
threshold effects (Baldwin et al, 2003)
2.
Specific policy conclusions rather difficult or based
on “wrong” version of NEG model (main example:
World Bank, World Development Report 2009)
a. Effective average tax rate, 19 OECD countries
0.6
0.15
maximum; Germany (left scale)
effective average tax rate
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standard deviation
(right scale)
0.4
0.10
average
(left scale)
Japan
0.2
0.05
minimum; Ireland (left scale)
0.0
1980
0.00
1985
1990
year
1995
2000
The Wiggle Diagram
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rN/rS
(iii) standard tax competition
(ii) geo ec. high trade costs
D
B
tN-tS
A
0
C
(i) geo ec. intermediate trade costs
share of firms in North
1
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Lessons (Not) Learned
› Lack of theoretical progress & wrong focus in
empirical research: main message of NEG got lost!!
› So what? [assuming(!) mainstream economics can
add to understanding of spatial interactions]
› Example: Urbanization in China (to show
comparative advantage of NEG)
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Two background papers for our example
›
Bosker M, S Brakman, H. Garretsen & M Schramm:
1. “Adding Geography to the New Economic
Geography”, Journal of Economic Geography, 10(6),
pp. 793-823, 2010.
2. “The New Economic Geography of Prefecture Cities
in China: The Relevance of Market Access and Labor
Mobility for Agglomeration”, mimeo, February 2012
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Behavior of NEG models in n-region case
› Does “real world”with many regions which are not
equi-distant and differ in size resemble anything like
the “Tomahawk” or “Bell-Shaped Curve” from the 2
region NEG models?
› Answer: (qualified) YES
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Transport costs and the long run equilibrium when distance matters, n=194
b. Without inter-regional labor mobility
a. With inter-regional labor mobility
0.02
1
Herfindahl index
Herfindahl index
0.8
0.6
0.4
0.01
0.2
0
0
0.4
1.4
transport costs
2.4
0.0
0.7
transport costs
1.4
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Transport costs, distance, initial conditions, n=194
b. Without inter-regional labor mobility
a. With inter-regional labor mobility
0.02
1
Herfindahl index
Herfindahl index
0.8
0.6
0.4
0.01
0.2
0
0
0
5
transport costs
10
0
7
transport costs
14
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Motivation for Chinese cities study
› Are Chinese cities too small? (despite rapid
urbanization)….
› … if so: does China, does not benefit fully from
agglomeration economies?
› Main culprit: Hukou system (restricted interregional
labour mobility)
› What will happen with increassed labour mobility?
› Krugman (2011): China=NEG; “what if” questions
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Set up of analysis
› NEG model (extensive mix of agglomeration and (!)
spreading forces (housing rents))
› Use wage equation to estimate structural model
parameters (notably “freeness of trade”)
› Model simulations (with real migration dynamics)
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Initial (2000) distribution
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Conclusions based on China example
› Use strong (and novel) points of NEG approach:
agglomeration is endogenous; NEG provides answers
to “what if” questions
› Comparative advantage of NEG, but this advantage is
not used very well
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Final words……..
How to proceed?
1. Economics can be of greater use to analysis of
EG/spatial interactions,
2. Make better use of NEG while recognizing its
limitations
3. More collaboration or debate? (today’s lecture……)
Credible models in Economic Geography at large?
(Garretsen& Martin, SEA, 2010)