bones, bombs, and break points the geography of economic activity
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Bones, Bombs, and Break Points The Geography of Economic Activity. Donald R. Davis and David E. Weinstein Columbia University and NBER. The Questions. The Central Question of EG is: Why does the density of economic activity vary? why vary across region? why vary across countries? - PowerPoint PPT PresentationTRANSCRIPT
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Bones, Bombs, and Break Points
The Geography of Economic Activity
Donald R. Davis and David E. Weinstein
Columbia University and NBER
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The Questions
The Central Question of EG is:
Why does the density of economic activity vary?
why vary across region?
why vary across countries?
why vary across regions within countries?
why vary across cities?
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Why do we Care• It is a first order thing that we wonder about!
Krugman• We have policies whose motivation is to affect the location
of EA– Urban Policy– Regional Policy– Trade/Policy– Catastrophes
• It can matter for welfare whether a region has a lot of EA or not.
• Understanding the location and function is a key determinant of production and hence trade
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Theories
I) IR TheoriesA) External Efforts (e.g. Henderson)
- Industry specific positive externalities- General congestion effects- Combination of these gives rise to variation in
city sizesb) NEG: Monopolies, competition and thick costs (FKU)c) Other theories, (e.g. labor market pooling)
IRS forces a producer to choose a location choices are interdependent.
Producers want to be near demanders and input producers labor may be mobile and wants to be near producers.
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Theories
II) RG:
Depending on stochastic process generating Cities. You can get very different sizes of cities.
- Some cities get a lot of positive shocks
Not a lot of economics underlying this but
- This theory can explain – Zipf’s law.
Graph
- IR cannot generate this
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Zipf Graph
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Theories
III) LF
Krugman: a broad range of natural resources follow
Zipf: River flows
Each location is the sum of a long sequence of randomly distributed spatial qualities.
- river, coast, harbor, desert, mountaintop, rainfall, flatland, latitude, etc.
Can explain zipf’s law.
Difference with RG is response to temporary shocks
RG shocks are permanent
LF Temp shocks are temporary
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What we do
We Look at Japanese Historical Data
Japan is a particularly good place because it is possible to conduct regional census data going back to 725
Also have data distribution of archaeological cities back to 6000 BC
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The Question Dataset I
I) How much variation historicallyFKU quote.
II) Persistence if we go back to Stone Age/ Japan how much information does that give us about modern densities
- what about yayoi- what about 1600- In IRS world if you see a lot of persistence then
you must believe in path den.Henderson models do not because industries change.
- RG predicts persistence if near growth rate is high relative to variance
- LF predicts persistence
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The Questions Dataset II1) If econ were a laboratory science you would want to conduct
experiments by cloning shocks to city sizes.
- Natural Experiment does same thing Allied bombing of Japan during WWII
2) Essential Question if you kill up to 21% of a city or knock out up to 99.5% of its building does it return to its former size or not.
What do our theories tell us.
I) IRS – suggests the possibility of catastrophes passing over critical values of city sizes has permanent effects. Are these catastrophes a central part of the data or are they a theoretical curiosity.
Can temporary economic policies permanently alter the geography of EA.
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Dataset II
RG: this is a decisive test.
RG; Growth is a random walk
LF: Temporary shocks can have no permanent effects.
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History1) First we look at variation in regional dens.
- How much is there- What explains it.
2) FKU – without IRS we cannot understand the variation in regional dens.- FKU quote- Mfs belt – talk about his at various levels of aggregation.
Strategy: go back in time and askHow much was thereAny Changes.Hypothesis Importance of IRS decreases as we go back in time
- Knowledge spillovers }- RED } Less - Linkages }- Tech growth }- Costs of tracks} more
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Data Slide
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History
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Measures
1) Non parametric: C5 share
2) Parametric: Rel variation of log pop density (Prop variation) Dist as F statistic
3) Zipf coeff: Luri = aludensity + Two graphs
Equality: = - perf inequality: = 0
Trad: Zipf run on cities/ agglon’s. Run on regional densitiesControl for measurement error.Correct w/ instr. of log size w/ correct log density.
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Results on Concentration
Lessons
1) Always lots of variation
2) Modern variation larger (although not always so ???)
3) Period when rises coincides with industries
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Persistence
1) Correl’s of 0.5 or better variation trend at nearly all times
2) 1600 correl 0.8
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What do we learn?
Under main tide by that IRS held irp + earlier
1) IRS not crucial for high degree of variation
2) Increase in variation at same time as ind. Is pred’ed by IRS (not clearly by others.)} counter w/ Eaton Echst spatial correls
3) If IRS then Lath dependence
If RG then low variance relative to means
LF clearly pred’s this
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Bombing
Do large temporary shocks have permanent effects on city size?
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Data Slide
Variables
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Why is this a good experiment?
1) Big Shocks
- 66 cities
- ½ of studies 2.2 mm bldgs
- 300, 000 killed
- 40% of population homeless
- some lost ½ of population
Details
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2)Variance
3) Temporary
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Specification
Detail
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Results
Figures
Tables