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Geography matters, but how much?
Making economic geography a quantitative …eld
Dávid Krisztián Nagy
CREi and Barcelona GSE...
Geography matters
Why does geography a¤ect aggregate economic outcomes?
Some frictions are spatial by nature:
I transport ...
Standing on the shoulders of giants
Krugman, P. (1991): Increasing Returns and Economic Geography.
Journal of Political Ec...
Krugman (1991): two locations
High transport costs
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography Oct...
Krugman (1991): two locations
Low transport costs
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography Octo...
What are the bene…ts of Spain’s high speed rail network?
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geograp...
What are the bene…ts of Spain’s high speed rail network?
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geograp...
Quantitative models of international trade
Armington (Anderson, 1979)
Eaton and Kortum (2002)
Krugman (1980)
Melitz (2003)...
Quantitative models of international trade
Armington (Anderson, 1979)
Eaton and Kortum (2002)
Krugman (1980)
Melitz (2003)...
Quantitative models of international trade
Armington (Anderson, 1979)
Eaton and Kortum (2002)
Krugman (1980)
Melitz (2003)...
The Geography of Development
Klaus Desmet, Dávid Krisztián Nagy and Esteban Rossi-Hansberg (JPE, forthcoming)
Where a pers...
A quantitative model of growth in space
Each location is unique in terms of its
I amenities
I productivity
I geography
Eac...
Endowments and preferences
Economy occupies a two-dimensional surface S.
I location is point r 2 S
I S is partitioned into...
Migration restrictions
Assumption: m (r, s) = m1 (r) m2 (s) and m (r, r) = 1.
Then an agent’s value function can be writte...
Technology
Production per unit of land of a …rm producing good ω 2 [0, 1]:
qω
t (r) = φω
t (r)γ1
zω
t (r) Lω
t (r)µ
φω
t (...
Productivity draws and competition
Firms face perfect local competition and innovate.
I Productivity draws are i.i.d. acro...
Equilibrium
The spatial distribution of population at time t is given by
B1t (r) Lt (r)
λθ θ
1+2θ
h
α 1+
h
λ+
γ1
ξ [1 µ]
i...
Equilibrium
The spatial distribution of population at time t is given by
B1t (r) Lt (r)
λθ θ
1+2θ
h
α 1+
h
λ+
γ1
ξ [1 µ]
i...
The Handbook of Integral Equations
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 16...
Equilibrium: Existence, uniqueness and solution algorithm
Lemma 3: An equilibrium exists and is unique if
α
θ
+
γ1
ξ
λ + 1...
Calibration
1. Preferences
β = 0.965 Discount factor
ρ = 0.75 Elasticity of substitution of 4 (Bernard et al., 2003)
λ = 0...
Benchmark calibration: Period 1
a. Population density b. Productivity: τt (r) Lt (r)α
1
θ
c. Utility d. Real income per ca...
Keeping migratory restrictions unchanged: Period 600
a. Population density b. Productivity: τt (r) Lt (r)α
1
θ
c. Utility ...
Free migration: Period 1
a. Population density b. Productivity: τt (r) Lt (r)α
1
θ
c. Utility d. Real income per capita
Dá...
Free migration: Period 600
a. Population density b. Productivity: τt (r) Lt (r)α
1
θ
c. Utility d. Real income per capita
...
Large welfare gains from liberalizing migration
Mobility Discounted Real Income* Discounted Utility** Migration Flows***
ϑ...
Evaluating the Economic Cost of Coastal Flooding
Desmet, Kopp, Nagy, Oppenheimer and Rossi-Hansberg (2016)
We evaluate the...
Average sea level rise for 40 random paths
Mean severity scenario
Dávid Nagy (CREi and Barcelona GSE) Quantitative economi...
Losses in real GDP per capita
Mean severity scenario
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography O...
Substantial loss in world welfare
Mean severity scenario
Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geograp...
City Location and Economic Development
Nagy (2016)
Spatial frictions a¤ect aggregate economic growth through the
locations...
City Location and Economic Development
Nagy (2016)
Spatial frictions a¤ect aggregate economic growth through the
locations...
City Location and Economic Development
Nagy (2016)
Spatial frictions a¤ect aggregate economic growth through the
locations...
Consumption
Lt consumers, each endowed with one unit of labor and chooses
I a location to live and work
I a location to tr...
Farm technology
Farmers at r use production function
xF
t (r) = B (r) `F
t (r)α
ht (r)1 α
Land available in exogenous supp...
Non-farm technology
Each non-farm variety is produced by a single …rm, using labor and
the farm good:
xN
t (s) =
"
`P
t (s...
Evolution of non-farm technology
Fundamental productivity at s evolves according to
At+1 (s) = max
r
e δjr sj
At (r)
h
`I
...
Calibration
Parameter Target / Comment
α = 0.71 Labor share in agriculture (Caselli and Coleman, 2001)
ν = 0.75 Non-farm s...
Expansion to the West
Population per square mile in 1790: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1800: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1810: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1820: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1830: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1840: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1850: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Expansion to the West
Population per square mile in 1860: model (top) vs data (bottom)
5
10
15
20
25
30
35
40
45
50
5
10
1...
Railroads reordered population
Population per square mile in 1860, baseline simulation
10
20
30
40
50
Population per squar...
Railroads had a large impact on cities and growth
The absence of railroads
I decreases the sizes of large cities, especial...
Other work
Bridges with Roc Armenter and Miklós Koren
Transit Trade and Economic Geography with Roc Armenter and
Miklós Ko...
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Geography matters, but how much? Making economic geography a quantitative field

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Dávid K. Nagy (CREI, UPF and Barcelona GSE)

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Geography matters, but how much? Making economic geography a quantitative field

  1. 1. Geography matters, but how much? Making economic geography a quantitative …eld Dávid Krisztián Nagy CREi and Barcelona GSE October 14, 2016 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 1 / 44
  2. 2. Geography matters Why does geography a¤ect aggregate economic outcomes? Some frictions are spatial by nature: I transport costs I mobility restrictions I road congestion I di¤usion of technology across space Some shocks are spatial by nature: I railroad construction I political border changes I global warming –rise in sea levels The e¤ect of these shocks on aggregate output, welfare and growth is in‡uenced by spatial frictions, hence by geography. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 2 / 44
  3. 3. Standing on the shoulders of giants Krugman, P. (1991): Increasing Returns and Economic Geography. Journal of Political Economy, vol. 99(3), 483–499. Use formal modeling to explain facts such as I high concentration of economic activity across space I persistence of spatial patterns F but also sudden changes caused by seemingly small shocks Main mechanism: circular causation due to increasing returns, transport costs and labor mobility. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 3 / 44
  4. 4. Krugman (1991): two locations High transport costs Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 4 / 44
  5. 5. Krugman (1991): two locations Low transport costs Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 5 / 44
  6. 6. What are the bene…ts of Spain’s high speed rail network? Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 6 / 44
  7. 7. What are the bene…ts of Spain’s high speed rail network? Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 7 / 44
  8. 8. Quantitative models of international trade Armington (Anderson, 1979) Eaton and Kortum (2002) Krugman (1980) Melitz (2003) Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 8 / 44
  9. 9. Quantitative models of international trade Armington (Anderson, 1979) Eaton and Kortum (2002) Krugman (1980) Melitz (2003) Stay tractable, even with rich geography: I any number of countries I any distribution of population, productivity and transport costs Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 8 / 44
  10. 10. Quantitative models of international trade Armington (Anderson, 1979) Eaton and Kortum (2002) Krugman (1980) Melitz (2003) Stay tractable, even with rich geography: I any number of countries I any distribution of population, productivity and transport costs Use them as a basis to develop quantitative geography models by adding I mobility of labor (and frictions to mobility) I increasing returns (to Armington, EK) I congestion I dynamics Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 8 / 44
  11. 11. The Geography of Development Klaus Desmet, Dávid Krisztián Nagy and Esteban Rossi-Hansberg (JPE, forthcoming) Where a person lives determines their productivity, income and well-being. But a person’s location is neither a permanent characteristic nor a free choice. I How do migratory restrictions shape the economy of the future? I How do they interact and a¤ect the spatial distribution of productivity and amenities? We propose a theory of development that explicitly takes into account I the geography of economic activity I the mobility restrictions and transport costs associated with it Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 9 / 44
  12. 12. A quantitative model of growth in space Each location is unique in terms of its I amenities I productivity I geography Each location has …rms that I produce and trade subject to transport costs I innovate Static part of the model I Allen and Arkolakis (2014) and Eaton and Kortum (2002) I allow for migration restrictions Dynamic part of the model I Desmet and Rossi-Hansberg (2014) I land competition and technology di¤usion Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 10 / 44
  13. 13. Endowments and preferences Economy occupies a two-dimensional surface S. I location is point r 2 S I S is partitioned into C countries L agents, each supplying one unit of labor. An agent’s period utility is ui t (¯r , r) = at (r) Z 1 0 cω t (r)ρ dω 1 ρ εi t (r) t ∏ s=1 m (rs 1, rs ) 1 I εi t (r) is a location preference shock that is iid Fréchet I m (rs 1, rs ) is the cost of moving from rs 1 to rs I amenities take the form at (r) = a (r) Lt (r) λ Agents earn income from work and local ownership of land. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 11 / 44
  14. 14. Migration restrictions Assumption: m (r, s) = m1 (r) m2 (s) and m (r, r) = 1. Then an agent’s value function can be written as V r0, εi 1 = 1 m1 (r0) max r1 u1 (r1) m2 (r1) εi 1 (r1) + βE max r2 u2 (r2) m2 (r2) εi 2 (r2) + V r2, εi 3 where ut (r) = at (r) Z 1 0 cω t (r)ρ dω 1 ρ I current location only in‡uences current utility and not future decision Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 12 / 44
  15. 15. Technology Production per unit of land of a …rm producing good ω 2 [0, 1]: qω t (r) = φω t (r)γ1 zω t (r) Lω t (r)µ φω t (r) is an innovation requiring νφω t (r)ξ units of labor. If γ1 < 1, there are decreasing returns to local innovation. zω t (r) is the realization of a r.v. drawn from a Fréchet distribution F (z, r) = e Tt (r)z θ where Tt (r) = τt (r) Lt (r)α and τt (r) = φt 1 (r)θγ1 Z S ηt 1 (r, s) τt 1 (s) ds 1 γ2 τt 1 (r)γ2 If γ2 < 1, we get global di¤usion of technology. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 13 / 44
  16. 16. Productivity draws and competition Firms face perfect local competition and innovate. I Productivity draws are i.i.d. across time and goods, but correlated across space (with perfect correlation as distance goes to zero). I Firm pro…ts are linear in land, so for any small interval there is a continuum of …rms that compete in prices. I Firms bid for land up to point of making zero pro…ts after covering investment in technology. Dynamic pro…t maximization simpli…es to sequence of static problems. I Next period all potential entrants have access to same technology (Desmet and Rossi-Hansberg, 2014). Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 14 / 44
  17. 17. Equilibrium The spatial distribution of population at time t is given by B1t (r) Lt (r) λθ θ 1+2θ h α 1+ h λ+ γ1 ξ [1 µ] i θ i +Ω θ(1+θ) 1+2θ = κ1 Z S Lt (s) 1 λθ+ 1+θ 1+2θ h α 1+ h λ+ γ1 ξ [1 µ] i θ i Ω θ2 1+2θ B2t (s) ς (r, s) θ ds where B1t ( ) , B2t ( ) , ς ( , ) are exogenously given functions. I This is an integral equation for Lt ( ). Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 15 / 44
  18. 18. Equilibrium The spatial distribution of population at time t is given by B1t (r) Lt (r) λθ θ 1+2θ h α 1+ h λ+ γ1 ξ [1 µ] i θ i +Ω θ(1+θ) 1+2θ = κ1 Z S Lt (s) 1 λθ+ 1+θ 1+2θ h α 1+ h λ+ γ1 ξ [1 µ] i θ i Ω θ2 1+2θ B2t (s) ς (r, s) θ ds where B1t ( ) , B2t ( ) , ς ( , ) are exogenously given functions. I This is an integral equation for Lt ( ). Does the equilibrium exist? Is it unique? How can we solve for it e¢ ciently? Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 15 / 44
  19. 19. The Handbook of Integral Equations Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 16 / 44
  20. 20. Equilibrium: Existence, uniqueness and solution algorithm Lemma 3: An equilibrium exists and is unique if α θ + γ1 ξ λ + 1 µ I Moreover, a simple iterative procedure converges to the unique equilibrium. Lemma 4: There exists a unique balanced growth path if α θ + γ1 ξ + γ1 [1 γ2] ξ λ + 1 µ I This condition is stronger than the one in Lemma 3. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 17 / 44
  21. 21. Calibration 1. Preferences β = 0.965 Discount factor ρ = 0.75 Elasticity of substitution of 4 (Bernard et al., 2003) λ = 0.32 Relation between amenities and population Ω = 0.5 Elasticity of migration ‡ows to income (Monte et al., 2015) 2. Technology α = 0.06 Static elasticity of productivity to density (Carlino et al., 2007) θ = 6.5 Trade elasticity (EK, 2002; Simonovska and Waugh, 2014) µ = 0.8 Labor or non-land share in production (Greenwood et al., 1997; Desmet and Rappaport, 2014) γ1 = 0.319 Relation between population distribution and growth 3. Evolution of productivity γ2 = 0.993 Relation between population distribution and growth ξ = 125 Desmet and Rossi-Hansberg (2015) ν = 0.15 Initial world growth rate of real GDP of 2% 4. Trade Costs Allen and Arkolakis (2014) and elasticity of trade ‡ows to distance of 0.93 (Head and Mayer, 2014) Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 18 / 44
  22. 22. Benchmark calibration: Period 1 a. Population density b. Productivity: τt (r) Lt (r)α 1 θ c. Utility d. Real income per capita Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 19 / 44
  23. 23. Keeping migratory restrictions unchanged: Period 600 a. Population density b. Productivity: τt (r) Lt (r)α 1 θ c. Utility d. Real income per capita Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 20 / 44
  24. 24. Free migration: Period 1 a. Population density b. Productivity: τt (r) Lt (r)α 1 θ c. Utility d. Real income per capita Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 21 / 44
  25. 25. Free migration: Period 600 a. Population density b. Productivity: τt (r) Lt (r)α 1 θ c. Utility d. Real income per capita Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 22 / 44
  26. 26. Large welfare gains from liberalizing migration Mobility Discounted Real Income* Discounted Utility** Migration Flows*** ϑ %∆ w.r.t. ϑ = 0 %∆ w.r.t. ϑ = 0 1a 0% 0% 0.30% 0.75 31% 60% 21.2% 0.5 69% 144% 43.2% 0.25 102% 229% 60.2% 0b 126% 306% 70.3% We use β = 0.965. a: Current Moving Costs. b: No Costs. *: Population-weighted average of cells’real GDP. **: Population-weighted average of cells’utility levels. ***: Share of world population moving to countries that grow between period 0 and 1 (immediately after the change in ϑ). Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 23 / 44
  27. 27. Evaluating the Economic Cost of Coastal Flooding Desmet, Kopp, Nagy, Oppenheimer and Rossi-Hansberg (2016) We evaluate the economic cost of rising sea levels caused by global warming. I based on Desmet, Nagy and Rossi-Hansberg (2016) I analysis is spatially detailed (1 1 cells), dynamic, and includes general equilibrium linkages between locations F trade, migration, agglomeration and congestion are all included I economic data matched with realizations of local and dynamic sea-level rise scenarios I we study mean e¤ects, but also the degree of uncertainty in the costs of ‡ooding Existing literature I accounting exercises based on current data (Dasgupta et al., 2007) I if they account for changing conditions (Nicholls, 2004) F lack of detail: all regions in a country behave in the same way F no linkages with areas that are not a¤ected directly Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 24 / 44
  28. 28. Average sea level rise for 40 random paths Mean severity scenario Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 25 / 44
  29. 29. Losses in real GDP per capita Mean severity scenario Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 26 / 44
  30. 30. Substantial loss in world welfare Mean severity scenario Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 27 / 44
  31. 31. City Location and Economic Development Nagy (2016) Spatial frictions a¤ect aggregate economic growth through the locations and sizes of cities as I cities host …rms that are the engines of innovation, I they provide dynamic externalities that foster growth. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 28 / 44
  32. 32. City Location and Economic Development Nagy (2016) Spatial frictions a¤ect aggregate economic growth through the locations and sizes of cities as I cities host …rms that are the engines of innovation, I they provide dynamic externalities that foster growth. Propose a quantitative model of endogenous growth and city formation in space. I any number of locations I any distribution of trade costs, land and productivity Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 28 / 44
  33. 33. City Location and Economic Development Nagy (2016) Spatial frictions a¤ect aggregate economic growth through the locations and sizes of cities as I cities host …rms that are the engines of innovation, I they provide dynamic externalities that foster growth. Propose a quantitative model of endogenous growth and city formation in space. I any number of locations I any distribution of trade costs, land and productivity Use model to study determinants of city formation and growth in 19th-century United States. I e¤ect of railroad construction I e¤ect of international trade Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 28 / 44
  34. 34. Consumption Lt consumers, each endowed with one unit of labor and chooses I a location to live and work I a location to trade A consumer living at location r and trading at location s obtains per-period utility Ut (r, s) = Z nt 0 xN t (r, s, i) ε 1 ε di ν ε ε 1 xF t (r, s)1 ν Consumers decide in which sector to work. I farmers produce at home, sell their good and shop at the trading place I non-farm workers work for …rms and shop at the trading place F high enough commuting costs ) live, work and trade at the same place Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 29 / 44
  35. 35. Farm technology Farmers at r use production function xF t (r) = B (r) `F t (r)α ht (r)1 α Land available in exogenous supply Ht (r) > 0. I leads to dispersion of farm production across space I dispersion reinforced by population growth and territorial expansion Shipping the good to trading place s subject to iceberg trade cost ςt (r, s) 1. I leads to dispersion of consumers across space Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 30 / 44
  36. 36. Non-farm technology Each non-farm variety is produced by a single …rm, using labor and the farm good: xN t (s) = " `P t (s) β 1 β + h bAt (s) xF t (s)µ iβ 1 β # β β 1 I rising productivity bAt (s) induces structural change if β 6= 1 Non-farm …rms agglomerate and create cities due to I increasing returns: …xed per-period operation cost f > 0 I shipping subject to iceberg trade costs τt (s, r) 1 Agglomeration reinforced by structural change. I calibrate β to match U.S. urbanization Firm can increase its productivity by hiring workers to innovate: bAt (s) = At (s) `I t (s)1 µ Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 31 / 44
  37. 37. Evolution of non-farm technology Fundamental productivity at s evolves according to At+1 (s) = max r e δjr sj At (r) h `I t (r)1 µ + g LN t (r) i Growth rate shifted by size-dependent dynamic externality g LN t (r) . I choose g LN t (r) = γ if LN t (r) λ 0 if LN t (r) < λ I motivated by evidence on di¤erent growth rates of cities vs towns, which suggests λ = 10, 000 I calibrate γ to di¤erence in city vs town growth rate Technology di¤uses across space, with exponential decay e δjr sj. I perfect local di¤usion and free entry into non-farm sector (DRH, 2014) ) …rm solves a static problem as future gains to innovation are 0 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 32 / 44
  38. 38. Calibration Parameter Target / Comment α = 0.71 Labor share in agriculture (Caselli and Coleman, 2001) ν = 0.75 Non-farm share in consumption (Lebergott, 1996) ε = 9 Trade elasticity (Donaldson and Hornbeck, 2015) δ = 0.005 Speed of technology di¤usion (Comin et al., 2013) B ( ) FAO GAEZ data and 1860 Census of Agriculture φ = 0.44 Concentration of population, 1790 A0 (k) Population of …ve pre-existing cities k, 1790 A0 (s) = 0.10 Non-farm employment share (s 6= k), 1790 f = 4.4 Growth in real GDP per capita until 1860 γ = 0.014 Di¤ between avg growth of cities vs towns until 1860 µ = 0.9 Convergence in city size until 1860 β = 0.29 Increase in urbanization until 1860 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 33 / 44
  39. 39. Expansion to the West Population per square mile in 1790: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 34 / 44
  40. 40. Expansion to the West Population per square mile in 1800: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 35 / 44
  41. 41. Expansion to the West Population per square mile in 1810: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 36 / 44
  42. 42. Expansion to the West Population per square mile in 1820: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 37 / 44
  43. 43. Expansion to the West Population per square mile in 1830: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 38 / 44
  44. 44. Expansion to the West Population per square mile in 1840: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 39 / 44
  45. 45. Expansion to the West Population per square mile in 1850: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 40 / 44
  46. 46. Expansion to the West Population per square mile in 1860: model (top) vs data (bottom) 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 41 / 44
  47. 47. Railroads reordered population Population per square mile in 1860, baseline simulation 10 20 30 40 50 Population per square mile in 1860, no railroads 10 20 30 40 50 Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 42 / 44
  48. 48. Railroads had a large impact on cities and growth The absence of railroads I decreases the sizes of large cities, especially Boston (by 75% in 1860) and Philadelphia (by 25% in 1860), I decreases real GDP in 1860 by 6.4%, I decreases the growth rate by 23%, from 1% to 0.77% per year. Endogenous city development accounts for 40% of the e¤ect on real GDP. Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 43 / 44
  49. 49. Other work Bridges with Roc Armenter and Miklós Koren Transit Trade and Economic Geography with Roc Armenter and Miklós Koren Dávid Nagy (CREi and Barcelona GSE) Quantitative economic geography October 14, 2016 44 / 44

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