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Empirics of economic geography

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14 июля / Суздаль Началась первая летняя школа трёхлетнего проекта РЭШ "Роль географии в экономике: теория и эмпирика".

14 июля / Суздаль Началась первая летняя школа трёхлетнего проекта РЭШ "Роль географии в экономике: теория и эмпирика".

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  • Market potential M = part of variable profits! ( π – F = M*f(c)) P σ -1 – price index. Because of CES it = cost* markup. I.e. info on costs is already in there in a theoretical model.
  • Transcript

    • 1. Empirics of Economic Geography, part 1 Suzdal, 2010
    • 2. Stylized facts of spatial development patterns
      • Inequality (!)
    • 3. Earth at night
    • 4. Rappaport, Jordan & Sachs, Jeffrey D, 2003. " The United States as a Coastal Nation ," Journal of Economic Growth , Springer, vol. 8(1), pages 5-46, March.
    • 5. Stylized facts of spatial development patterns
      • Inequality (!)
        • Physical geography plays an important part
        • Role of natural endowment?
          • Yes, in agricultural societies
          • Yes, for primary industries and if transport costs are high
      • Persistence
        • Physical geography plays a role in initial concentration of economic activity
        • Lock-in effects work long after the initial factors become unimportant
    • 6.  
    • 7. Stylized facts of spatial development patterns
      • Inequality (!)
        • Physical geography plays an important part
        • Role of natural endowment?
          • Yes, in agricultural societies
          • Yes, for primary industries and if transport costs are high
      • Persistence
        • Physical geography plays a role in initial concentration of economic activity
        • Lock-in effects work long after the initial factors become unimportant (agglomeration externalities)
    • 8. Agglomeration externalities
      • Questions for the empirical research:
        • Concentration increases productivity (?)
          • Is this true? What do the data say?
          • How to measure?
        • How do firms choose location? Do firms prefer to cluster?
        • What factors determine concentration? Why concentrate in a particular location?
          • Location-specific
          • Industry-specific
    • 9. The determinants of spatial concentration
      • Kim, S., 1995 “Expansion of Markets and the Geographic distribution of economic activities”, Quarterly Journal of Economics 110:881-908
      • Let I s,t – measure of concentration
        • (t = time, s = industry)
      • Estimate
        • I s,t = X s,t β + γ s + δ t + ε s,t
          • choose a set of explanatory variables X s,t (size of plant, share of raw materials)
          • Why are industries concentrated? Because it is profitable (increasing returns)?
          • Test: are industries with stronger increasing returns more concentrated spatially? Answer: yes.
    • 10. The determinants of spatial concentration, cont.
      • Omitted variables problem
        • Omitted variable can drive both concentration and plant size
        • What is missing?
          • Trade costs
          • Intermediate goods
          • Demand factors (elasticity of substitution)
          • Production factors and production technologies
          • History
          • … .
        • Omitted variables lead to bias in β estimates
        • Solution (partial): panel data with individual effects
          • Omitted variables that vary with time and industry are still a problem
    • 11. The determinants of spatial concentration, cont.
      • How do we measure concentration?
        • (S. Kim had Gini coefficients)
        • Ellison G., and E.L. Glaeser 1997, “Geographic Concentration in U.S. Manufacturing Industries: a Dartboard Approach”, Journal of Political Economy 105: 889-927
          • Index has to be neutral to the # of firms in the industry
        • But the topic of measuring concentration needs a whole lecture
          • Choice of industrial disaggregation level
          • Choice of spatial unit, etc
          • … for the next session
    • 12. The determinants of local productivity
      • Consider a production function:
      • y j = A j (s j l j ) μ k j 1- μ
        • A j – technology level, s j – labor productivity
        • What do A,s depend on? Parameterize, estimate production function.
          • Problems?
          • The usual: 1)endogeniety, 2) omitted variables
      • If we have wage data
        • Use firm’s FOC
    • 13. Firm’s maximization problem
      • π j = ∑ b p jb y jb – w j l j – r j k j  max
        • p jb – price in region b, y jb – quantity exported into region b
        • let p j = ∑ b p jb (y jb /y j )
        • π j = p j y j – w j l j – r j k j
      • From FOC:
        • w j = μ p j A j s j μ (k j /l j ) 1- μ and r j = (1- μ )p j A j s j μ (k j /l j ) - μ
        • or w j = μ (1- μ ) (1- μ )/ μ s j (p j A j /r j 1- μ ) 1/ μ
        • or (wage equation):
        • ln w j = const + ln s j + 1/ μ ln p j + 1/ μ ln A j –( μ -1)/ μ ln r j
    • 14. Estimating wage equations:
      • ln w j = const + ln s j + 1/ μ ln p j + 1/ μ ln A j –( μ -1)/ μ ln r j
      • Parameterize A and/or s
      • Test whether they increase with density
        • example: ln w rs = α + β ln den r + ε rs
      • Problems?
        • Omitted variables, again
        • Endogeniety
          • density is endogenous
          • self-sorting
          • Solutions: IV, panel data
    • 15. Combes, Duranton & Gobillon 2008. " Spatial wage disparities : Sorting matters ! ," Journal of Urban Economics , v ol. 63(2), pages 723-742 .
      • Large panel data on individual workers
        • worker fixed effect (= ability)
        • lifetime learning (proxied by age)
        • industry effect
        • time effect
      • Compare with the results obtained with average regional wages
      •  worker sorting explains 50% of agglomeration economies estimate
        • individual data: double the density  productivity ↑ by 2%
        • aggregate data: ….  by 5%
      • Sorting matters, reduces estimates.
      • But agglomeration economies remain >0
    • 16. How do firms choose location?
      • Logit framework for empirics of location choice:
        • Carlton (1983) used McFadden’s logit model
        • Several alternative locations, firm chooses one
        • U r - location-specific factors, common
        • ε r - firm-specific shock for each location, individual
        • Locate in r iff π r * > π s * for all s.
        • If F( ε r ) = exp(exp(- ε r )),
        • then P{choose location r} = exp(U r )/∑ s exp(U s )
        • Linearize, estimate
    • 17. What are the factors that attract firms?
        • … exogenous
      • Low costs
      • Higher productivity
      • Being closer to consumers, to save on trade costs
        • Market potential
    • 18. Market potential: history of thought
      • Old idea (Harris, 1954)
        • MP r = ∑ s Y s /d rs
      • Evidence from growth literature: proximity to the developed countries increases GDP
      • NEG models
        • theory-based explicit expression for RMP
        • RMP = ∑ s φ rs μ s Y s P s σ -1
        • (since π = c r 1- σ / σ * (∑ s φ rs μ s Y s P s σ -1 ) – F r = c r 1- σ / σ *M – F r )
    • 19. Head & Mayer, 2004 ( φ =?)
    • 20.  
    • 21.  
    • 22.  
    • 23.  
    • 24.  
    • 25. Head and Mayer, 2004: conclusions
      • Market potential attracts firms, but…
        • Harris’ measure performs better than Krugman’s
        • Nonstructural agglomeration controls are significant
          • why do firms cluster? DSK: because of downstream linkages (customers). But this is not the whole story.
          • Are there important upstream and horizontal linkages?
          • Or omitted variable bias?
      • Wages are +, unemployment -, subsidies –
        • endogeniety
    • 26. Stability of spatial development patterns
      • Why are agglomerations so persistent?
        • Location fundamentals are good
        • Agglomeration externalities are strong
        • Which are more important for regional development?
          • Regional policy is useless if location fundamentals prevail in long run
    • 27. (Davis, Weinstein (2002), “Bones, Bombs, and Breakpoints…”
      • “ Bones” part – regional archeological data
      • Lessons:
        • persistence in density levels and rank (Could this be explained by the physical geography?)
        • same level of concentration before XXth century, increasing after
        • closure to trade decreases concentration
      • Variation in regional population density is great for all historical periods and persistent through history. Does this look like random growth?
        • Zipf’s law holds (Could be explained by the features of physical geography)
    • 28.  
    • 29.  
    • 30.  
    • 31.  
    • 32. (Davis, Weinstein (2002), “Bones, Bombs, and Breakpoints…”, cont.
      • “ Bombs and Breakpoints” – WWII bombing data
        • Estimate ρ (How? Use bombing data for identification)
      • Lessons:
        • mean-reversion: ρ = 0 by 1965 (even if controlled for reconstruction efforts!)
        • No breakpoints (no multiple equilibria)?
      S i =  i +  it where  it+1 =   it + v it+1
    • 33.  
    • 34.  
    • 35. (Davis, Weinstein (2002), “Bones, Bombs, and Breakpoints…”, cont.
      • Lessons, cont:
        • mean-reversion: ρ = -1 by 1965 (even if controlled for reconstruction efforts!)
        • Tails of the distribution are “nice”.
          • Where are the breakpoints?
        • What is a role of increasing returns and location fundamentals?
          • Historically: first geography, then IR
          • At present: unknown. War shocks destroy infrastructure, but do not destroy either location fundamentals or agglomeration externalities.
      • Other countries?
        • Miguel , Roland (2005) : in Vietnam spatial structure unaffected by bombing in long run
    • 36.  
    • 37. Davis , Weinstein (2008) “ A Search For Multiple Equilibria In Urban I ndustrial Structure ”, JRSc
      • The war shock to the industrial production is even larger
    • 38.  
    • 39.
      • Cities were specialized
    • 40.
      • But there is still mean-reversion in aggregate manufacturing shares…
    • 41.
      • And even in the shares of each industry separately
      Single spatial eq’m?
    • 42.  
    • 43. More natural experiments: Germany
      • Germany bombing (Garretsen, Schramm, Brakman (2003)):
        • mean-reversion in West Germany
        • no mean-reversion in East Germany
        • ρ Ger < ρ Jap
          • Is it due to longer adjustment period, or is Germany converging to a different equilibrium?
          • Bosker, Brakman, Garretsen,
          • Schramm (2005):
          • 2 equilibria,
          • geography matters
    • 44. More natural experiments: Germany, cont.
      • German division and reunification
        • Redding, Sturm , Wolf , (2007) “ History and Industry Location: Evidence from German Airports ”
          • Major air hub has shifted from Berlin to Frankfurt after division
          • … and did not shift back to Berlin after unification
          • … while if not for the division, Berlin would be a hub
    • 45.  
    • 46. Redding, Sturm , Wolf , (2007) “ History and Industry Location: Evidence from German Airports ”
      • Is this a change in economic fundamentals or multiple equilibria?
        • Measure the “fundamentals” and compare Frankfurt and Berlin
          • From international data on cities and air traffic: Berlin today has all the economic advantages to be a hub
          • Projected losses of passenger traffic from relocating the hub are small relative to sunk costs
    • 47.
      •  multiple possible hub locations
    • 48. German division and reunification: role of distance and market access
      • Growth literature: development and market access correlate. Causality?
        • Redding , Sturm (2008) “ The Costs of Remoteness: Evidence from German Division and Reunification ”, AER
      • German division = a shock to market access for the cities close to the border
        • What are the consequences for development?
      • Strategy: write a NEG model, estimate some parameters, calibrate the model to German cities, compare estimations with calibrations  find the parameters
        • significant impact of division
        • negligible impact of reunification
    • 49.  
    • 50. Setup questions
      • Division and reunification = shocks to market access (market potential) for the cities on the border
          • were the shocks unexpected?
        • Does it lead to the decline of the affected cities? (treatment group)
          • yes, it does
        • Are there alternative explanations for the decline?
          • pre-existing dynamics and changes in industry structure
          • differences in wartime destruction, refugees
          • economic integration with the western neighbors
          • fear of armed conflict
          •  need to rule out
    • 51. Theoretical framework
      • Helpman (1998) “The size of regions”
        • fixed number of cities = C, each endowed with nontradeables = H C (amenity)
        • Consumption: μ = share of tradeables (differentiated as per D-S, σ >1), 1- μ = share of amenities
        • city-to-city transport cost matrix: T ij
        • labor: inelastic supply=1, free mobility
        •  get the (long-run!) size of the city as a fnc of:
          • consumer price index
          • firm market access index
          • amenity stock
    • 52. Calibrations:
      • μ =2/3, σ =4, T = (distance) φ , φ =1/3
      • after division T->∞ for the cross-border terms
    • 53.
      • The effect is stronger for small cities (HME!)
    • 54. Estimations
      • Errors: clustering by city (why?)
      • Why need β ?
    • 55.  
    • 56.  
    • 57.  
    • 58. Results:
      • small cities suffer more, indeed
      • 75 km ≈ border cutoff
      • Regional policy act (1965) = aid to the cities on the immediate border (<25 km)  all the effects are underestimated!
      • + robustness checks
    • 59. Quantitative analysis
      • grid search over parameters ( μ , σ , φ ) in single-equilibrium range ( σ (1- μ )>1)
    • 60.
      • Parameter of interest = distance coefficient
        • (1- σ ) φ ≈ 1.6 > than in international trade gravity estimates
        • and < than in interregional trade (= 1.76)
        • Land vs sea transport?
    • 61. Structural changes (robustness check)
    • 62. Destruction, refugees and migration
    • 63. Western integration
    • 64. Reunification
    • 65. Reunification:
      • The change in market access is much smaller than from division
      • convergence between East & West  recovery of the border cities

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