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".

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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.
  • Empirics of economic geography

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