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Urban and Regional Economics Prof. Clark

Urban and Regional Economics Prof. Clark. ECON 246 Weeks 5 and 6. Discussion of Growth Papers. Bartik Addresses the question of who benefits from regional growth Noll and Zimbalist Does the building of stadiums promote economic growth. A Brief Overview of Central Place Theory.

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Urban and Regional Economics Prof. Clark

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  1. Urban and Regional EconomicsProf. Clark ECON 246 Weeks 5 and 6

  2. Discussion of Growth Papers • Bartik • Addresses the question of who benefits from regional growth • Noll and Zimbalist • Does the building of stadiums promote economic growth

  3. A Brief Overview of Central Place Theory

  4. Size distribution of U.S. cities Pop Range Number of Areas • >12.8 million 1 • 6.4 - 12.8 million 2 • 3.2 - 6.4 million 4 • 1.6 - 3.2 million 14 • 800k - 1.6 million 19 • 400k - 800k 33 • 200k - 400k 52 • 100k - 200k 99 • 50k - 100k 172

  5. Systems of Cities • Considers market areas • Focus is on distribution of goods within market • Derive market shapes in competitive market structure • Cities shaped by markets for various goods and services • Look at market sizes • Look at number of markets

  6. Define Market Areas • Producer assumptions • Producers serve geographic areas. • Producers have same technology • ubiquitous inputs • No agglomeration in shopping • Consumer assumptions • Consumers evenly distributed over space • Buyers must travel to store to buy goods (constant travel costs per mile) • Consumers care about the net price

  7. Net price=store price + transport cost Consumers living closer to market pay lower net prices. Market area defined by cost of home production x miles in this case Suppose monopolists carve up a region Net Price Graphically Market Area for Firm Net Price Cost of HP Store Price x 0 x Distance from Market Center

  8. Monopoly Markets Non-Competing Market Areas $ Monopolist 1 Monopolist 2 Monopolist 3 Monopolist 4 Distance from Market Centers

  9. Monopolist 1 Monopolist 2 Monopolist 3 Monopolist 4 Monopolist 5 Monoplist 6 Monopolist 7 Monopolist 8 Market Areas

  10. Assume monopolists are making pure economic profits Is this a stable situation? If not, where would firms enter? Monopolist 1 Monopolist 2 Monopolist 3 Monopolist 4 Monopolist 5 Monoplist 6 Monopolist 7 Monopolist 8 Introducing Competition

  11. Monopolist 1 Monopolist 2 Monopolist 3 Monopolist 4 Monopolist 5 Monoplist 6 Monopolist 7 Monopolist 8 Firm Entry Drives Out Profits Firms enter here

  12. All profits driven out Market structure is monopolist comp. No areas unserved Note: Actual shape of market is open to debate. Introduce other markets Eventual Market Shape

  13. Different sized markets Market sizes differ according to the scale economies and density of demand. There are many small markets and fewer large markets.

  14. Cities simply reflect Collections of Markets • Maybe only one market for top level plays • Located in NYC, and spans entire nation • Maybe 4 markets for large international airports • LA, NY, Chicago, Atlanta • There are many markets for gas stations

  15. How Realistic is this? • Are assumptions satisfied? • What does this imply about value of model? • Does model predict well? • Rank-size rule: • Rank*Size=constant • Statistical regularity in some regions • Doesn’t seem to hold in U.S. • Population more evenly distributed over space than this suggests. • Possible reasons?

  16. The Regional IO model • The regional IO model is based on an accounting identity that states: Thesum of all inputs must equal the sum of all outputs. • Assuming: • accurate accounting of all sectors • accurate account of for all the transactions between sectors and outside economy • Then identity should hold!

  17. Overview of How Model Works • Step 1: Model identifies sectors in the regional economy, and then sets up a transactions table to evaluate resource flows between these sectors. • Step 2: From transactions table, coefficients of technical coefficients can be inferred. • Step 3: Derive demand relationships. • Step 4: Shocks in external or final demand are mapped to each sector.

  18. Step 1: Transactions Table • Output Sold To • Inputs Manuf. Service Trade Households Exports Gross • SuppliedbyOutput • ------------------------------------------------------------------------------ • Manuf. 6 4 10 0 20 40 • Service 5 8 2 25 10 50 • Trade 0 0 0 30 0 30 • Local L,K,D 14 33 8 0 0 55 • Imports 15 5 10 0 --- 30 • Total Inputs 40 50 30 55 30 • This gives indication of intersectoral interdependencies.

  19. Step 2: From Transactions Table to Technical Coefficients • Determine how much of the total value of inputs for the sector was spent on any given output. • Divide the column by the total input value for that column.

  20. Technical Coefficients Table Manuf. Service Trade Households ------------------------------------------------------------------------------ Manuf. 0.15=6/40 0.08=4/50 0.33=10/30 0.00=0/30 Service 0.125=5/40 0.16=8/50 0.067=2/30 0.455=25/55 Trade 0.00=0/40 0.00=0/50 0.00=0/30 0.545=30/55 Local VA 0.35=14/40 0.66=33/50 0.26=8/30 0.00=0/55 Imports 0.375=15/40 0.10=5/50 0.33=10/30 0.00=0/55 Total Inputs 40 50 30 55 Column Interpretation: How inputs are used in the sector. For $1 of Manuf., you use $0.15 of Manuf., $0.125 of Service, $0 of Trade, $0.35 of Local inputs, and $0.375 of Imports.

  21. Technical Coefficients Table Manuf. Service Trade Households ------------------------------------------------------------------------------ Manuf. 0.15=60/40 0.08=4/50 0.33=10/30 0.00=0/30 Service 0.125=5/40 0.16=8/50 0.067=2/30 0.455=25/55 Trade 0.00=0/40 0.00=0/50 0.00=0/30 0.545=30/55 Local VA 0.35=14/40 0.66=33/50 0.26=8/30 0.00=0/55 Imports 0.375=15/40 0.10=5/50 0.33=10/30 0.00=0/55 Total Inputs 40 50 30 55 Row Interpretation: Who buys output Manuf. Demand = 0.15*M+0.08*S+0.33*T+0*Y(income)+XM XM is known as final or exogenous demand.

  22. Step 3: Derive Demand Equations • M=0.15*M + 0.08*S + 0.33*T + 0.00*Y + XM • S=0.125*M + 0.16*S + 0.067*T +0.455*Y + XS • T= 0.00*M + 0.00*S + 0.00*T +0.545*Y + 0 • Y= 0.15*M + 0.66*S + 0.267*T + 0.00 *Y + 0 • Endogenous variables: M, S, T and Y are determined inside this system of equations: (i.e., we have 4 equations and 4 unknowns) • Exogenous Variables: XM, XS, (XT=0 in this case) (XY=0 since this is local value added) are determined outside this system.

  23. Question: If we solved for M, S, T and Y given current values of exports, what solution would we get? M=40, S=50, T=30 and D=55

  24. Can Derive Local Multipliers(Manipulate so each sector depends only on X) • M = 1.613*XM + 0.772*XS • S = 1.141*XM + 3.236*XS • Y = 1.542*XM + 2.413*XS • T = 0.880*XM + 1.316*XS • Thus, multipliers no longer constant for all sectors!

  25. Step 4: Mapping out influence of Disturbances • Suppose exports change: • Then you have a new set of four equations and four unknowns to solve simultaneously. • Technical coefficients don’t change. • What does this assume about input substitutability? • Get new endogenous levels of demand, as a result of the external shock. • Allows you to get idea of interdependencies between sectors and how growth in one sector effects other sectors.

  26. Limitations • This is still a demand-based model. • It does not allow for supply effects. • Implicitly assuming constant wage (i.e., horizontal supply). • Why? • SR model • Assumes constant multipliers • LR vs. SR assumption? • No substitution available. • LR vs. SR?

  27. Limitations - continued • Regional limitations • Difficult to get local transactions tables • National proxies must be used but they may be inappropriate. • Regional technical coefficients may change more rapidly than national coefficients.

  28. Extensions of this approach • Over the last 20 years, this model has been refined substantially. • There are ways to deal with supply issues. • There are also ways to allow isoquants to be smooth (i.e., allow inputs to be substituted in production).

  29. Two popular models: IMPLAN and REMI • IMPLAN model is pure IO model • REMI model is commercially available hybrid model. • Developed by George Treyz at U. Mass. • Has an econometric and an IO component. • Does incorporate supply effects. • Widely used by policy makers. • Look at demo

  30. Regional Econometric Modeling • These are constructed differently than IO or Export-Base Models. • Can incorporate both supply and demand factors. • Model is based on model-builders beliefs about how the urban economy works. • Relationships are typically estimated using local, regional and national data.

  31. Regional Econometric Models: Overview Roger Bolton Journal of Regional Science, 1985, Vol. 25 (4) pp. 495-519. Not assigned but on reserve FYI

  32. Very thorough review article • Article focuses on academic models which have been developed in 1970’s and early 1980’s. • Focuses on single-region models. • Our focus on Sections 1-4 briefly, and 13-14. • 5-12 give specific details on individual components of models. • Keep this paper handy as a reference, should you work in public policy.

  33. Exogenous vs. Endogenous Variables • Distinction between two types • Advantage of model with numerous endogenous variables. • Can model simultaneous (feedback) effects between variables. • e.g., increase in demand may put upward wage pressure in the sector, and ultimately lead to inmigration. • Disadvantage • Difficult to estimate due to data limitations.

  34. Level of Aggregation • Single-region model • May develop model for Milwaukee, or Southeastern Wisconsin. • Everything else is considered ROW. • No interdependencies between cities in region. • Multi-regional model • May include metropolitan areas in Wisconsin • Can include all metropolitan areas in state. • Derive the interdependencies in great detail.

  35. From regional to national is called bottom-up structure From national to regional is called top-down structure Link between region and the rest of world (ROW) is frequently unidirectional. Single-Region Model Regional Model National Model (ROW) Bottom up influences (i.e., ) are frequently negligible.

  36. When Bottom-up links are not negligible • One sector in region is dominant for nation. • e.g., Detroit and auto industry. • When single region is large. • e.g., Suppose California is considered a single region. • Region’s policies affect national markets • e.g., California emission standards

  37. Multi-Regional Models • Bottom-up component now more likely to be important. • Interregional feedback effects now possible. • Models get more complex. • Lets look at Bolton’s Figure 2. • We break it into components.

  38. Multi-regional Models: National components Exogenous National Variables Endogenous National (regional sum) Endogenous National Vars. (not regional sum)

  39. Multi-regional Models: Adding Regional Components Exogenous National Variables Endogenous National (regional sum) Endogenous National Vars. (not regional sum) Region 1 Model Region 3 Model Region 2 Model Interregional Feedback Effects

  40. Multi-regional Models: Top-down structure Exogenous National Variables Endogenous National (regional sum) Endogenous National Vars. (not regional sum) Region 1 Model Region 2 Model Region 3 Model

  41. Multi-regional Models: Bottom-up structure Exogenous National Variables Endogenous National (regional sum) Endogenous National Vars. (not regional sum) Region 1 Model Region 2 Model Region 3 Model

  42. Which is theoretically preferred?

  43. Differences between regional and national models • National models based on National Income identity: Y=C+I+G+X-M • Data limitations prevent comparable regional models. • Components C, I, X, and M typically not available. • Regional income becomes sum of labor earnings or industry output.

  44. Other data limitations • Nonmanuf. output data less readily available. • No investment data on nonmanuf. sector. • Nonlabor income is difficult to track from region to region. • i.e., returns on land and capital earned in one region and spent in another. • Thus, focus is on less mobile labor income. • Capital stock even in manuf. sector weak. • No public capital stock included.

  45. Models tend to be SR rather than LR Since models can’t deal well with changes in industrial structure due to investment, they tend to be SR rather than LR.

  46. Purposes • Models are often built for a specific purpose. • pure science (not typical) • forecasting • government revenue forecasting • policy analysis • Like other academic endeavors, models may not be balanced. • Tend to favor purpose of the modeler. • Tests of forecast performance rarely done for long term forecasts due to limited time-series.

  47. Econometric Techniques • Frequently use OLS. • Sample sizes may be to small to take advantage of 2SLS. • Data limitations may make identification a problem • Monte Carlo studies suggest that the simultaneous equation bias is small.

  48. Advantages and Disadvantages • Advantages • Flexibility • Ability to model both supply and demand side of economy. • Disadvantages • Expensive to build • Data constraints frequently lead to top-down even when theory suggests bottom-up design.

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