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Urban and Regional Urban and Regional EconomicsEconomicsProf. ClarkProf. Clark

ECON 246Weeks 5 and 6

Discussion of Growth Discussion of Growth PapersPapers 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 A Brief Overview of Central Place TheoryCentral Place Theory

Size distribution of U.S. Size distribution of U.S. cities 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

Systems of CitiesSystems 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

Define Market AreasDefine 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

Market Area for FirmMarket Area for Firm

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 GraphicallyNet Price

Distance from Market Center0

StorePrice

Costof HP

x x

Monopoly MarketsMonopoly Markets

Non-Competing Market Areas

Monopolist 1 Monopolist 2 Monopolist 3 Monopolist 4

$

Distance from Market Centers

Market AreasMarket Areas

Monopolist5

Monopolist1

Monopolist 2

Monopolist3

Monopolist4

Monoplist6

Monopolist7

Monopolist8

Introducing CompetitionIntroducing Competition

Assume monopolists are making pure economic profits

Is this a stable situation?

If not, where would firms enter?

Monopolist5

Monopolist1

Monopolist 2

Monopolist3

Monopolist4

Monoplist6

Monopolist7

Monopolist8

Firm Entry Drives Out Firm Entry Drives Out ProfitsProfits

Monopolist5

Monopolist1

Monopolist 2

Monopolist3

Monopolist4

Monoplist6

Monopolist7

Monopolist8

Firms enter here

Eventual Market ShapeEventual Market Shape All profits driven out Market structure is

monopolist comp. No areas unserved Note: Actual shape

of market is open to debate.

Introduce other markets

Different sized marketsDifferent sized markets

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

Cities simply reflect Cities simply reflect Collections of MarketsCollections 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

How Realistic is this?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?

The Regional IO modelThe Regional IO model The regional IO model is based on an

accounting identity that states: The sum 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!

Overview of How Model Overview of How Model WorksWorks

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.

Step 1: Transactions Step 1: Transactions TableTable

Output Sold ToInputs Manuf. Service Trade Households Exports Gross Supplied by Output------------------------------------------------------------------------------Manuf. 6 4 10 0 20 40Service 5 8 2 25 10 50Trade 0 0 0 30 0 30Local L,K,D 14 33 8 0 0 55Imports 15 5 10 0 --- 30

Total Inputs 40 50 30 55 30

• This gives indication of intersectoral interdependencies.

Step 2: From Transactions Step 2: From Transactions Table to Technical Table to Technical Coefficients 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.

Technical Coefficients Technical Coefficients TableTable Manuf. Service Trade Households

------------------------------------------------------------------------------Manuf. 0.15=6/40 0.08=4/50 0.33=10/30 0.00=0/30Service 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=30/55Local VA 0.35=14/40 0.66=33/50 0.26=8/30 0.00=0/55Imports 0.375=15/40 0.10=5/50 0.33=10/30 0.00=0/55Total Inputs 40 50 30 55

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.

Column Interpretation: How inputs are used in the sector.

Technical Coefficients Technical Coefficients TableTable Manuf. Service Trade Households

------------------------------------------------------------------------------Manuf. 0.15=60/40 0.08=4/50 0.33=10/30 0.00=0/30Service 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=30/55Local VA 0.35=14/40 0.66=33/50 0.26=8/30 0.00=0/55Imports 0.375=15/40 0.10=5/50 0.33=10/30 0.00=0/55Total Inputs 40 50 30 55

Manuf. Demand = 0.15*M+0.08*S+0.33*T+0*Y(income)+XM

XM is known as final or exogenous demand.

Row Interpretation: Who buys output

Step 3: Derive Demand Step 3: Derive Demand EquationsEquations 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.

Question: If we solved for M, S, T Question: If we solved for M, S, T and Y given current values of and Y given current values of

exports, what solution would we exports, what solution would we get?get?

M=40, S=50, T=30 and D=55

Can Derive Local MultipliersCan Derive Local Multipliers(Manipulate so each sector depends (Manipulate so each sector depends only on X)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!

Step 4: Mapping out Step 4: Mapping out influence of influence of

DisturbancesDisturbances 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.

LimitationsLimitations 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?

Limitations - continuedLimitations - 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.

Extensions of this Extensions of this approachapproach 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).

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

Two popular models: Two popular models: IMPLAN and REMIIMPLAN and REMI

Regional Econometric Regional Econometric ModelingModeling

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.

Regional Econometric Regional Econometric Models: OverviewModels: Overview

Roger BoltonJournal of Regional Science,

1985, Vol. 25 (4) pp. 495-519.

Not assigned but on reserve FYI

Very thorough review Very thorough review articlearticle 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.

Exogenous vs. Exogenous vs. Endogenous VariablesEndogenous 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.

Level of AggregationLevel 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.

Single-Region ModelSingle-Region Model

• From regional to From regional to national is called national is called bottom-up structurebottom-up structure

• From national to From national to regional is called regional is called top-down structuretop-down structure

• Link between region Link between region and the rest of world and the rest of world (ROW) is frequently (ROW) is frequently unidirectional.unidirectional.

Regional Model

National Model (ROW)

Bottom up influences (i.e., ) are frequently negligible.

When Bottom-up links are When Bottom-up links are not negligiblenot 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

Multi-Regional ModelsMulti-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.

Multi-regional Models:Multi-regional Models: National components National components

ExogenousNationalVariables

EndogenousNational Vars.(not regional sum)

EndogenousNational(regional sum)

Multi-regional Models:Multi-regional Models: Adding Regional Adding Regional ComponentsComponents




Region 1 Model

Region 2 Model

Region 3 Model

Interregional Feedback Effects

Multi-regional Models:Multi-regional Models: Top-down structure Top-down structure




Region 1 Model

Region 2 Model

Region 3 Model

Multi-regional Models:Multi-regional Models: Bottom-up structure Bottom-up structure




Region 1 Model

Region 2 Model

Region 3 Model

Which is theoretically Which is theoretically preferred?preferred?

Differences between Differences between regional and national regional and national modelsmodels 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.

Other data limitationsOther 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.

Models tend to be SR Models tend to be SR rather than LRrather than LR

Since models can’t deal well with changes in industrial

structure due to investment, they tend to be SR rather

than LR.

PurposesPurposes 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.

Econometric TechniquesEconometric 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.

Advantages and Advantages and DisadvantagesDisadvantages

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.