Today

• Chapter 4 extensions 4.6 - 4.8

• Chapter 5

Krugman & Venables (1995-1996)

• intermediate inputs• labor mobile between sectors but not between

regions• firms use M-products (µ) and M-labor (1-µ)• also known as the Vertical Linkages (VL) model• base model of Chapter 3 is usually named the

CP model (Core-Periphery)

The VL model in writing• U = F1-δ Mδ

• cj = pj -ε Iε-1 E

• total spending on M-products in stead of δY is now

E = δY + μ npx

1

1

N

iicM

11

1

1N

iipI

value of allvarieties produced

Supply side

• mark-up pricing in core model was p = βW/ρ with normalization β = ρ -> p = W

• now becomes p = Iµ W(1-µ) • zero profit condition px = Iµ W(1-µ) ( α + βx)• x = α(ε-1)/β = αε

• Food sector:– CRS: F(1-λ) = 1-λ – DRS: F'(1-λ) >0 ; F''(1-λ) < 0

• Consumer income = (M) wage income + output food sector: Y = Wλ + F(1-λ)

Intersector mobility

• price index is the same for F workers and M-workers

• for mobility between sectors only the nominal wage matters

• dλ/λ = η [W - F'(1-λ) ] (4.14)• same as in core model: equal demand and

supply leads to

Regional wages in VL modelSupply x1 = demand in region 1 + demand in region 2

+ extra production melted away

• α (ε -1)/β = (E1 p1 -ε

I1ε-1

+ E2 p1 -ε

T 1-ε I1

ε-1 )

leads to

• W1 = (1- β)/α)1/ε(1-µ) I1-µ/1-µ (E1I1

ε-1 + E2 T1-ε I2

ε-1)1/ε (1-µ)

• W2 = (1- β)/α)1/ε(1-µ) I2-µ/1-µ (E2I2

ε-1 + E1 T1-ε I1

ε-1)1/ε (1-µ)

• simplifies to core model when µ = 0

• main differences: – E in stead of Y

– Extra term I-µ/1-µ : supplier access effect : closer to suppliers lowers price index and can give higher nominal wages.

The four forces in the LV model

• extent of competition effect: a higher λ lowers the price index of all other products (-)

• market size or home market effect: a higher λ increases the market (+)

• (new) access supplier effect: a higher λ increases nominal wages (+)

• (new) marginal productivity effect in food sector (-)– only with DRS: a higher λ increases food wages

region 2

region 1

share of M-workers in region 1

W1/W2

T=1.5

B stable equilibrium

B

The VL model with DRS in the food sector

region 1

region 2

share of M-workers in region 1

W1/W2

T=1.3

B unstable equilibrium

B

The VL model with DRS in the food sector

region 2

region 1

share of M-workers in region 1

W1/W2

T=1.1

B stable equilibrium

B

The VL model with DRS in the food sector

T0

1

0,5

λ1

Fig 4.10 The bell-shaped cirve

Unstable equilibria

Stable equilibria

The VL model with DRS in the food sector

VL model: with lowering T from dispersion to agglomeration to dispersion

T0

1

0,5

λ1

Fig 4.3 The Tomahawk diagram

Unstable equilibria

Stable equilibria

CP model: no (increasing) spreading force when T becomes lowNext chapter: Helpman(1998) also gets a bell-shaped curve by introducingthe housing market as a spreading force

The generalized model

• Puga (1999) not discussed here in detail

• CP model plus µ (intermediate production) ηs (intersector migration) and ηr (interregional migration)

• Only the model with ηr =0 (no interregional migration) gives the bell-shape curve

The Footloose Entrepreneur (FE) model

• two labor production factors in stead of one:• skilled/unskilled ; human capital/labour ; R&D/production;

headquarters/plants• Skilled labor is mobile, unskilled labor immobile• In production function:

– α: skilled labor as fixed costs– β: unskilled labor as variable costs

• makes the model solvable because the mobile skilled labor demand is not a function of x

• equation (4.25) for skilled labor wage rate r1/r2

• discussion on the FE model will come back later

Chapter 5

Agglomeration, the home market effect

and spatial wages

Terminology (confusing!)• Concentration:

– industry is concentrated in some regions (xri /xni ) / ( xr /xn ) >1

• Specialization:– region is specialized in some industries (xri /xr ) / ( xin /xn ) > 1

– (xri /xr ) / ( xin /xn ) also know as the location coeffcient

• is the same : (xri /xni ) / ( xr /xn ) = (xri /xr ) / ( xni /xn )

Concentration=Specialization

• The distinction between concentration and specialization is not relevant for one spatial level. It is only done to be consistent with trade theory terminology:– specialization=concentration at the country level

• Agglomeration:– concentration of more than one industry

Wrong terminology:

• There is more car production in Germany than in The Netherlands or:

concentration -> Eir / Ein ≠ Eis / Ein

• concentration is relative not absolute

No concentration, no agglomeration

Concentration, no agglomeration

No concentration, agglomeration

concentration, agglomeration

Country A Country B

a. Neither specialization, concentration nor agglomeration

Industry 1 Industry 2

b. Specialization (=country concentration), no agglomeration

c. regional concentration, specialization, no agglomeration (??)

d. Concentration and agglomeration, no specialization

Country A Country B

Industry 1 Industry 2

in terms ofch 3-4 thiswas called agglomeration

hereagglomerationis about more industries

e. Concentration, agglomeration and specialization

Country A Country B

Industry 1 Industry 2

d. Concentration and agglomeration, no specialization

Concentration manufacturing

<1 Below national average

> 1 Above national average

(Eir / Er)/( Ein / En )

Absolute size manufacturing

Eir

Concentration bussiness services

< 1 Below national average

> 1Above national average

(Eir / Er)/( Ein / En )

Absolute size bussiness services

Eir

Convergence

• Increase/decline of gdp/cap differences

• Barro & Sala-i-Martin and others:

• Global no;within EU yes,but

• Results for EU:– 1980-1990 convergence– Later: divergence– Depends on level of region disaggregation

Nuts2 1995

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Gini coefficient: 0.1561

EU 1995-2001

Nuts2 2001

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Gini coefficient: 0.1539

EU 1995-2001

Nuts3 1995

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Gini coefficient: 0.2109

EU 1995-2001

Nuts 3 2001

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Gini coefficient: 0.2118

EU 1995-2001

Figure 5.2 Krugman specialization index

0 0,2 0,4 0,6 0,8

France

UK

Spain

Austria

Germany

Italy

Belgium

Average

Sweden

Netherlands

Portugal

Denmark

Finland

Greece

Ireland

1994-1997

1980-1983

1970-1973

1980-2000:Increasingspecialization

Figure 5.3 Agglomeration of manufacturing in the EU*

0 10 20 30

Greece

Ireland

Portugal

Denmark

Finland

Austria

Sweden

Belgium

Netherlands

Spain

UK

Italy

France

Germany

1994-1997

1970-1973

Moderate changes

G. Ellison & E. L. Gleaser (1997)/(1999)

• Concentration is the rule, not the exception• Geography accounts for 20% of economic concentration• Concentration itself does not imply the existence of spill-overs• Natural advantages (first nature) may have similar effects

• -> no real support for GE

D. Black & J. Vernon Henderson (1999) ‘Spatial Evolution of Population and Industry in the United States’, American Economic Review Vol. 89, No. 2, May 1999, pp321-327

• evolution US urban growth 1900-1990

• Scale economies and agglomeration

• distribution remains remarkably stable• big cities stay big• little downward mobility• more upward mobility

“Geography matters?”

• Market potential

• mpj = ∑ i ≠ j ( Ni /dij)

Five hypotheses to be tested

1. The home market effect: large home market leads to net exporters

2. Large market potential raises local factor prices

3. Large market potential induces factor inflows (Chapter 9)

4. Shock sensitivity

5. Reductions in trade costs induce agglomeration

1. Home market effect

• an increase in a country's demand for cars will lead to a more than proportional increase of the production of cars

• if yes: support for new trade theory with transport costs and geographical economics

• if no: support for new trade theory without transport costs or neoclassical theory

Davis & Weinstein (1996-2003)

• Distinguish between trade theory and geographical economics• Measuring the home-market effect

• Xgnr = κgnr + κ1SHAREgnr + κ2IDIODEMgnr + END + errgnr

• SHARE = share of output good g in industy n for country r

• IDIODEM = difference between demand gn in r and demand gn in other countries

• END = endowments for gn + (neo-classical theory)

• if κ2 >1 home market effect (geographical economics)

• IDIODEM no geographical content (no distance)• Test on Japanese regions

Table 5.1 Home market effect for Japanese regions

IDIODEM 1.416 (0.025)

0.888 (0.070)

SHARE 1.033 (0.007)

-1.7441 (0.211)

END included? No Yes

# Observations 760 760

Source: Davis and Weinstein (1999); Standard errors between brackets, estimation method: Seemingly Unrelated Regressions

ProblemsEND is in fact endogenous according to GE theoryHome market effect <-> lack of labor supply elasticity -> higher wages in agglomerations

yes no

• Neoclassical trade theory: factor price equalization

-> no spatial wage structure• New trade theory: some varieties produced in country A

and others in country B, no endogeous agglomeration towards A or B

-> no spatial wage structure

(unless A and B are different in size from the start)

2) Spatial wage structure

2) Spatial wage structure:distance to centres

• Hanson (1998) study on Mexico• Hypothesis 1: regional wages lower at higher

distances from Mexico City and USA• Hypothesis 2: trade liberalization has lead to a decline

of regional wage differences• finds strong support for H1 and weak support for H2• H1: (H2 with time dummy)

ln (Wit /Wct ) = k0 + k1 ln(tit ) + k2 ln(tfit ) + errit (5.2)

(k1 and k2 negative)

remember Wr = ( Σs Ys Trs1-ε

Isε-1 )1/ε

2) Spatial wage structure:market potential

• Log (Wj) = κ0 + κ1 log(Σk Yk e-κ2 Dij) + erri (5.4)

Table 5.3 EU regions 1992-2000

remember Wr = ( Σs Ys Trs1-ε

Isε-1 )1/ε

Coefficient Standard error

k1 0.898 0.020

k2 0.013 0.001

R2 0.61

2) Spatial wage structure:real market potential

• Hanson (1996)

• Log (Wj) = κ0 + ε-1log(Σk Yk ε+(1- ε)/δ Hk

(1-δ)(ε-1)/δ

Wk(ε-1)/δ T(1-ε)Djk) + errj (5.5)

• assumption: agriculture replaced by the housing market as a spreading force of non-tradables. If local demand increases due to agglomeration prices will go up -> additional spreading force

Structural wage

equation: (5.5) 1970-80 1980-90

δ 0.962 (0.015) 0.956 (0.013)

ε 7.597 (1.250) 6.562 (0.838)

Log(T) 1.970 (0.328) 3.219 (0.416)

Adjusted R2 0.256 0.347

Observations 3075 3075

ε/(ε-1) 1.152 1.180

ρ 0.868 0.847

Significant, but δ very high