Transcript Slide 1

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
 N r
M = ci 
 i=1 
1
r

(1- e )
I =  pi

 i =1

N
• cj = pj -ε Iε-1 E
1
(1- e )
• total spending on M-products in stead of δY is now
E = δY + μ npx
value of all
varieties produced
Supply side
• mark-up pricing in core model was p = βW/ρ
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
with
• Food sector:
– CRS:
– DRS:
F(1-λ) = 1-λ
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 Mworkers
• 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 model
Supply 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 I1ε-1 )
leads to
• W1 = (1- β)/α)1/ε(1-µ) I1-µ/1-µ (E1I1ε-1 + E2 T1-ε I2ε-1)1/ε (1-µ)
• W2 = (1- β)/α)1/ε(1-µ) I2-µ/1-µ (E2I2ε-1 + E1 T1-ε I1ε-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
The VL model with DRS in the food sector
T=1.5
W1/W2
region 2
B
B stable equilibrium
region 1
share of M-workers in region 1
The VL model with DRS in the food sector
T=1.3
W1/W2
region 1
B
B unstable equilibrium
region 2
share of M-workers in region 1
The VL model with DRS in the food sector
T=1.1
W1/W2
region 2
B
B stable equilibrium
region 1
share of M-workers in region 1
The VL model with DRS in the food sector
Fig 4.10 The bell-shaped cirve
1
λ1
0,5
0
Unstable equilibria
T
Stable equilibria
VL model: with lowering T from dispersion to agglomeration to dispersion
Fig 4.3 The Tomahawk diagram
1
λ1
0,5
0
Unstable equilibria
T
Stable equilibria
CP model: no (increasing) spreading force when T becomes low
Next chapter: Helpman(1998) also gets a bell-shaped curve by introducing
the 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
Industry 1
Industry 2
a. Neither specialization, concentration nor agglomeration
b. Specialization (=country concentration), no agglomeration
Country A
Country B
Industry 1
Industry 2
c. regional concentration, specialization, no agglomeration (??)
in terms of
ch 3-4 this
was called agglomeration
d. Concentration and agglomeration, no specialization
here
agglomeration
is about more industries
Country A
Country B
Industry 1
Industry 2
d. Concentration and agglomeration, no specialization
e. Concentration, agglomeration and specialization
Country A
Country B
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
EU 1995-2001
Nuts2 1995
1
Gini coefficient: 0.1561
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
EU 1995-2001
Nuts2 2001
1
Gini coefficient: 0.1539
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
EU 1995-2001
Nuts3 1995
1.0
Gini coefficient: 0.2109
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.0
0.1
0.2
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0.4
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0.6
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0.8
0.9
1.0
EU 1995-2001
Nuts 3 2001
1.0
Gini coefficient: 0.2118
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.0
0.1
0.2
0.3
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0.6
0.7
0.8
0.9
1.0
Figure 5.2 Krugman specialization index
Ireland
Greece
Finland
Denmark
Portugal
Netherlands
Sweden
1994-1997
Average
1980-1983
1970-1973
Belgium
Italy
Germany
Austria
Spain
UK
France
0
0,2
0,4
0,6
0,8
1980-2000:
Increasing
specialization
Figure 5.3 Agglomeration of manufacturing in the EU*
Germany
France
Italy
UK
Moderate
changes
Spain
Netherlands
Belgium
1994-1997
Sweden
1970-1973
Austria
Finland
Denmark
Portugal
Ireland
Greece
0
10
20
30
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.
2.
3.
4.
5.
The home market effect: large home market leads to
net exporters
Large market potential raises local factor prices
Large market potential induces factor inflows (Chapter
9)
Shock sensitivity
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
SHARE
1.416
(0.025)
yes
0.888
(0.070)
no
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
Problems
END is in fact endogenous according to GE theory
Home market effect <-> lack of labor supply elasticity -> higher wages in agglomerations
2) Spatial wage structure
• 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:
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-κ
2
Dij)
+ erri
(5.4)
Table 5.3 EU regions 1992-2000
Coefficient
Standard error
k1
0.898
0.020
k2
0.013
0.001
R2
0.61
remember Wr
= ( Σs Ys Trs1-ε Isε-1 )1/ε
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