Coordination costs and location: multiple equilibria with specialization trap

Coordination costs and location: multiple equilibria with specialization trap

Sandrine Noblet, Antoine Belgodere

Preliminary version, june 2012

Abstract

This paper presents a modified version of Venables’ model (1996). Our model differs from the latter in two respects: 1) we introduce endogenous coordination costs (that comes from the number of available varieties of intermediate goods) as additional inter-regional transfer costs, 2) we assume two kinds of intermediate goods are used by downstream firms: standard and complex ones. Only complex ones are subject to coordination cost.

Within this framework, we obtain multiple equilibria associated to the last degree of integration (i.e. when transport cost tends to zero): 1) a symmetric equilibrium and 2) an equilibrium characterised by a agglomeration of downstream firms and upstream firms producing complex intermediate goods in the core region and up- stream firms producing standardised intermediate goods in the peripheral region.

We then investigate conditions under which peripheral regions can avoid to be locked into a “specialization trap”. We argue that it mainly depends on the relative responsiveness of firms to profit differential in both upstream sectors.

Key words:

coordination cost, location of industrial activities, specialization trap

JEL Classification:

F12, F15, R12, O18

1

Assistant professor, Universit´ e de Corse - CNRS UMR LISA 6240. Email:

noblet@univ-corse.fr

2

Assistant professor, Universit´ e de Corse - CNRS UMR LISA 6240. Email:

belgodere@univ-corse.fr

1 Introduction

One of the main features of current globalization process is the fragmenta- tion of production process. As a consequence, trade flows of intermediates goods, or more broadly goods in process, have dramatically increased (Jones and Kierzkowski (2005b)). For instance, between 1990 and 2000 trade of in- termediate goods have experienced a 150% increase whereas GDP has only experienced a 50% increase (Jones et al. (2005)). This global trend applies in Europe since in 1996 trade of intermediate goods accounted for 50% of trade flows between Eastern and Western Europe Freudenberg and Lemoine (1999).

According to Yi (2003), the importance of intermediate goods in trade flows can be explained by the specialization of countries in production stages. This idea seems to be supported in Europe by an early study Fontagn´ e et al. (1995) providing evidences on the shift from horizontal to vertical comparative ad- vantage

.

What are the main drivers of these global changes? Yi (2003) highlighted a magnification effect. He argued that the relationship between changes in trans- port costs and changes in growth trade rate is non-linear. More recently, Bald- win (2006b) analysed the globalisation process as “two great unbundlings”.

The first unbundling, triggered by the fall in transport costs, weakens the need of proximity between final goods producers and consumers whereas the sec- ond unbundling, triggered by the fall in communication technology, weakens the need of proximity between upstream and downstream producers. Robert- Nicoud (2008) studied the implications of these two vectors of globalization with respect to firm’s decision location. He showed that the fall in both trans- port and communications costs results in a specialization of the Northern region in complex tasks and a specialization of the Southern region in routine tasks.

In this vein, the aim of this paper is to study the effect of globalization on the geographical distribution of industrial activities. More specifically, we argue that integration process, or more broadly globalization process, have produced two distinct effects on intermediate goods: some are becoming more special- ized requiring proximity between upstream and downstream firms whereas some others are becoming more standardized and do not require any spatial proximity.

Noblet (2011) showed that taking into account the complexification of pro- duction process through the introduction of coordination costs can impede the

3

Horizontal comparative advantage refers to specialization of a full production

process while vertical comparative advantage refers to specialization of a particular

stage.

redispersion of economic activities into a Venables (1996) framework. Indeed, in such kind of models, the fall in transport costs (in Core-Periphery equilib- rium) increases the complexity of production processes, through an increase in the number of varieties of intermediate goods. This increasing complexity strengthens the need, for downstream firms, to be close to their suppliers. If the coordination costs that arise from this complexity effect are strong enough, the redispersion of industrial activities in the last stage of the integration process does not take place.

The present paper presents an extension of this framework by introducing a differentiation within intermediate goods sector. Indeed, we assume that there are two upstream sectors: one produces standardized intermediate goods while the other produces specialized ones. This dichotomy of intermediate goods is introduced through the presence of inter-regional coordination costs. Inter- regional trade of standardized intermediate goods are only subject to trans- port costs whereas inter-regional trade of specialized ones are subject to both transport and coordination costs. Within this framework, we are able to show that integration process could end up either in a full redispersion of industrial activities or a partial redispersion, characterized by specialization of the core region in the production of both final goods and specialized intermediates and specialization of the periphery in the standardized intermediate goods. The presence of multiple equilibria raises some issue in terms of industrial policy.

The paper is organized as follows. Section 2 describes the theoretical frame- work and characterize long-run equilibrium. Section 3 presents the different spatial configuration prevailing on the long run equilibrium. Section 4 discuses our results with respect to previous literature. Finally Section 5 concludes.

2 Theoritical framework

This section describes an extended version of the Venables framework. Our

model differs from the latter in two respects: 1) we introduce endogenous co-

ordination costs (coming from the increasing number of available varieties of

intermediate goods) as additional inter-regional transfer costs, and 2) we as-

sume two kinds of intermediate goods are used by downstream firms: standard

and complex ones. Only complex ones are subject to coordination cost defined

in section the next section.

2.1 Coordination costs in vertically-linked industries

We define coordination cost as an increasing function of varieties of inter- mediate goods (Noblet (2011)). This assumption reflects an increasing level of complexity of production process (Kremer (1993)). Hereafter, we briefly present the formalization of coordination costs. We define an intermediate good as a set of characteristics. In an incomplete contract environment, let z represent the distance between the required or “ideal” characteristics of an intermediate and an actual good. It is a random variable, where its density function (g(z)) is defined as follows: g(z) = γe

. The expected value of z is: E(z) =

. We assume that γ is an inverse function of both complexity (N

) and geographical distance (d

): γ =

I

, where φ (> 0) is a key in- stitutional parameter reflecting the level of contract enforcement. The higher φ, the weaker the contracting environment. Consequently, a low value of γ (high expected value of z) is evaluated in terms of coordination costs defined by: c

= p

× z. The expected value of the ad-valorem inter-regional transfer costs, encompassing both transport (denoted τ , with τ > 1) and coordination costs (φdN

), is given by:

t

≡ τ + φdN

(1)

These coordination costs are introduced in a extended framework of Venables (1996). Then, we assume that this transfer costs is a source of differentiation of intermediate goods. Indeed, globalization has led to a much deeper level of fragmentation. Then stages of production are not only vertically linked but also horizontally linked (Jones and Kierzkowski (2001)). To illustrate this point, let us rephrase the example used by Jones and Kierzkowski (2001), the computer chip has originally produced by, and for, the computer sector.

However, it is now widely used in an important number of sectors from autos to toasters. This computer chip story illustrates well the way some intermediates have become standardized while some others have become more specialized. As a consequence we assume that coordination matters are an increasing function of the degree of specialization. Indeed, simple intermediates are assumed to be standardized so that their use do not require any particularities whereas complex intermediates are assumed to be more specialized so that their use require adjustment and difficulties along the assembly process. For the sake of simplicity, we assume that inter-regional trade of “simple” intermediate goods incurs only transport costs, denoted t

(where t

= τ) whereas inter-regional trade of “complex” intermediate goods incurs both transport and coordination costs, denoted t

, where t

is the sum of transport costs (τ ) and coordination

4

Since this coordination will be introduced in two region setting, we set

= 0 when

intermediates are bought locally and

= 1 when they bought from the distant

region. In other words, it is assumed that complexity matters only with distant

suppliers.

costs (φN

), and N

represents the number of different available varieties of intermediate goods.

Apart from this differentiation of intermediate goods, the model shares the same structure than the Venables’ one. There are two regions ({1; 2}), la- belled k and k

, initially identical. There are four different sectors potentially active in each region. A traditional sector, characterized by perfect competi- tion, producing a homogeneous good under decreasing returns to scale, and three industrial with a full input-output structure, characterized by imperfect competition ` a la Dixit-Stiglitz, producing horizontally differentiated goods under increasing returns to scale. The downstream sector uses two different types of intermediate goods: simple and complex ones.

The following subsections describe the agent’s behaviour on the long run equi- librium.

2.2 Consumer’s behaviour

The utility of a representative consumer located in k depends on the con- sumption of an agricultural good (X

) and of an aggregate of industrial good (X

). This aggregate is represented by the following Cobb-Douglas function:

U

= X

X

, ∀ k ∈ {1, 2}, where β (∈ [0; 1]) and 1 − β account respec- tively for the share of income spent on industrial and agricultural goods. The aggregate X

is defined by a CES function:

x

(x

) denotes the quantity of final good j produced in k (k

) and sup- plied in k (k). The parameter σ (> 1) is the elasticity of substitution between two varieties.

The maximization program for the consumer is solved in two stages. Let Y

be the consumer’s income and p

and P

respectively be the prices of the agricultural good (X

) and of the industrial aggregate good (X

). Budget constraint is given by: Y

= p

X

+ P

X

. First, she determines the optimal level of X

and X

for a given budget constraint. Solving this program ( max U

s.t. Y

− p

X

− P

X

= 0) gives the following demand functions:

X

=

A

and X

=

F k

Second, for a given a level of E

(= βY

), the consumer determines the

5

We could assume instead that coordination costs are not only function of

but on the total number of intermediate goods available (N

par

=

+

).

However, this change in the assumption does not lead to major qualitative changes

in the model.

optimal composition of X

. The maximization program writes:

max X

s.t. E

−

j=1

p

x

−

n_{F k}0

P

j=1

τ p

x

= 0

Solving this maximization program leads to the following demand function for any variety j of final good produced in k (k

) and purchased in k :

x

= E

P

p

x

= E

P

(τ p

)

, ∀ k ∈ {1, 2} (2) The price index of this aggregate is:

P

=





nF k

X

j=1

(p

)

+

n_{F k}0

X

j=1

(p

t

)





1 1−σ

, ∀ k ∈ {1, 2} (3)

where t

is the transport costs incurred by inter-regional trade of final goods, n

(n

) represents the number of varieties of final goods produced in region k (k

)and p

the price of an individual variety j of final goods (defined by equation (16)).

2.3 Firm’s behavior

2.3.1 Agricultural sector

Agricultural firms produce a freely tradeable homogeneous good X

, under perfect competition which is set as the numeraire so that p

= 1, ∀ k ∈ {1, 2}.

The production function is strictly concave : X

= aL

, ∀ k ∈ {1, 2}. With α ∈ ]0, 1[ and a > 0. Labour L

is the only input. The basic profit maxi- mization program of a representative firm in this sector gives the equilibrium amount of labour in agricultural sector:

L

=

w

αa

_α−1¹

, ∀ k ∈ {1, 2} (4)

The equilibrium profit writes:

π

= a

(1 − α)

1 α

_α−1^α

w

α α−1

k

> 0, ∀ k ∈ {1, 2} (5)

Following Venables (1996), it is assumed that agricultural profits are divided

equally among consumers.

2.3.2 Downstream sector

Production of final goods requires fixed (f ) and variable (x

) quantity of inputs: labour and intermediate goods. Spending on complex intermediates (X

) account for a share µ of production costs (X

) while spending on simple intermediates (X

) account for a share ν, the remaining share of production costs (1 − µ − ν) account for labour costs. The production function of any representative downstream firm located in k is :

AL

X

X

= f + x

, ∀k ∈ {1, 2}

Aggregate of simple (X

) and complex (X

) intermediate goods are respec- tively defined by: X

=

_nICk P

i=1

x

σ−1 σ

ikk

+

n_ICk0

P

i=1

x

σ−1 σ

ik⁰k

_σ−1^σ

and X

=

_nISk P

s=1

x

σ−1 σ

skk

+

n_ISk0

P

s=1

x

σ−1 σ

sk⁰k

_σ−1^σ

,

∀ k ∈ {1, 2}.

Where x

(x

) and x

(x

) are respectively the quantity of simple and complex intermediates produced in k (k

) and supplied in k. The minimization cost program for the downstream firm is solved in two stages. First, each firm chooses L

, X

and X

which minimizes the production cost (C

) sub- ject to the production technology. Demand function of labour (L

), complex intermediates (X

) and simple ones (X

) derived from the minimization cost program of a representative downstream firm located in k write

:

L

= (f + x

) (1 − µ − ν) w

P

P

, ∀ k ∈ {1, 2} (6)

X

= (f + x

) µw

P

P

, ∀ k ∈ {1, 2} (7)

X

= (f + x

) νw

P

P

, ∀ k ∈ {1, 2} (8) The cost function of a downstream firm is then: C

= (f + x

) w

P

P

,

∀ k ∈ {1, 2}. The marginal cost c

is then equal to:

c

= w

P

P

, ∀ k ∈ {1, 2} (9)

Price index of both complex and simple intermediates, in region k, respectively denoted P

et P

are defined by:

6

In order to obtain these simplified form, we set:

= (1

)

,

where

is a scale parameter.

P

=

"_nICk X

i=1

(p

)

+

n_ICk0

X

i=1

(p

t

)

#_1−σ¹

, ∀ k ∈ {1, 2} (10)

P

=

"_nISk X

s=1

(p

)

+

n_ISk0

X

s=1

(p

t

)

#_1−σ¹

, ∀ k ∈ {1, 2} (11)

Once demands of aggregate intermediates are chosen, firms have to determine their demand of any particular variety of both types of intermediates. The corresponding cost minimization program is:

min C

=

n_ICk

P

i=1

p

x

+

n_ICk0

P

i=1

t

p

x

s.t. X

−

_nICk P

i=1

x

σ−1 σ

ik

−

i=1

x

σ−1 σ

ik⁰k

_σ−1^σ

= 0

Solving the program yields to the following demand of variety i of complex intermediate goods:

x

= E

P

p

, ∀ k ∈ {1, 2} (12)

x

= E

P

(p

t

)

, ∀ k ∈ {1, 2} (13) Demand of variety s of simple intermediate goods are derived from the same minimization cost program and takes the following form:

x

= E

P

p

, ∀ k ∈ {1, 2} (14)

x

= E

P

(p

t

)

, ∀ k ∈ {1, 2} (15) Solving the profit maximization program gives the price of any variety j of final good produced in k:

p

= σ

σ − 1 c

, ∀ k ∈ {1, 2} (16) where c

represents the marginal production cost defined by the equation (9).

In a long-run equilibrium the following no-profit condition applies and gives

the total quantity of final goods produced in region k and sold in k (x

) and

k

(x

):

x

= x

+ x

= f (σ − 1) , ∀ k ∈ {1, 2} (17)

2.3.3 Upstream sectors

Different varieties of complex (simple) intermediate goods are denoted i (s)

∀i ∈ {1, n

} ( ∀s ∈ {1, n

}). Their production required fixed and variable quantity of labor denoted L

(L

) defined as follow:

L

= f + x

(18)

(L

= f + x

) (19)

Since labour is the only input, the cost function writes: C

= w

L

(C

= w

L

). The marginal cost of any i (s) variety of complex (sim- ple) intermediate is simply: c

= w

(c

= w

). The price of any variety i of complex intermediate good and the quantity supplied in region k, de- rived from the no-profit condition write respectively: p

=

c

(p

=

σ σ−1

c

), ∀ k ∈ {1, 2}.

The quantity of complex (simple) intermediate goods produced in k and sold in k and k

, derived from the no-profit condition is:

x

= x

+ x

= f (σ − 1)

(x

= x

+ x

= f (σ − 1)), ∀ k ∈ {1, 2}

2.3.4 Clearing conditions

Resource constraint in the labour market is given by:

L

= L

+ n

L

+ n

L

+ n

L

, ∀ k ∈ {1, 2} (20) The first clearing condition is associated with the final goods market. At this point, total expenditure on final goods addressed to a downstream firm is defined as X

P

which is equal to the share β of the consumer’s income Y

. Consumers draw income from wages (w

) and from profits of the agricultural sector, which are equally divided among consumers. Therefore, total spending

7

Note that, in a long-term equilibrium,

=

+w

+n

+n

is equal

to

+

, since the no-profit condition holds (

= 0 and

= 0 ).

on final goods in region k can be written as:

E

= β (π

+ w

L

) , ∀ k ∈ {1, 2} (21) Turning to the market for intermediate goods, total expenditure on interme- diate complex (simple) goods E

(E

) is defined by X

P

(X

P

), which is a cost function of the downstream firm C

(C

). More specifi- cally, a share µ (ν) of production cost is spent on complex (simple) intermedi- ate goods E

(E

) and, since the no-profit condition holds in a long-term equilibrium, E

(E

) can be written as:

E

= n

µ (f + x

) = n

µp

, ∀ k ∈ {1, 2} (22)

E

= n

ν (f + x

) = n

νp

(23)

3 Spatial configuration of equilibrium

In this section, we study three different spatial configurations: core-periphery, partial core-periphery and full redispersion. Since we are interested in the spatial configuration prevailing at the last stage of the integration process, we focus the analysis from the intermediate level of transport costs to low one. In other words, we study what happens once the full core-periphery configuration has occurred for intermediate values of transport costs, focusing on the case where τ → 1.

3.1 CP configuration

The sustainability of the core-periphery equilibrium is studied for a range of transport costs in order to check if it appears to be a stable equilibrium for intermediate values of transport costs and for 0 < φ < 0.015. Simulations are performed following Puga (1999)’s methodology. We arbitrarily assume that the agglomeration takes place in region 1 so that π

= 0, π

= 0 and π

= 0. This configuration is sustainable (unsustainable) if a deviant firm faces positive (negative) profits by moving toward the peripheral region.

To answer this question, we performed simulations for the same set of pa-

rameters than the one used by Venables (1996). This spatial configuration is

sustainable when τ = 1.2 for the all values of φ ∈ [0; 0.015]. Indeed, as we can

see on the figure 1, for these parameter values the profit in region 2 is always

negative. Hence firms, whatever the sector they belong, have no incentives to

move from the core region.

-0,03 -0,02 -0,01 0 0,01

0 0,005 0,01 0,015 0,02

potential profits in region 2 

final goods complex intermediate goods standard intermediate goods

Fig. 1. Full core-periphery equilibrium (τ = 1,2)

Simulations are now performed when τ = 1. Noticed that, as soon as φ > 0, distance still matters for inter-regional trade of complex intermediate goods.

Figure 2 depicts the results which are mixed. As we can see, simple inter- mediate goods firms face positive profits in region 2 for the whole range of φ parameters. This result is easily explained by the fact that their produc- tion is not subject to coordination costs. Hence locating in the peripheral region allows them to face lower wages than in the core region. However, firms belonging to the two other industrial sectors display different profit trends.

Their profits decrease with φ. Indeed, within this framework the firm location decision results from a trade-off between lower wages and lower coordination costs. Then, incentives to move to the peripheral region decrease for increasing value of φ. Moreover, figure 2 shows that upstream firms producing complex intermediates are the most sensitive to coordination costs since their profits become negative for a lower of φ than downstream firms. As a consequence the core-periphery configuration is unsustainable when τ = 1.

Since upstream firms producing simple intermediates have always the incen-

tive to move toward the periphery, we will test the sustainability of a new

spatial configuration characterised by the agglomeration of upstream firms

and downstream firms producing complex goods in the core region and the

concentration of the other industrial sector in the periphery. This configura-

tion is called partial core-periphery configuration as opposed to the usual full

core-periphery structure. These tests are presented in the next section.

-0,015 -0,01 -0,005 0 0,005 0,01 0,015

0 0,005 0,01 0,015

potential profits in region 2



final goods complex intermediate goods standard intermediate goods

Fig. 2. Full core-periphery equilibrium (τ = 1)

3.2 Partial CP configuration

Following the same methodology as above, we assume that complex upstream firms and downstream firms are entirely located in region 1 while simple up- stream firms are entirely located in region 2. We then study the incentives of firms to move out of their region. The evolution of profits faced by a deviant firm with respect to φ when τ = 1 are represented by figure 3.

We observe that simple upstream firms have no incentives to move whatever the value of φ since their location decision are only driven by input cost mini- mization (ie. labour) which are minimized when there is no other sector active in the same region. Concerning the two other industrial sectors, they do not have incentive to move for high value of φ. Indeed, when φ is high enough, coordination matters offset the wage benefit offered by the peripheral region.

On the other hand, when φ is low since coordination costs decrease, lower wages in the peripheral region offset coordination matters. In this case, firms have greater incentives in locating in the peripheral region than in staying in the core region.

However, the sustainability of this configuration does not guarantee that it is

the only one prevailing during the later stage of integration. We study now

the analysis of symmetric equilibrium.

-0,015 -0,005 0,005

0 0,005 0,01 0,015

potential profits in the other region



final goods complex intermediate goods standard intermediate goods

Fig. 3. Partial core-periphery equilibrium (τ = 1) 3.3 Symmetric configuration

When φ → 0 our model become very similar to the Venables’ one. So we can expect that the symmetric equilibrium is stable for low values of φ and τ. The stability of this configuration is studied by setting nil profits. We introduce a small asymmetry between regions so that the share of firms in region 1 is 0.5 + , where is a small perturbation, and observe the sign of profits in region 2 for all sectors. If they are positive then firms have the incentives to go back to the region 2 which makes the symmetric equilibrium stable. If they are negative, firms in region 2 will have incentives to go to region 1. In this case, the symmetric equilibrium is not stable.

Numerical results are shown in figure 4. As expected, symmetric equilibrium is stable for a range of low φ values (for φ ∈ [0; 0.0072]). We notice that for low values of φ, symmetric equilibrium is the unique stable equilibrium.

To sum up, for low values of transport costs (τ → 1), the full core-periphery equilibrium is unsustainable whatever the value of φ. When φ is low (φ ∈ [0; 0.0072]) the symmetric equilibrium is stable. Finally, when φ is high enough (φ > 0.0065) the partial core-periphery equilibrium is sustainable. The inter- esting result is that there exists a range of φ values where the equilibrium prevailing when τ = 1 is not unique. Indeed, for a range of intermediate val- ues of φ (φ ∈ [0, 0065; 0, 0072]) there are multiple equilibria (cf. figure 5).

The presence of multiple equilibria means that for a same value of parameters

-2,0E-06 -1,0E-06 0,0E+00 1,0E-06 2,0E-06

0 0,005 0,01 0,015

potential profits in the other region

 final goods complex intermediate goods standard intermediate goods

Fig. 4. Symmetric equilibrium (τ = 1)

ϕ

0 0,0065 0,0072

Stability of symmetric equilibrium

Sustainability of partial redispersion

Multiple equilibria

Fig. 5. Multiple equilibria (τ = 1)

the integration process could result in inter-regional convergence (symmetric equilibrium), as in the Venables’ framework, or in specialization (partial core- periphery equilibrium) with a core region producing complex intermediates and final goods and a periphery producing simple intermediates and agricul- tural goods. We propose two different scenario in order to explain conditions under which one equilibrium is more likely to be reached than the other.

In the first scenario, starting from the core-periphery equilibrium prevailing

for intermediate values of transport costs, a fall in transport costs make this

spatial configuration unsustainable. Simple upstream firms find it profitable to

move toward the periphery since their need of proximity with the two others

industrial sectors is weaker than the one between the two other sectors. This positive profit differential leads to a massive relocation of the simple upstream sector which raises wages in the periphery reducing the inter-regional wage differential. Then, wages in periphery are no longer low enough to offset the need of proximity between complex upstream firms and downstream ones as their need of proximity is still important.

In the second scenario, starting from the core-periphery equilibrium prevailing for intermediate values of transport costs, a fall in transport costs decreases the need of proximity between complex upstream firms and downstream ones, making the periphery an attractive region associated to lower wages. If the migration of simple upstream firms is slow, then firms belonging to complex upstream sector will also have incentives to move toward the periphery as long as wage benefit offset coordination costs. In this case, the symmetric config- uration emerges. In other words, whether the economy reaches the partial core-periphery equilibrium or the symmetric one depends on the adjustment speed of simple upstream sector. If the adjustment is fast, the massive relo- cation of firms belonging to this sector raises wages in region 2 deterring the location of others industrial sectors.

Since welfare is likely to be higher, in region 2, in the full symmetric equilib- rium, we call specialization trap the partial redispersion equilibrium

.

4 Discussion

Our model echoes several previous contributions within the literature. The first one is the model developed by Robert-Nicoud (2008) who investigated the effect of the “two unbundling” on the distribution of industrial activi- ties. He considers that industrial activities require routine and complex tasks.

While the location of routine tasks is affected by transport costs, the location of complex tasks is affected by communication cost. A parallel can be made between the tasks dichotomy used by Robert-Nicoud (2008). In this setting he showed that the fall in both transport and communication costs lead to regional specialization with the Northern region specialized in complex tasks and the Southern in routine tasks. Our model and results are similar to the latter with respect to: 1) the differentiation of tasks (standard vs complex intermediates), 2) the specialization pattern of the core and peripheral region.

However, our original result is the presence of multiple equilibria. Indeed, while Robert-Nicoud (2008) shows that the fall in transport costs and communica- tions costs alone lead to different spatial configurations, we show that for a given value of φ, the fall in transport costs increases coordination costs (via

8

Welfare analysis will be included in further version of the paper.

an increase in the number of intermediate goods varieties) can lead to two different spatial configurations.

On the other hand, a new question emerges from the presence of multiple equi- libria: what kind of industrial policy can be implemented in order to favour an economy to catch the “best equilibrium”? This question echoes an other strand of literature dealing with multiple equilibria: development economics.

One of the early theories explaining the catching up of an economy from spe- cialization in traditional industries (decreasing return to scale industries) to a specialization in increasing return to scale industries is the so-called big-push theory

. The basic idea, formalized by Murphy et al. (1989) is that increas- ing return to scale technology has to be adopted simultaneously by different sectors in order to create demand linkages locally making all sector profitable.

The big push argument has been extensively emphasized within the develop- ment economics literature. Among them, we notice the contribution of Rodrik (1996). He proposes two different policies in order to allow an economy to get out of specialization trap (ie. to get stuck into the low-tech specialization as opposed to the high-tech specialization). The first one is to attract foreign investment by proposing higher returns than the international average return.

These investment is supposed to raise productivity in the traditional industry (constant return to scale), which increases wages. Once wages are high enough, it becomes more profitable for firms to adopt high-tech technology associated to higher returns. However, the high-tech technology differs from the tradi- tional one in having an input-output structure. Then, Rodrik (1996) pointed out the fact that the adoption of high-tech technology requires a coordination of private agents since both upstream and downstream firms have to be settled simultaneously since he assumes that intermediates goods are not tradeable

. The second policy proposed by Rodrik is to raise wages in order to make high- tech industry attractive for firms through the mechanism describes above. Our explanation on the responsiveness of firms to inter-regional profit differential shares some similarities with the theory of big push. Indeed, the emergence of symmetric configuration rather than partial redispersion depends on whether the relocation of firms is simultaneous across sectors. However, we can notice that in Rodrik an increase in wage enhances the transition from low-tech to high-tech whereas in our model an increase in wage can halt the redispersion process, leading to partial redispersion of industrial activities.

9

His fatherhood is commonly assigned to Rosenstein-Rodan (1943).

10

This assumption is justified by their high level of specificity which prevents their

trade.

5 Concluding remarks

The aim of this paper is to highlight that standardization and complexification of production stages are the two sides of the same phenomenon: globalization process. The integration of both sides in a NEG framework allow us to high- light that integration process does not necessarily lead to a core-periphery or symmetric equilibrium. Indeed, we show that integration process could end up in a partial redispersion process or in a symmetric equilibrium. We argue that the type of equilibrium reaches by an economy depends on the firms responsiveness to inter-regional profit differential. (to be completed)

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