Integration and Transition:
Scenarios for Location of Production and Trade in Europe*
Rikard Forslid
University of Stockholm and CEPR
Jan I. Haaland
Norwegian School of Economics and Business Administration and CEPR
Karen Helene M. Knarvik
Norwegian School of Economics and Business Administration and CEPR
Ottar Mæstad
Foundation for Research in Economics and Business Administration
ABSTRACT
Applying a newly developed CGE-model, we present scenarios for the future
economic geography of Europe. The model divides the world into ten regions, of
which five are European, and there are 14 industries, of which 12 are imperfectly
competitive. With a complete input-output structure, the model captures comparative
advantage mechanisms as well as intra-industry trade and “new economic geography”
agglomeration forces. The simulations focus on the consequences of successful
transformation in Eastern Europe. The results indicate that transformation and
European integration are of great importance for Eastern Europe, while the overall
effects for other European regions are small. Individual sectors in EU, such as
Textiles and Transport Equipment, are however in some cases strongly affected.
Bergen, March 2001
*
Thanks to Richard Baldwin and Victor D. Norman for very valuable discussions and comments, and
to Joseph Francois for providing data on demand elasticities. We also thank two anonymous referees
for valuable comments. This research has been financed by the Research Council of Norway grants
nos. 40050/230 and 124559/510. The work was carried out while Forslid visited Bergen under a TMR
grant from the European Commission (TMR-programme FDI MC).
1.
Introduction
The economic integration of the Eastern European countries is often identified as one
of the main challenges for the EU. Clearly a successful integration of these states
would mean a major improvement for Europe as a whole not only in economic terms,
but also in terms of enhanced security. A revived Eastern Europe means new export
markets but, of course, also enhanced competition, for instance, in the form of low
wage manufacturing imports to the EU.
The purpose of this paper is to assess, through model simulations, the longterm production and trade effects of an Eastern European transformation. While both
economic integration and Eastern European transformation have been studied before1,
we believe our analysis has something to add. We apply a newly developed model
that incorporates several features that have not been implemented in CGE-models
before, and has a regional structure that allows us to identify effects for various parts
of Europe. In particular, we calibrate the model on actual, region-specific, complete
input-output matrices. By modelling all intra- and inter-industry linkages in a setting
with imperfect competition and trade costs, we are able to capture important
agglomeration forces. Furthermore, we specify several scenarios that differ from
previous studies. In particular, for Eastern European transformation we include
productivity improvements and a lower risk premium as well as the more common
experiment of closer market integration.
The model is based on both traditional trade theory and more recent theory of
international trade and economic geography. In this way it captures comparative
advantage as well as agglomeration effects of structural changes or policy events. Five
1
European integration has been studied in many model-based analyses, e.g. Gasiorek, Smith and
Venables (1991, 1992), Haaland and Norman (1992, 1995), Baldwin, Forslid and Haaland (1996),
Allen, Gasiorek and Smith (1998), Keuschnigg and Kohler (1996). For studies of Eastern European
transformation and eastern enlargement of the EU, see e.g. Baldwin, Francois and Portes (1997) or
1
of the ten regions in the model are European ones. Hence, the model should be
suitable for analysing regional development in Europe – where phenomena like
agglomeration effects and the centre-periphery dimension have been emphasised in
the theoretical and applied literature, but so far not been implemented in a full-scale
general equilibrium model calibrated on actual data.
Eastern Europe has experienced a number of very significant changes since
1989, on both the political and the economic arena. However, we have not yet seen
the long-term effects of the economic reforms. What we have observed so far in most
of the countries, is more of the short-term adjustment problems and less of the longterm possibilities. This paper analyses possible long-run outcomes such as productivity growth and investment.
In all experiments we study stylised characteristics rather than trying to assess
the exact nature or magnitude of the exogenous changes. Hence, the results should be
read as “what – if” type of experiments not as complete scenarios for the future
production and trade patterns of Eastern Europe and its trading partners.2 Moreover,
the absolute magnitudes of the effects obtained in this type of simulations must be
treated with caution. However, in a qualitative sense we expect the results to be much
more robust.
In the next section the model is presented, while section 3 reviews some
important aspects of the benchmark data. Section 4 presents the simulation results,
while conclusions are given in the final section.
Keuschnigg and Kohler (1998).
2
In Forslid et al (1999a) the approach is discussed in more detail.
2
2.
The model
The model has ten regions. In each region there are fourteen production sectors; the
regions and sectors are listed in Table 1 below. There are three primary factors of
production – capital, skilled labour and unskilled labour; these are mobile between
industries within a region, but immobile between regions.3 The supply of the two
types of labour is exogenously given; for capital the supply is endogenously
determined by the condition that the return to capital should equal the steady-state
level, which is calculated in the benchmark case. The factor demand comes from the
fourteen producing sectors. In addition to the three sectorally mobile factors, two of
the sectors – energy and agriculture –use sector-specific natural resources. Hence,
these two sectors show decreasing returns to scale with respect to the mobile factors.
Energy and agriculture are modelled as perfectly competitive and with free trade4.
The remaining twelve sectors are all modelled with increasing returns to scale,
imperfect competition and product differentiation. For the ten manufacturing sectors
in the model, there are trade flows between all regions, but trade costs hamper trade.
The two imperfectly competitive services sectors in the model are assumed to be nontraded. Although trade in services is not negligible in reality, it is clear from the
benchmark data that a very large share of the output from these sectors is sold in
domestic markets.
3
The regional immobility assumption is consistent with the empirical observation that the return to
capital differs widely between countries. See Ménil (1999) for evidence related to the European capital
market.
4
The assumptions of perfect competition and free trade for these two sectors are not realistic ones;
however, since the emphasis of the model is on manufacturing, we have kept the resource-based sectors
as simple as possible. With perfect competition and homogenous products, the model will only determine the net trade position of a region, and that is why we do not include trade policies for these goods.
The model includes production subsidies in agriculture and energy; however, in the scenarios presented
in this paper, these policies are kept unaltered. These policies may still give rise to second best effects
in our model simulations.
3
Table 1: Regions and sectors in the model
Regions
Europe Central (EuropeC)
Austria, Denmark, Germany, Switzerland
Europe North (EuropeN)
Finland, Iceland, Norway, Sweden
Europe South (EuropeS)
Greece, Italy, Portugal, Spain
Europe West (EuropeW)
BeNeLux, Ireland, France, UK
Europe East (Europe E)
Bulgaria, Hungary, Czeck. Rep., Poland,
Romania, Slovakia, Slovenia
Former Soviet Union (FormSov)
Former Soviet Republics including Estonia,
Lithuania, Latvia
China and South Asia (CSA)
China, India, Bangladesh, Bhutan, Maldives,
Nepal, Pakistan, Sri Lanka
South East Asia (SEA)
South East Asia including Japan
USA and Canada (USACAN)
Rest of the World (ROW)
Trade manufactures (imperf. comp., IRS, diff. prod.)
Textiles
Leather
Wood and pulp products
Metals
Minerals
Chemicals
Food products
Transport equipment
Machinery and equipment
Other manufacturing
Non-traded services (imperf. comp., IRS, diff. prod.)
Public services
Private services
Traded resource intensive industries (perf comp., free trade)
Agriculture
Energy goods
One important feature of the model is the input-output linkages between sectors.
There is a complete input-output system, and together with trade costs and imperfect
competition this creates a force for agglomeration through backward and forward
linkages (see e.g. Venables, 1996).5 The data reveals a clear pattern of these linkages:
for most industries inputs from own industry dominate, with inputs from the services
industries as number two. Hence, the non-traded nature of the services industries as
well as the trade costs for manufactures are potential sources of agglomeration in this
model. We calibrate parameters of the cost function using actual region and industry
specific input-output matrices. Most previous models (e.g. Norman and Haaland,
1992) use a common CES-composit of differentiated goods as intermediate input,
which implies that differences at the industry level are not taken into account. This is
a strong assumption when analysing agglomeration forces. For instance, a region with
a large textile industry may not provide strong linkages to firms in the steel industry.
5
In the new economic geography literature two major sources of agglomeration have been emphasized:
Linkages between mobile firms (as in Venables, 1996) and factor mobility (as in Krugman, 1991). Here
4
Thus, the use of region and industry specific input-output data is important as means
of capturing real world agglomeration forces.
Basic model equations
We here display some basic model equations to illustrate how specific features of the
model work; a complete description is found in Forslid et al (1999a) 6.
Consumers in region m have Cobb-Douglas preferences over a set of all
goods (AG), implying that they will spend a fixed share of their income on each good:
C im = α im
Yim
Pim
i ∈ AG
(1.)
For perfectly competitive goods prices are world market prices given by world market
clearing conditions for the respective goods. One of these goods is chosen as
numeraire. As for imperfectly competitive, differentiated goods (the set I), the price
level for good i (Pim) is an index of the prices of each variety of the good sold in
market m (PIijm). The calibrated demand parameter for each of the Nij varieties of
good i from country j sold in market m, is aijm.
1
1−σ i
⎛ R
(1−σ i ) ⎞
⎟
Pim = ⎜⎜ ∑ N ij aijm PI ijm
⎟
⎝ j =1
⎠
i∈I
(2.)
For non-traded, differentiated goods aijm=0 for all m≠j, since by assumption only
domestically produced varieties are consumed. σi is the elasticity of substitution
between various varieties of good i.
we focus on the former of these.
6
The model builds on Haaland and Norman (1992), but with significant differences. The regional setup differs, and so does the input-output structure. Hence the present model is more suitable for
economic geography issues. The model has similarities with a few other models, like e.g. the one
applied in Baldwin et al (1997) but, again, the regional as well as the input-output structure is richer in
the present model. The CGE-model sketched in Gasiorek and Venables (1998) focuses on the effects of
transport improvements on industrial location and real income. Those simulations, however, are based
on made up data.
5
The imperfectly competitive sectors are characterised by monopolistic
competition á la Dixit and Stiglitz (1977) with free entry and zero profits. Producer
prices (PPI) of individual varieties are given as a mark-up over firms’ marginal costs
(MC):
PPI ij =
σi
MCij
σ i −1
i∈I
(3.)
while consumer prices (PIijm) for the traded goods are subject to trade costs, Tijm.7
PI ijm = PPI ij (1 + Tijm )
i ∈ ITG
(4.)
where ITG is the set of imperfectly competitive traded goods. Demand for each
variety of good i in market m may now be derived as:
⎛ P
X ijm = aijm ⎜ im
⎜ PI ijm
⎝
σ
⎞ i
⎟ C im
⎟
⎠
i ∈ ITG
(5.)
Prices and demand for non-traded differentiated goods are derived in the same way as
for traded goods, but with no need to distinguish between producer and consumer
prices since there is only domestic consumption of these goods.
The price index for differentiated intermediate goods (Qhm) is industry
specific by purchasing industry (h) and region (m). The industry uses all goods as
inputs, weighting the aggregate price of each good by the parameter gihm. The
parameter is calibrated from the use of good i as intermediate input in the production
of industry h in country m.
⎛
⎞
Qhm = ⎜⎜ ∑ g ihm Pim(1− sq ) ⎟⎟
⎝ ∀i∈I
⎠
1
1− sq
∀ h ∈ AG
7
(6.)
T includes trade costs of three types: export taxes (EXTAX) levied by the exporting country, transport
costs (TRANS), and tariff equivalents of import barriers (TAREQ) set up by the importing country. The
transport costs are of the iceberg type, while export taxes and import tariffs are transfers (to the
6
where sq is the elasticity of substitution among imperfectly competitive goods used as
intermediates. The price indices for perfectly competitive goods (the set PC) as
intermediates are constructed in the same way, where the market price for perfectly
competitive goods is denoted by PPC.
1
QPC hm
⎛
⎞ 1− sq
= ⎜⎜ ∑ g ihm PPCi(1− sq ) ⎟⎟
⎝ ∀i∈PC
⎠
∀ h ∈ AG
(7.)
PVij is a price aggregate for all primary factors used in the production in sector i in
region j. The use of each individual factor is industry and country specific and given
by the parameter β.
1
⎞ 1− si
⎛
1− s
PVij = ⎜
β ijk W jk i ⎟
⎜
⎟
⎝ k =1
⎠
K
∑
i ∈ AG
(8.)
Finally, the marginal cost for industry i in country j is specified as a nested CESfunction, with primary inputs, differentiated intermediates, and homogenous
intermediates in one second-level nest each, and with Stop as the elasticity of
substitution between the nests at the top level. Using the price indices above, the
marginal cost function can we written
1
1− Stop
1− Stop
1− Stop 1−Stop
MCij = ⎡ BVij ( PVij ) i + BZ ij ( Qij ) i + BZPCij ( QPCij ) i ⎤ i ,
⎣⎢
⎦⎥
(9.)
where BVij, BZij, and BZPCij are all calibrated parameters. From (9), using (6) – (8)
and market clearing conditions for each good, we find the demand for primary factors
and intermediate goods from each sector. Together with supply conditions, these
form the general equilibrium system. As the imperfectly competitive sectors are
representative consumer). T is constructed according to:
(
1+ Tijm = 1 + EXTAX ijm
)(1 + TRANS )(1 + TAREQ )
ijm
ijm
7
i ∈ ITG
characterised by monopolistic competition and zero profits, the scale of each firm is
fixed, and any output expansion is entirely reflected through an increase in the
number of firms (varieties) in the respective sector.
The use of intermediates from own as well as other industries implies
externalities through the existence of inter- and intra-industry cost and demand
linkages. These effects can be seen from the presence of the second and third term in
(9) and from (6). The parameters in (9) are calibrated using actual region and industry
specific input-output tables. For instance, in (9), a high BZij for a particular industry i
implies that manufacturing inputs are important, while a high giim in (6) implies that
inputs from your own industry are important. In this case, given the existence of trade
costs, your production costs are reduced if you are located in a region with a high
concentration of firms in your own industry. This dependence on intermediate inputs
from other firms is often referred to as the supply (or forward) link (se e.g. Venables
1996). In the same fashion the parameters of (9) (by the use of Shepard´s lemma)
determine which sectors and regions are important buyers of your product as
intermediate input. Because of the trade costs, you would increase your operating
profits by locating in a region with a high demand for your product. This comprises
the demand (or backward) link. The presence of these linkages implies pecuniary
externalities. Firms located in a region with a large number of buyers and suppliers of
important intermediates, will be relatively more competitive ceteris paribus.
Because our model has endogenous capital stocks, there is an additional
agglomeration force. More capital expands output via firm entry, which entails an
increased number of varieties. Consequently, the price indices for the imperfectly
competitive goods decline, which feeds back on investment and makes yet more
capital investments profitable – an instance of cumulative causation.
8
Agglomeration forces do not directly affect the perfectly competitive sectors.
These sectors, however, expand or contract as a consequence of competition for
factors with the other sectors. The decreasing returns to scale in these sectors (due to a
specific factor) act to dampen the expansion of the ITG sectors.
Data
The model is calibrated on actual data for1992, which is used as benchmark year.
Data sources for input-output tables, trade flows and factor shares are EUROSTAT
(EU input-output tables), GTAP (Global Trade Analysis Project) version 3 database
and NBER World Trade Flows (see Feenstra et al, 1997). A detailed description of
data, data sources, and how the benchmark data set was constructed, can be found in
Forslid et al (1999a) or obtained from the authors upon request. Forslid et al (1999a)
also provide a descriptive analysis of the data material, focusing on the distribution of
production across regions, trade flows and trade volume, differences in technology
and factor use across industries. Our data on economies of scale estimates have been
based on Pratten (1988). Transport costs and data on trade barriers are from GTAP
versions 3 and 4.
3.
The benchmark case
In this section we briefly review some key characteristics of production and trade that
are important when it comes to understanding the scenarios we later construct.
9
Table 2: Key characteristics – Base case 1992
The region's share (measured in percent) of
World
World manufacturing
GDP
exports
imports
EuropeW
12.09
17.65
17.70
EuropeC
11.75
18.50
17.88
EuropeS
8.27
8.84
8.80
EuropeN
1.96
3.51
3.45
EuropeE
0.89
2.06
2.78
FormSOV
2.21
0.78
0.78
CSAsia
3.17
4.39
4.37
SEAsia
20.27
20.80
20.80
USACAN
27.71
15.74
15.75
RestofW
11.67
7.73
7.69
World production of
energy
agriculture
12.52
8.71
7.09
3.54
7.34
6.05
2.16
1.69
1.53
2.57
3.79
2.11
3.12
17.07
12.94
18.31
20.46
16.07
29.05
23.87
Table 2 shows some key characteristics. First, it is important to notice the significant
differences in size between the regions. In particular when we analyse the effects of
successful transformation in Eastern Europe, it should be remembered that in
economic terms this region is very small. Secondly, trade is relatively more important
for the European regions than for the other regions8; in part this reflects the close
integration within Europe, but it should be observed that trade flows are relatively
important for Europe East as well. Finally, the table shows that the regions differ
significantly when it comes to relative importance of the resource-based industries. In
particular, in Europe East, Former Soviet Union, China and South Asia, and the rest
of the world, the shares of energy and/or agriculture production are higher than these
regions’ overall shares of global GDP.
Next we focus on manufacturing. Table 3 shows the pattern of specialisation
as measured by the Hoover localisation quotient; for each industry in a region, the
number shows the region’s share of global production (measured by value added) in
this industry relative to the region’s overall share of manufacturing value added.
8
The table only reports trade between the regions; in addition there may be significant trade flows
between the countries within each region.
10
Hence, a number greater than one indicates that this industry is of more than average
importance for the region.
The pattern of specialisation is, to a large extent, as we should expect. The
big, advanced regions like Europe Central and West, USA and Canada, and South
East Asia specialise in skill-intensive products (Transport equipment, Machinery),
while poorer regions like Europe East, China and South Asia, and also Europe South
specialise in labour intensive products (Textiles and Leather).
Table 3: The pattern of manufacturing specialisation
Share of the industry’s value added relative to share of total manufacturing value added
Textiles
Leather
WoodProd
Metals
Minerals
Chemical
FoodProd
TransEq
Machines
OtherMan
EuropeW EuropeC EuropeS EuropeN EuropeE Form
SOV
CSAsia SEAsia USA&
CAN
Rest of
World
0.78
0.88
1.06
0.96
0.92
1.06
1.08
1.15
0.95
0.96
2.51
2.17
0.48
0.82
1.87
0.84
0.92
0.38
0.70
3.74
1.71
1.68
0.91
0.95
1.67
1.05
1.57
0.61
0.44
1.01
0.58
0.56
0.91
1.05
0.70
0.98
0.81
1.32
1.18
1.48
1.61
3.15
0.87
1.15
1.17
0.92
1.13
0.84
0.74
0.54
0.53
0.72
1.63
1.19
1.06
0.83
0.89
0.84
1.01
0.64
1.59
2.75
1.03
1.16
1.57
0.89
1.31
0.47
0.60
0.91
0.73
0.22
1.12
1.04
1.52
0.88
1.13
1.09
0.90
0.73
0.86
0.86
0.84
1.07
0.92
0.97
0.94
0.87
1.22
0.95
0.78
0.34
1.25
0.90
0.74
1.06
0.84
1.23
1.10
0.66
Europe East is also fairly specialised in energy- and natural resource-intensive
industries, like Metals, Minerals and Food products. A similar pattern appears for
Europe North, with specialisation in Wood Products and Metals.
The geographical pattern of trade is reviewed in Table 4, which shows the
geographical distribution of manufacturing sales from each region. A couple of
observations are due. First, the home market dominance is very clear in all regions,
but less so in Europe than in the other regions. The explanation for this is obvious;
Europe is split into five regions with close ties among them. Second, geographical
closeness seems to matter; Europe Central typically has strong trade links to the other
European regions. In Asia, South East Asia is the most important trading partner for
11
China and South Asia. Thirdly, the strong trade links between Europe East and
Europe Central should be noticed; Europe Central is by far the most important market
for exports from Europe East, and it is also the most important supplier of imports to
Europe East.
Table 4: Geographical patterns of manufacturing sales
Distribution (in percent) of total sales of manufactures from a region
EuropeW EuropeC EuropeS EuropeN EuropeE Form
CSAsia SEAsia USA&
Sales to
SOV
From
EuropeW
EuropeC
EuropeS
EuropeN
EuropeE
FormSOV
CSAsia
SEAsia
USACAN
RestofW
69.3
11.9
8.1
8.6
3.6
0.7
1.1
1.3
1.8
1.4
11.2
71.3
7.0
7.9
10.5
0.9
0.9
1.2
0.9
0.9
6.9
5.5
78.0
2.7
3.6
0.5
0.5
0.5
0.5
0.8
1.4
1.9
0.6
73.5
1.0
0.4
0.1
0.2
0.2
0.1
0.5
1.4
0.6
0.5
74.8
0.5
0.1
0.1
0.1
0.2
0.3
0.6
0.4
0.5
1.2
94.3
0.6
0.1
0.1
0.1
CAN
0.5
0.4
0.2
0.4
0.6
1.3
82.0
1.6
0.4
0.5
2.5
2.4
1.3
1.8
0.9
0.7
7.2
87.5
3.1
2.2
3.6
2.5
1.8
2.6
0.8
0.3
4.7
5.3
89.4
4.1
Rest of
World
3.7
1.9
2.1
1.4
3.1
0.5
2.9
2.3
3.5
89.8
Trade costs, elasticity of substitution, scale economies, and input-output coefficients
are key parameters in our model, and affect the strength of linkages and cumulative
causation. Note, however, that in a model such as ours with large-group monopolistic
competition and free entry/exit, there is in equilibrium a one-to-one (inverse)
relationship between elasticity of substitution between varieties and scale elasticity.
This is a property we use when characterising the industries. The scale elasticities
used here are based on a ranking of industries according to engineering estimates of
scale economies by Pratten (1988).9 Table 5 shows a summary of trade costs and
returns to scale. It is evident that scale economies are most significant in the sectors
producing Transport equipment, Chemicals, Machinery and Metals. Hence, these are
sectors where market size matters the most; i.e. where firms’ competitiveness depends
on their market access.
9
In the sensitivity testing section below we use mark-up estimates provided by Martins, Scarpetta and
12
The use of intermediates is also a factor determining the location of production
and magnitude of agglomeration forces. The sixth column in Table 5 gives purchases
of intermediates from own sector as share of gross value of output. Supplies from own
sector create a positive feedback – via cost and demand linkages – and make
agglomeration self-reinforcing. From Table 5 we can see that Textiles, Wood
Products, Minerals and Chemicals are industries with an above average use of inputs
from own sector.
Table 5: Summary of trade distortions, scale economies
Average Intra
EEA trade
costs*)
Trade costs on
Returns to
Share of inputs
exports from
scale**)
from own sector in
EuropeE into
total costs***)
EEA*)
Textiles
4.7
29.3
0.06
0.357
Leather and Products
3.7
10.8
0.06
0.192
Wood Products
0.6
6.5
0.12
0.239
Metals
4.5
11.4
0.16
0.259
Minerals
7.0
16.7
0.10
0.272
Chemicals
6.5
20.6
0.24
0.279
Food Products
15.1
46.7
0.08
0.128
Transport Equipment
2.3
9.3
0.26
0.185
Machinery
2.6
6.1
0.20
0.203
Other Manufacturing
3.1
6.3
0.08
0.055
*) Percentage of producer price, averaged over the four EEA regions.
**) Percent reduction in average cost (AC) with a one-percent increase in output.
***) Based on Eastern European input-output matrices.
Location of production is moreover affected by comparative advantage, i.e. by
differences in factor endowments and factor intensities. Value added shares and factor
intensities ratios are shown in Table 6. Chemicals, Transport equipment and
Machinery are skill-intensive sectors. Textiles and Leather are intensive in the use of
unskilled labour. Food Products and Minerals are the most capital-intensive
industries, while Transport equipment, Textiles, and Machinery are the least capitalintensive industries.
Pilat (1996).
13
Table 6: Value added shares (based on Europe East).
PubServ
PrivServ
Textiles
Leather
WoodProd
Metals
Minerals
Chemical
FoodProd
TransEq
Machines
OtherMan
Agricult
Energy
4.
Skilled
Labour
0.476
0.140
0.085
0.081
0.085
0.087
0.077
0.108
0.073
0.127
0.148
0.073
0.009
0.036
Unskilled
Labour
0.321
0.436
0.583
0.571
0.561
0.534
0.492
0.42
0.424
0.584
0.531
0.579
0.504
0.301
Capital
0.203
0.424
0.332
0.348
0.354
0.379
0.431
0.472
0.503
0.289
0.321
0.348
0.145
0.405
Skilled/
Labour/
Unskilled ratio Capital ratio
1.483
3.926
0.321
1.358
0.146
2.012
0.142
1.874
0.152
1.825
0.163
1.639
0.157
1.320
0.257
1.119
0.172
0.988
0.217
2.460
0.279
2.115
0.126
1.874
0.018
3.538
0.120
0.832
Simulation results
The aim of the simulations is to get an impression of the consequences for production,
trade and development should Eastern Europe transform into well-functioning market
economies and become more integrated into the EU.
Apart from trade integration, a “successful transformation” of the Eastern
European economies includes several features. It is a change from the old command
economy to a market system, and it is a change towards freer trade relations and
maybe new trade partners. It includes a restructuring of industry and changes in the
pattern of production and consumption. It implies an improvement in resource
allocation and better investment and employment opportunities and, ultimately,
increased income and welfare. In a general equilibrium model some of these
implications appear as endogenous equilibrium effects, while others must be specified
as exogenous changes. The model cannot capture the transformation from non-market
to market economy endogenously. But it can help us understand what the
consequences may be for production, trade, and welfare under various possible
scenarios.
14
We will look at three “stylised stages” of successful transition. The first case is
one in which we study the consequences of improved productivity in all sectors in the
relevant region.
There are a number of reasons why we should expect such
productivity improvements: transformation to market economies implies more
efficient organisation of production, more cost-efficient production and more
competition in goods and factor markets, both within, and between, countries. All of
these changes will lead to more efficient production. In our stylised case we analyse
the general equilibrium consequences of an exogenously given 2.5 and 5 percent
Hick-neutral productivity improvement in all industries in the region in question. In
reality, there will, of course, be differences between industries; some may experience
huge improvements, while for others the scope for improvements is less. Lack of
information about individual industries prevents us from modelling sector-specific
variation.
Our second case focuses on investments. It is widely assumed that successful
transformation would imply a better investment climate in Eastern Europe. While the
combination of low production costs and closeness to the big European markets may
already give high expected rates of returns to investments in Eastern Europe,
uncertainties regarding the general development have so far dampened the actual
willingness to invest. In this scenario we lower the required risk premium for
investments in Europe East, to reflect the improved confidence following a successful
transformation. In the model the aggregate capital stock in a region is determined
from a steady-state condition such that the capital stock will grow until the marginal
rate of return is equal to the steady-state required rate.10 Reduced uncertainties entail
10
All economies are assumed to be in steady state in the benchmark case. From a standard neoclassical
growth model the steady-state capital stock is determined by parameters of the model such as the
subjective rate of time preference. We, thus, calculate the steady-state real return to capital in the
benchmark case, and in all experiments the capital stock adjusts until this steady-state return is
15
lower required rate of return, and a higher aggregate capital stock. But where – in
what industries – will investments be undertaken when the overall investment climate
improves? This depends on the relative profitability of various industries, and is one
of the questions our simulation model allows us to address. Note that our model does
not have any transitional dynamics – we do not model the transition whereby foregone
consumption is used to build up the capital stock. This implies that while we can
compare the real income in different steady-states we are not in a position to make a
proper welfare calculation taking into account the present value of forgone
consumption during the capital accumulation phase towards a new steady-state.
Previous analyses of Eastern European transition, in particular Baldwin et al
(1997) and Keuschnigg and Kohler (1998), have focused on market access and EU
enlargement; hence, trade liberalisation has been the key aspect. In our third
simulation exercise, we also analyse the effects of trade liberalisation in the form of
lower tariff equivalents of import barriers. However, to bring forward possible
sectoral differences, we consider an experiment where tariff equivalents between
Eastern Europe and the European Economic Area (EEA) are lowered towards the
(internal) EEA average instead of a uniform reduction in tariff equivalents.11
Economy wide effects
Table 7a shows the real income effects for all the regions for the cases sketched
above. The specific model experiments are: i) 2.5 and 5 percent Hicks-neutral
productivity improvement in all sectors in Eastern Europe (EE); ii) 5 and 10 percent
achieved. See Baldwin et al (1996) for further explanations of this approach.
11
The EEA average tariff equivalent is zero in most industries in our data; five exceptions exist:
Textiles, Metals, Chemicals, Food products, and Transport equipment. Of these, Food products in
particular, have substantial intra EEA tariff equivalents of 26.5 percent on average. (Note, that total
trade costs for intra EEA trade of this type of goods are lower due to the presence of export subsidies.)
Our experiment therefore gives a less dramatic effect on this sector than would a flat rate reduction.
16
reduction in the steady-state return to capital EE ; and iii) 10 and 20 percent sector-bysector reduction in the gap between Eastern Europe-EEA tariffs and intra-EEA
average tariffs. Note that the range of experiments implicitly also provide a sensitivity
test of the model.
The simulations suggest that the changes are of great importance for Eastern
Europe itself, while the consequences for other regions are minor. This result
corresponds well to the general impression of other studies.12
Table 7a: Real income effects (percent change from base case)
EuropeW
EuropeC
EuropeS
EuropeN
EuropeE
FormSov
CSAsia
SEAsia
USACAN
RestofW
Productivity
2.5 %
5%
0.02
0.03
-0.06
-0.12
0.00
-0.01
-0.03
-0.05
7.61
15.88
0.00
-0.01
-0.06
-0.13
0.01
0.01
0.00
0.00
-0.02
-0.04
Risk premium
-5%
-10%
0.01
0.02
-0.04
-0.08
-0.01
-0.01
-0.02
-0.04
4.81
9.94
0.00
-0.01
-0.04
-0.08
0.00
0.00
0.00
0.00
-0.02
-0.04
Liberalisation
10%
20%
0.03
0.12
-0.08
-0.31
0.00
-0.01
0.00
-0.01
2.54
12.38
0.00
-0.01
-0.08
-0.22
0.01
-0.01
0.00
0.00
0.00
-0.07
When a 5 percent productivity improvement results in 16 percent real GDP growth for
Eastern Europe, there are three important mechanisms: first, the direct production
effects of higher productivity; secondly, the implied improvements in international
competitiveness, and thirdly, induced investment growth. A reduced risk premium has
a similar effect: A 10 percent risk premium reduction entails an almost 10 percent
growth in real GDP. The fact that the capital stocks are endogenous gives rise to a
growth-linked circular causality à la Baldwin (1999); hence, the induced investments
play an important role in explaining the significant impact of economic transformation
on real income. Also the presence of intra- and inter-industry linkages and increasing
12
See e.g. Baldwin et a.l. (1997), and Keuschnigg and Kohler (1998).
17
returns to scale in production is important for the magnitude of the observed effects.
These create pecuniary externalities, and reinforce the effects of the initially induced
shifts in manufacturing production.
Although it is difficult to compare with previous studies, as the model
experiments differ, brief comparisons with recent studies suggest that we get
relatively strong real income gains. To take one example, the reduced risk premium
case is similar to a case studied in Baldwin et al (1997), and when comparing the
results, the steady-state real income gains we get are approximately twice as high as
what they report. This difference can most likely be ascribed to the fact that, unlike
Baldwin et al, we employ industry specific intermediate aggregates in our cost
functions (see equations (6) and (9)), and in that way the model captures stronger
linkage effects. A look at input-output matrices reveals that the diagonal (intraindustry deliveries) is highly dominating. As we shall see below, the real income
gains to Eastern Europe relate to an expansion of the manufacturing sector. However,
this expansion is largely concentrated to some five industries. The unequal degree of
expansion across manufacturing industries means that the magnitude of the extra
boost to production generated by increasing returns to scale and inter-and intraindustry linkages will hinge on how the linkages are modelled. Employing nonindustry specific intermediate aggregate, implies in this case that the importance of
intra-industry linkages is underestimated.
For the trading partners, there are two opposing forces affecting production;
they experience increased competition from Eastern European producers but, on the
other hand, enhanced demand in Eastern Europe also allows for more exports. In
addition consumers and purchasers of intermediates enjoy lower prices on goods from
Eastern Europe. The most important trading partner for Europe East is Europe
18
Central, and Table 7a shows that here the competition effect dominates. However, the
negative impact on real income in Central Europe, caused by successful Eastern
transformation, is very small. Among the remaining regions, it is worth noticing that
China and South Asia lose from transitions in Eastern Europe. This loss does not stem
from the direct trade relations between the regions; but is rather a terms-of-trade loss
for CSAsia as the transforming region expands production in industries that are
important CSAsia export sectors. For the rest of the regions the impact of Eastern
transition is almost insignificant. Unlike most of the other regions Europe West
actually gains from Eastern transition – i.e. demand effects dominate competition
effects – but the changes in real income are tiny.
Next we turn to the effects of trade liberalisation. The last two columns of
Table 7a report simulation results for liberalisation of trade between Eastern Europe
and the Western European regions (i.e. the EEA area). While the model behaves
roughly linearly in the two “transformation experiments” – focusing on productivity
and risk premium – there is a strong non-linear response to trade liberalisation. A 20
percent reduction in the defined tariff gap gives a real income effect about five times
as large as a 10 percent reduction in the tariff gap. Interestingly, this non-linearity is
exactly what theory would predict using smaller, stylised models of economic
geography (e.g. Fujita et al, 1999) where firms, similarly to our model, are linked by
their use of each other’s output as intermediate input. Trade liberalisation not only
allows for improved market access and a boost in productivity (due to scale effects), it
also affects in a very non-linear fashion, the magnitude of the agglomeration forces
(the circular causation). Regarding the impact of trade liberalisation between Eastern
Europe and EEA on other regions of the world, just as in the previous transformation
experiments, the effects are small or insignificant, and follow the same pattern as in
19
these experiments.
Table 7a reports real income effects instead of welfare effects. The reason for
this is that a proper welfare calculation must include real resources used to achieve the
new and higher steady-state capital stock. In Table 7b we decompose the real income
effect for Europe East. The first column shows the effects with endogenous capital
stock (as in table 7a) and the second with a constant capital stock. While the figure
with constant capital stock can be directly translated into welfare, the endogenous
capital stock case cannot. An approximate measure of the actual welfare gain in the
case with endogenous capital is given in column three, where we have subtracted the
return to capital of the “new” part of the capital stock.13 The difference between
columns two and three indicates that there are significant indirect welfare gains of
capital accumulation, due to e.g. an additional increase in the number of firms, which
implies more varieties and lower price indices.
Table 7b: Decomposing the real income changes for Europe East
Productivity 5%
Liberalisation 20%
Endogenous
capital stock
15.88
12.38
Constant capital End. cap. stock.
stock
minus return to ΔK
7.61
10.43
3.80
7.42
In Tables 8a and 8b the aggregate trade effects are shown. As indicated above,
Europe Central – as the most important trading partner for Europe East – is most
severely hit on the export side; but there is also an impact on Europe North. For the
other regions the trade effects are minor. It is worth noticing that imports to Europe
East in general decline as the economy boosts, and that is even true in the
liberalisation cases. Hence the competition (from Europe East producers) effect
dominates the income and demand (from Europe East) effect. As the trade balance for
13
In the absence of variety effects, and if returns to capital during transition equalled the subjective
20
each region is kept unaltered in all scenarios, the counterpart of this pattern of changes
in manufacture trade must be a decline in net exports from the agriculture and/or
energy sector in Europe East.
Table 8a: Changes in total manufacturing exports (percent change from base case)
EuropeW
EuropeC
EuropeS
EuropeN
EuropeE
FormSov
CSAsia
SEAsia
USACAN
RestofW
Productivity
2.5 %
5%
0.14
0.29
-1.64
-3.27
-0.19
-0.39
-0.82
-1.63
16.76
34.94
-0.25
-0.50
-0.22
-0.44
0.03
0.07
0.03
0.06
-0.20
-0.40
Risk premium
-5 %
-10%
0.10
0.20
-1.03
-2.06
-0.12
-0.23
-0.50
-1.00
9.98
20.52
-0.15
-0.30
-0.12
-0.25
0.02
0.05
0.03
0.06
-0.12
-0.25
Liberalisation
10%
20%
0.20
1.41
-2.35
-9.73
-0.13
-0.42
-0.43
-1.15
21.60
101.48
-0.28
-0.67
-0.48
-1.06
-0.01
0.06
-0.01
0.09
-0.16
-0.45
Table 8b: Changes in total manufacturing imports (percent change from base case)
EuropeW
EuropeC
EuropeS
EuropeN
EuropeE
FormSov
CSAsia
SEAsia
USACAN
RestofW
Productivity
2.5 %
5%
-0.49
-0.95
0.87
1.82
-0.11
-0.17
-0.01
0.03
-0.93
-1.85
0.78
1.68
0.11
0.25
-0.18
-0.34
-0.14
-0.27
0.14
0.31
Risk premium
-5%
-10%
-0.31
-0.62
0.53
1.08
-0.08
-0.14
-0.01
-0.01
-0.58
-1.16
0.46
0.95
0.07
0.15
-0.10
-0.21
-0.09
-0.17
0.09
0.18
Liberalisation
10%
20%
-0.74
-2.64
1.39
7.14
-0.23
-0.30
-0.34
-0.91
-0.61
-4.98
0.17
1.86
0.04
0.34
-0.26
-0.74
-0.18
-0.45
0.03
0.49
Sectoral Effects
Table 9 shows the sectoral production effects in Europe East. When studying the
sector-specific production effects, we should distinguish between three groups of
industries:
(i) Public and private services are non-traded.
Hence, these industries
develop in accordance with domestic demand. The input-output structure of the
model, and the fact that private services are important inputs in other industries,
discount rate, this calculation would be exact.
21
explain part of the growth in production of private services. The increased demand for
services from an expanding manufacturing sector triggers the expansion of the service
sector, which reduces the price on services (due to IRS in production), and in turn
enhances the profitability of manufacturers.
(ii) Energy and agriculture are treated as perfectly competitive sectors in the
model; hence, these are fairly flexible, and will to a large extent serve as residuals.
Should other sectors become profitable enough to expand beyond the possibilities
provided by increased productivity and new investments, primary factors will have to
be drawn from the perfectly competitive sectors.
(iii) There are ten traded, manufactured goods. Given that the initial “shocks”
in terms of productivity improvements and additional resources are neutral across
sectors, it is interesting to see the significant differences in production effects:
Textiles and Transport equipment experience the highest growth effects, while
Leather and Machines experience strong growth in some of the cases. The results
indicate that successful transformation may have strong pro-competitive effects on
these sectors. However, industries are sensitive to different experiments. In relative
terms, Textiles and Leather respond most strongly to the liberalisation experiment.
This indicates the importance of market access for these sectors. Also from Table 5,
the high own-input share in Textiles may be noted, which implies potentially strong
agglomeration forces in this sector. An accompanying significant fall in Textiles
production in Europe Central (see Forslid et al, 1999a) also indicates that there is an
agglomeration in this sector in Eastern Europe. Transport Equipment shows more
comparable growth rates across all experiments. Here delocation from other regions is
more modest. What these sectors have in common is an extensive use of labour
relative to capital (see Table 6). However, the former is typically unskilled intensive
22
sectors, while the latter is typically skill-intensive. It might seem surprising that skillintensive sectors such as Transport equipment and Machines expand in Europe East,
which is a relatively less skill-abundant region. However, even though Transport
equipment and Machinery are relatively skill intensive compared to other sectors in
Europe East, they are significantly less skill intensive than the same sectors in the EU
(see Forslid et al, 1999a).
Table 9: Production effects in EuropeEast (percent change from base case)
Productivity
2.5 %
5%
4.55
9.38
6.00
12.35
14.00
28.84
10.61
21.99
7.94
16.32
12.30
24.93
9.01
18.40
9.09
18.73
7.29
15.32
37.15
74.97
18.16
37.05
9.62
19.76
-5.58
-11.33
-4.28
-9.04
Public service
Private service
Textiles
Leather
Wood Prod.
Metals
Minerals
Chemicals
Food Prod.
Trans.Eq.
Machines
OtherMan
Agriculture
Energy
Risk premium
-5%
- 10%
2.39
4.86
4.00
8.22
8.75
17.93
6.73
13.86
5.03
10.30
7.96
16.14
5.94
12.12
5.80
11.94
4.51
9.38
23.11
46.66
11.56
23.51
6.11
12.50
-4.95
-9.93
0.07
0.03
Liberalisation
10%
20%
0.56
3.05
1.68
7.37
42.08
179.16
12.09
44.03
2.81
12.89
4.66
15.94
2.96
11.05
6.19
23.96
3.63
16.91
23.92
71.81
1.56
10.50
2.95
15.83
-10.45
-46.92
-14.82
-63.12
Factor prices
Table 10 shows the effect on factor prices in Europe East. Both unskilled and skilled
wages rise in the different transformation and integration cases, but skilled labour
experiences a significantly higher wage increase than does unskilled labour.
Table 10: Real wages in Europe East (percent change from base case)
Skilled
labour
Unskilled
labour
Productivity
2.5 %
5%
10.70
22.40
8.50
17.80
Risk premium
-5%
- 10%
7.20
15.00
Liberalisation
10%
20%
5.00
22.80
5.70
3.10
11.80
23
14.50
The impact on real wages in the transformation experiments related to productivity
and risk premium, is best explained by reviewing Table 6 on factor intensities and the
production effects in Table 9. In general we see that both unskilled- and skillintensive sectors expand, but the expansion of skill-intensive sectors is significantly
larger than that of unskilled-intensive sectors. In the liberalisation cases, the most
significant structural change is the reallocation of resources from the perfectly
competitive sectors (agriculture and energy) to the manufacturing and services
sectors. As the declining sectors – and in particular agriculture – are the least skillintensive sectors, this reallocation explains the relative factor price changes.
Sensitivity testing
Our experiments so far contain an element of sensitivity testing with respect to the
magnitudes of changes in productivity, risk premium and trade costs. It is well known,
however, that numerical results in the Dixit-Stiglitz framework may be highly
sensitive to the magnitude of the scale elasticities (σ). We have earlier pointed out that
in equilibrium there is a one-to-one relationship between the scale elasticity (a
technology parameter) and the elasticity of substitution (a demand related parameter)
in such models in equilibrium. While our simulations so far have been conducted with
scale elasticities based on the ranking by Pratten (1988), in this section we will
employ alternative values for σ taken from Martins, Scarpetta and Pilat (1996), who
estimate sectorial mark-ups (σ/(σ−1)) within 14 OECD-countries over the period
1970-92.14 For comparison, Table 11 displays the mark-ups used in the simulations
above as well as the values based on Martins, Scarpetta and Pilat. While the overall
14
The matrix of mark-ups by Martins, Scarpetta and Pilat (1996) contains some empty cells due to
missing observations. Francois (2000) runs cross-country regressions with sector and country dummies,
and use the resulting coefficients to fill the empty cells. We use this matrix to calculate unweighted
24
levels are very similar, there are some significant sectoral differences. One example is
Transport equipment, which is a sector characterised by large (technological) scale
economies, but where fierce market competition forces mark-ups down.
Table 11: Mark-up Ratios
Pratten
Textiles
Leather and Products
Wood Products
Metals
Minerals
Chemicals
Food Products
Transport Equipment
Machinery
Other Manufacturing
1.05
1.05
1.14
1.19
1.11
1.31
1.09
1.35
1.25
1.09
Martins,
Scarpetta and
Pilat
1.13
1.15
1.19
1.19
1.27
1.24
1.14
1.12
1.31
1.18
In Tables 12 and 13 we display simulation results for real income and sectoral
production effects in Eastern Europe based on repeated experiments employing
elasticity estimates based on Martins, Scarpetta and Pilat instead of Pratten. As for
real income effects, a comparison of the results in Table 12 with those in Table 7a
indicates that our results are very robust – both in a qualitative and a quantitative
sense.
averages over the 14 countries for each sector.
25
Table 12: Real income effects II (percent change from base case)
EuropeW
EuropeC
EuropeS
EuropeN
EuropeE
FormSov
CSAsia
SEAsia
USACAN
RestofW
Productivity
2.5 %
5%
0.02
0.04
-0.07
-0.13
-0.01
-0.01
-0.02
-0.05
8.07
16.89
-0.01
-0.01
-0.07
-0.14
0.01
0.01
0.00
0.00
-0.02
-0.04
Risk premium
-5.0 %
-10 %
0.01
0.02
-0.04
-0.08
-0.01
-0.01
-0.02
-0.03
5.10
10.55
-0.01
-0.01
-0.04
-0.09
0.00
0.00
0.00
0.00
-0.02
-0.04
Liberalisation
10 %
20 %
0.01
0.04
-0.07
-0.24
0.00
-0.02
-0.03
-0.14
2.60
10.77
0.00
-0.02
-0.09
-0.30
0.01
0.01
0.00
0.00
-0.01
-0.08
Turning to production effects by sector in Eastern Europe (see Table 13), we would a
priori expect the results to differ more from our previous results, because of the
differences in size and rankings of sectoral mark-ups. However, again the results in
the two transformation experiments on productivity and risk premiums appear to be
remarkably robust. In the trade liberalisation experiments there are more significant
changes – in particular for Textiles, Leather and Transport equipment.
The
differences in the assumed mark-ups and hence elasticities are to a large extent
reflected in the simulation results. Martins et al report higher mark-ups for Textiles
and Leather, while their mark-up for Transport equipment is lower than the one
applied above. A lower mark-up – as in the Transport equipment case – implies a
higher elasticity of substitution and a more competitive market. Liberalisation would
typically yield stronger production effects in markets with a higher degree of
competition, and vice versa, and that is exactly what our simulations confirm. Textiles
and Leather, with less competitive markets according to the alternative assumptions,
show smaller production effects of liberalisation in Eastern Europe, while Transport
equipment, which is a much more competitive market under the alternative
assumptions, reveals significantly stronger production growth.
26
Table 13: Production effects in EuropeEast II (percent change from base case)
Public service
Private service
Textiles
Leather
Wood Prod.
Metals
Minerals
Chemicals
Food Prod.
Trans.Eq.
Machines
OtherMan
Agriculture
Energy
5.
Productivity
2.5 %
5%
4.70
9.68
6.23
12.84
14.47
29.76
11.10
23.02
8.05
16.57
13.15
26.73
8.84
18.02
9.67
19.94
7.60
16.02
42.08
85.74
18.99
38.74
9.84
20.24
-6.17
-12.60
-4.76
-10.20
Risk premium
-5%
- 10%
2.48
5.04
4.15
8.52
9.05
18.51
7.05
14.48
5.10
10.45
8.49
17.24
5.82
11.86
6.16
12.69
4.70
9.79
26.12
53.02
12.08
24.55
6.25
12.77
-5.30
-10.67
-0.21
-0.62
Liberalisation
10%
20%
0.49
2.27
1.71
6.48
20.12
60.55
6.96
22.14
2.67
10.36
8.84
32.34
4.20
15.51
5.23
16.44
3.19
13.20
60.77
229.87
5.63
24.79
3.35
13.15
-10.40
-40.37
-14.41
-54.17
Discussion and conclusions
In this paper we have presented model simulations for a set of scenarios involving the
transformation and integration of Europe East. Although the scenarios have been
specified in a stylised way, they might capture potentially important structural
changes. They might further indicate the relative importance of different reforms and
developments, and the very different effects Eastern transition may have across
sectors and regions.
Our simulations show that the neighbouring countries in Europe Central are
more affected than other European regions, and one feature that was not obvious ex
ante, is that the overall effect for Europe Central is negative. But even for Europe
Central, where some sectors may see successful transformation in Eastern Europe as a
threat, the overall effects in terms of real GDP are very small.
Simulations not reported in the paper show that adding Former Soviet Union
transition to Eastern European transition, has a negligible effect on all other regions
than the Former Soviet Union itself, which experiences a strong real income effect.
27
The reason for this being the region’s insignificant trade in manufacturing goods in
the benchmark case
Although our simulations do not cover agricultural policies, the implications
for agricultural markets in Europe may be of interest. The strong growth impetus to
manufacturing sectors in Europe East in our scenarios, actually implies increased
demand for imports of agricultural products to the region. In a more complete
scenario the overall effects on Europe East agriculture will depend on the direct
effects of reforming agricultural policies, and on the implied effects of transformation
of other sectors. Our analysis only includes the latter effect. However, the results
indicate that the “conventional wisdom” of an expected strong growth of agricultural
exports from east to west following an Eastern EU enlargement, is not necessarily
true.
The simulation results show a strong correlation between real income gains
and growth in the production of manufactures; this calls for an explanation, as such a
result would not appear in traditional trade models. However, our model differs from
traditional neo-classical comparative advantage models in many ways; in particular,
there are pecuniary externalities in manufacturing production. Hence, there are selfreinforcing growth effects in manufacturing industries, which may give rise to cost
advantages, increased value added, and the type of correlation we observe between
manufacturing production and real income in the simulation results. Put differently,
we get rent-shifting effects, similar to the profit-shifting effects we know from
strategic trade policy analysis. Regions that get more of the industries with pecuniary
externalities gain, while other regions may lose.
To summarise, we can conclude that successful reforms and transformation in
Eastern Europe and Former Soviet Union are of huge importance for the economic
28
development in these regions, and may imply significant changes in the pattern of
specialisation and trade in these regions. However, in economic terms both regions
are too small to matter very much for the overall production and real income
elsewhere.
29
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