The Spatial Wage Structure in the
European Union –What Role for Economic
Geography?
Holger Breinlich
LSE and CEP
WORK IN PROGRESS!
1
Introduction
Within the last ten years, the “New Economic Geography” has experienced rapid theoretical
advances. Empirical research, however, is still in an early stage. The existing literature has
investigated the effects of geography on trade flows, the structure of production, and the
spatial variation of income levels1.
This paper is concerned with the latter aspect and is thus part of a small but growing number
of empirical studies that have used the New Economic Geography framework to analyse the
impact of distance from markets for the level of wages. The basic idea is that firms in
locations further away from consumers will face higher transport costs which reduces their
exports receipts, resulting in a lower value added attributable to the factors of production.
A first strand of the literature is concerned with the effects of geography at a national level,
assuming perfect labour mobility between regions (and thus equalisation of real wages).
Hanson (1998) shows for a panel of US counties that the market access of a location
(measured as the weighted sum of all regional GDPs, where weights are inversely related to
distance from the location in question) has indeed a significant positive impact on local
(nominal) wages. Mion (2002) finds similar results for Italy, De Bruyne (2002) for Belgium
and Brakman et al. (2000) for Germany2.
Redding and Venables (2000) look at the relation between economic geography and income
on an international level. In their model, which builds on Krugman and Venables (1995) and
Fujita, Krugman and Venables (1999), wage levels are also influenced by the existence of
intermediate factors of production: distance from suppliers of these factors increases
intermediate input prices and thus further reduces value added. Taking the location of demand
and production as given, they derive a structural equation relating wages to market and
supplier access, which measure the proximity of a country to markets and intermediate goods
suppliers, respectively. Using data on 101 countries, they find that a significant fraction of
income differences can be explained by these two variables. The results are robust to the
inclusion of a number of other potential determinants.
This paper is concerned with the role of economic geography for the spatial structure of
regional wage levels in the European Union during the period 1980-1997. Methodologically,
it is very much related to the approach used by Redding and Venables, which seems to be
more appropriate to the issue in question, given the very low mobility of labour both between
and within countries in the European Union. For example, Barro and Sala-i-Martin (1995)
estimate the impact of income differences on regional migration for several European
countries3. On average, a ten percent increase in local real GDP per capita leads, ceteris
paribus, to a yearly population inflow of less than 0.1%.
The present analysis contributes to the growing literature on the consequences of economic
geography for income levels by applying the theoretical framework developed by Krugman
and Venables (1995) and Fujita, Krugman and Venables (1999) to a new kind of data. A
priori, it is not clear whether the importance of access to markets and intermediate goods is as
significant on a regional level as it is in the international context studied by Redding and
Venables. Other mechanisms, such as technological spillovers or density effects on
productivity might be more important here. Also, given the panel-character of the data set
used, a more detailed investigation of the implications of the underlying model is possible. As
1
See Overman, Redding and Venables (2001) for an overview of the literature.
De Bruyne tests for employment density rather than for wages.
3
The countries are Germany, Italy, France, Spain and the United Kingdom; the period of observation is 19501990. For the 1980s, data is available for Germany, Italy and Spain, only, and indicates a slightly higher
mobility, which is still only 0.1%.
2
compared to the papers using national data, this study has the advantage of taking detailed
account of demand and supply linkages that go beyond national borders 4. This seems to be
especially important in the case of the three European economies studied (Italy, Belgium and
Germany) which are more economically integrated with their neighbours than the United
States are5.
The structure of this paper is as follows. Section 2 presents a brief description of the spatial
structure of wage levels in the European Union and how it has changed during the period
1980-1997. Section 3 introduces the theoretical framework from which the econometric
specification is derived that is used in the subsequent sections. Section 4 estimates a trade
equation for trade both within the European Union and between the EU and the rest of the
world in order to derive estimates which will be used in section 5.1 to calculate the market
and supplier access of EU-regions. In sections 5.2-5.5, regional income levels are regressed
on these measures and several robustness checks are undertaken. Section 6 notes alternative
explanations for the spatial income structure that is found to exist within the EU and section 7
concludes.
As this paper is very much work in progress, ideas and extensions that go beyond the basic
framework are so far only briefly sketched.
4
De Bruyne and Brakman et al. include GDP of other EU-countries. However, they use distance/commuting
time to capital cities as their transport cost measure which reduces cross-regional variation of this part of market
access and underestimates the advantage of border regions.
5
A rough indicator of integration is the share of external trade in GDP (calculated as (imports+exports)/GDP).
Figures are 25% for the USA, 49% for Italy, 55% for Germany and 145% for Belgium (source: Penn World
Tables 6.1, year:1997).
2
Wage Gradients in Europe 1980-1997
Though differences are smaller than between countries in a worldwide perspective, income
levels within the European Union still vary by a substantial amount. For example, in 1997, per
capita GDP in the 5% richest Nuts2-regions was more than three times higher than that of the
5% poorest regions6. Also, there seems to be a strong income gradient, i.e. per capita GDP at
the geographical periphery is lower than at the centre. Table 1 shows this relation by
regressing the log of per capita GDP on distance from Luxembourg, the approximate
geographical centre of the EU15. On average, per capita GDP decreases by 4% for every
100km distance from Luxembourg. Also, the gradient seems to have flattened out slightly
since 1980, as displayed in figure 1 (the figure shows the distance coefficient of above
regression for every year since 1980 for the subsample of the EU127).
The existence of a strong but declining centre-periphery gradient is at least not inconsistent
with market access being a determinant of income levels. Peripheral countries might be at a
disadvantage due to the distance from main European markets. Also, as European integration
proceeds and inter-regional transport costs decline, centrality might become less important for
income levels, leading to a flattening out of gradients. Whether this cursory impression is
correct or not will be the subject of the subsequent analysis.
3
3.1
Theoretical Framework and Econometric Specifications
The Model
The theoretical framework underlying the empirical analysis in this paper is a reduced version
of a standard New Economic Geography model with intermediate goods (see, for example,
Fujita, Krugman and Venables (1999), chapter 14). I consider a world with R locations, where
the focus is on the manufacturing industry which produces under increasing returns to scale
and product differentiation.
Manufacturing goods can be used for final consumption and as intermediate inputs. Final
demand for goods in location j is derived via utility maximisation of the representative
consumer’s CES utility function:
R
( 1) /  
U j   ni xij

 l 1

 /( 1)
R
s.t.[ ni pij xij ]  Y j
i 1
Where ni is the number of firms in location i and xij is the amount of consumption in location j
of a variety produced in i8. Sigma is the elasticity of substitution between varieties and pij the
price of location i varieties in j (consistent of the mill price pi and iceberg transportation costs
Tij between the two location: pij=piTij). Finally, Yj is income in location j. Solving the
optimisation problem we obtain the demand facing a firm in i from location j:
6
Figure for EU15, GDP measured in 1995-ECU. See appendix A for a description of the data.
There is no GDP data available at the Nuts2-level for 1980-1988 for Austria, Finland and Sweden. This
exclusion also explains the (absolutely) larger coefficients in figure 1 as Finland and Sweden are peripheral highincome countries. Including these three countries for 1988-1998 shows a qualitatively similar picture of
flattening wage gradients (from –0.05 to –0.04).
8
In equilibrium, the amount of the different varieties produced in location i and consumed in location j can be
shown to be equal (due to symmetric weights of varieties in the utility function and identical production
technologies).
7
xij 
pij

R
n p
1
nj
n
n 1
 Yj
Defining a price index for manufacturing goods Gj and rewriting final consumption yields:
R
G j  [ nn p nj
1 1 / 1

]
xij
n 1
Cons
 pij

Yj  G j
( 1)
Demand for intermediate goods is derived analogously. Assuming a Cobb-Douglas
production function with share α of intermediates and a sub-production function for
aggregation of intermediates similar to U we get:
xij
IM
 pij

 I j Gj

where I is the amount of intermediates used for production in j. Thus total demand facing a
firm in i from j is:
xij  pij


 I j  G j  pij

Yj  G j
 1
 pij

Gj
 1
 (I jG j  Yj )
Summing over all products produced in location i, we obtain the value of total exports from i
to j as (“trade equation”):
ni pij xij  ni pi
(1)
1
(Tij )1 G j
 1
(E j )
where the last term (Ej) stands for total expenditure in j, summarising final and
intermediate demand. Turning to the supply side, firms maximise the following profit
function with respect to prices:
R
 i   pi xin  Gi  wi  vi  ci [ F  xi ]
n 1
where w is the wage rate, v the price of other factors of production, c the unit input
requirement and F fixed costs. Optimal prices turn out to be set at a mark-up σ/(σ-1) over
marginal costs. Free entry assures that long-run profits will be zero, implying:
R
pi x   E j G j

j 1
 1
(Tij )1
Inserting the profit maximising price I obtain:

R
 


ß 
 1
x
 Gi wi vi ci    E j G j (Tij )1
  1

j 1
This expression can be transformed to yield the maximum remuneration firms can afford to
pay the factors of production other than intermediate goods. Focusing on wages, I derive the
so-called “wage equation”:

R
 


ß 
 1
x
 Gi wi v i c i    E j G j (Tij )1
 1

j 1
(2)
 wi  A( SAi )

 ( 1)
1
( MAi )

1
 
 
  vi   ci  


where
R
SAi   snTin
n 1
1
R
  nn p n
n 1
1
1
Tin
R
and
MAi   m n Tin
n 1
1
R
  E n Gn
n 1
 1
Tin
1
The terms s and m stand for supply and market capacity of a location n, respectively. The first
reflects the number and price competitiveness of intermediate goods produced in n, the
second summarises the market potential of a location. The terms SA and MA, in turn, stand
for supplier access and market access and are transport cost weighted sums of the supply and
market capacities of all regions. These expressions summarise how well a location is endowed
with access to cheap and numerous intermediate products (supplier access) and with access to
markets for the products it produces (market access). As explained in the introduction, firms
in locations with higher market and supplier access incur less transportation costs and are able
to pay higher wages.
In this model, the location of firms and demand (n and E) is taken to be exogenously given.
This assumption allows the direct derivation of the econometric specifications from the
model. In a fully fledged version of this model (such as in Fujita, Krugman and Venables
(1999), chapter 14), both variables are endogenised. This leads to the possibility of
cumulative causation and multiple equilibria which would make empirical analysis much
harder.
3.2
Econometric Specifications
The two key relationships that will figure centrally in the rest of the paper are equations (1)
and (2). Equation (1), the “trade equation”, can be rewritten using the definitions of market
and supply capacities to relate bilateral exports to these measures:
ni pij xij  si (Tij )1 m j
Taking logs, I obtain the econometric specification used in the estimation in section 4:
(3)
ln(exp ij )  ln( si )  (1   )  ln( Tij )  ln( m j )   ij
In practice, market and supplier access will be proxied by the trading partners’ GDP, transport
costs by bilateral distances. From the coefficients of this regression, estimates of market and
supply capacities of European Regions and countries around the world can be derived (details
in section 4). These in turn can be used to calculate market and supplier access of the Nuts2
regions (details in section 5). The resulting access measures will then be used in the second
key equation of this paper, the wage equation (2). This equation relates wages to market and
supplier access. Again, by taking logs I get the specification used in the estimation in section
5:
(4)
ln( wi )    1 ln( SˆAi )   2 ln( Mˆ Ai )   i
4
Estimation of Trade Equation
In this section, I will estimate the trade equation derived in section 3. The underlying idea is
to use the information contained in trade flows to get estimates for market and supply
capacities and bilateral transport costs. The obvious problem this approach encounters is that
there is almost no data on bilateral trade flows at a regional level for the European Union. To
circumvent this problem, the assumption needs to be made that interregional trade flows are
governed by the same forces as international ones. That is, it is assumed that estimates
obtained from a gravity equation on an international level can be used with regional data on
GDP and bilateral distances to calculate market and supply capacities.
To make these estimates more applicable to regional European data, only trade flows within
the EU12 and between the EU12 and the rest of the world (ROW) are included in the
estimation9. Furthermore, I allow the estimates of transport costs to vary between within EU
and ROW-EU trade, possibly capturing differences in the importance of distance for the two
subsamples. The trading partners’ GDPs are used to proxy market and supply capacity. A
specification using country dummies would be preferable, especially for supply capacity
which summarises number and prices of intermediate goods and might be poorly proxied by
GDP10. However, such estimates would not be transferable to the regional level.
Data on bilateral manufacturing trade flows and distances is taken from the NBER World
Trade Database (Feenstra et al., 1997; Feenstra, 2000), GDP data from the World
Development Indicators 2001. The number of countries present in both data sets is 148. The
sample had to be reduced further due to missing GDP-data, leaving us with 119 countries11. In
order to make observations comparable over time, all data are expressed in 1995 US-dollars12.
The data are aggregated into three six-year periods (1980-1985, 1986-1991, 1992-1997) to
smooth short-run fluctuations in trade flows and GDP. Finally, it is known from the literature
on border effects that capital-to-capital distances between neighbouring countries are usually
overmeasured13. In the sample used this could lead to the erroneous conclusion that distance
is more important for intra-EU12 trade than for trade with the ROW. To reduce this problem,
adjusted distances are calculated as the GDP-weighted sum of bilateral distances between the
Nuts2-regions of the two trading partners (for intra-EU12 trade) or as the sum of GDPweighted distances between regions and non-EU12 countries for EU12-ROW trade. That is:
dist IJ   si s j dist ij
where si is share of region i in country I’s GDP. For J being a
iI jJ
non-EU12 country, this simplifies to:
dist IJ   si dist iJ .
iI
The specification estimated is derived from equation (3) in section 3.2 by replacing market
and supply capacities with the trading partners’ GDPs. Furthermore, transport costs are split
up in a multiplicative and an exponential component (Cmult and Cexp), allowing for a more
flexible relationship between distance and transport costs:
9
The estimation was also performed for the EU15 and the ROW, yielding only marginal differences in results.
Redding and Venables (2000) use dummies in their estimation.
11
See appendix A for a list of countries included.
12
The trade data are deflated using the US GDP deflator (source: World Development Indicators, 2001).
13
See for example Head and Mayer (2002).
10
ln( X ij )  ln( s i )  (1   ) ln( Tij )  ln( m j )
 ln(exp ij )  ln( GDPi )  (1   ) ln( Cmult ij  dist ij
C exp ij
)  ln( GDPj )
 ln(exp ij )  (1   ) ln( Cmult ij )  ln( GDPi )  (1   )  (C exp ij )  ln( dist ij )  ln( GDPj )
 ln(exp ij )  1  EU int   2  EUext  1 ln( dist ij )  EU int   2 ln( dist ij )  EUext
  1 ln( GDPi )   2 ln( GDP j )   ij
where EUint and EUext are dummy variables that take the value 1 when a trade flow is intraEU12 or EU12-ROW, respectively. This specification is separately estimated for all three sixyear periods.
Table 1 presents results for both OLS and Tobit-estimation. The latter takes into account that
a small fraction of the trade data is left-censored at zero. As expected, the Tobit estimates
show a slightly lower coefficient on distance, though differences are small due to the small
number of zero-observations14. The Tobit estimates are used in the calculation of transport
costs, and market and supplier access in the following.
From the intercept and distance coefficients, an estimate of the transport cost term in the trade
and the market and supplier access equations can be calculated for each of the three periods as
follows:
Tij
1
 e1 dist ij
1
 e 2 dist ij
Tij
1
2
for intra-EU12 trade
for trade ROW-EU12
These formulae show that it is actually important to allow for time varying intercepts in the
trade equation when comparing transport costs over time as changes in the coefficient of the
distance variable only reflect changes in the slope of the distance-transport cost relationship.
Results indicate a steady rise of Tij1-σ for both trade within the EU12 and for EU12-ROW
trade with an acceleration in the 1990s (note that absolute figures are difficult to interpret as
they depend on the choice of units in the trade equation). Using the median adjusted distance
between EU12-countries of approximately 1200km, the increase is 22% between the first two
periods (80-85 and 86-91) and 66% between the two last periods (86-91 and 92-97). For
EU12-ROW trade the figures are 16% and 101%, respectively. Assuming a demand elasticity
of σ=6, this corresponds to a decrease of intra-EU12 iceberg transportation costs of 4% and
10% (3% and 13% for EU12-ROW15). Though the gap seemed to be closing slowly in the
1990s, exports to a non-EU12 country that was 1200km away were still 8% more costly in the
last period under consideration (1992-97) than exporting to a EU12 country at the same
distance.
Extensions:
- Use regional trade data where available and compare results of regional gravity-equation
with values obtained here. Existing data are:
o Trade flows between British regions and the rest of the world (from HM Customs &
Excise, http://www.uktradeinfo.com/index.cfm?task=aboutreg).
o Interregional trade in France (see Combes, Lafourcade, Mayer, 2002).
14
Ceteris paribus, bilateral trade flows have a higher probability of being left-censored (i.e. equal zero) for
higher bilateral distances, introducing an upward bias on the distance coefficient.
15
The median distance between EU12-countries and the ROW is 6600km. Using this figure in above
calculations, we get a decrease in iceberg transportation costs of 6% and 11%.
-
Compare estimated distance coefficient with actual data on transportation costs (e.g. from
Combes and Lafourcade, 2001).
5
Market Access, Supplier Access and Regional Wages
5.1
Construction and Summary Statistics
Market and supplier access are constructed following the formulae in section 3.1, using the
results of the trade equation estimation. Thus, market access of a Nuts2-region is the weighted
sum of GDPs of all other Nuts2-regions and the rest of the world. Formally, market access of
region i in period t is given by:
Mˆ Ait 
 GDP
ˆ 2 t
jt
jEU 12
 (eˆ1t dist ijt
ˆ1t
)
 GDP
ˆ 2 t
jt
jEU 12
 (eˆ 2 t dist ijt
ˆ 2 t
)
Supplier access is calculated analogously, using the estimated parameter on reporter GDP in
the trade equation:
SˆAit 
 GDP
jEU 12
jt
ˆ1t
 (eˆ1t dist ijt
ˆ1t
)
 GDP
jEU 12
jt
ˆ1t
 (eˆ 2 t dist ijt
ˆ 2 t
)
To calculate the part of market or supplier access a region derives from itself, intraregional
transport costs are calculated by replacing own-distances with values obtained from the
formulae distii=0.33(area/)1/2 which gives the average distance between two points in a
circular region.
Due to the fact that I allow for varying transport cost coefficients, both access measures can
be split up into an EU12-part and a remaining part (the first and the second sum in above
equations). Table 3 provides some information on composition and changes of these
variables. Figure 2 graphs market and supplier access as a function of distance from
Luxembourg, the approximate geographical centre of the EU15, for the period 1992-97 (using
distance from the French region of Lorraine which corresponds a little better to the centre of
the EU12 regions used here, does not change results perceptibly).
5.2
Wage equation – baseline specification
I now proceed to the estimation of the wage equation. The baseline specification is that
derived in section 3.2:
ln( wi )    1 ln( SˆAi )   2 ln( Mˆ Ai )   i
As had to be expected from the construction of both market and supplier access as a function
of distance weighted GDPs of surrounding locations, the two measures are highly correlated
(correlation coefficient: 0.82). Thus, due to multicollinearity problems the baseline
specification is estimated separately for market and supplier access. To avoid the most
obvious source of endogeneity, both estimations are done excluding own market access 16. The
dependent variable is log of 1995 per capita GDP (in ECU), serving as a proxy for
manufacturing wages. Whether this will bias the results certainly requires further
investigation. However, note that with labour being relatively immobile across regions (in
contrast to capital), workers will probably bear the main burden of low market and supplier
16
Including own market and supplier access introduces a positive correlation with the error term of the wage
equation as a shock to own GDP raises both market and supplier access as well as per capita GDP unless GDP
changes are driven by population changes only. Indeed, including own access measures raises coefficients and
R2 slightly.
access, whereas the remuneration of capital will tend to be equalised across regions. Thus, it
is to be expected that using GDP data actually introduces a downward bias on the access
coefficients. Using GDP also has the advantage that such data is readily available for most of
the countries and periods of interest. Still, missing data restricts our analysis to the subsample
of the EU12 (excluding Austria, Finland and Sweden) when looking at the full period of
1980-1997. In section 5.4, I will replicate results using GDP data for the full sample of
countries for the period 1989-1997 and for a subsample using data on manufacturing wages.
Table 4 shows the results of the baseline specification. Both market and supplier access are
both statistically and economically highly significant. Doubling market access increases per
capita GDP by approximately 70% (95% for supplier access). The access measures explain
between 55-60% of income variations in the 171 EU12 Nuts2 regions used in the estimation.
The next sections examine the robustness of these results. As results for market and supplier
access are quite similar in all the following, I focus on market access only, which is probably
more exactly measured given the problems of proxying supply capacity by GDP (see section
4).
5.3
Instrumentation and country fixed effects
Another potential source of bias of the estimates comes from omitted variables that influence
per capita GDP and are correlated across regions. As a first step, I instrument 1992-1997
market access by its 1980-85 value and regress average 1992-1997 per capita GDP on it. This
should eliminate problems arising from intertemporarily uncorrelated variables (e.g.
nationwide strikes). The lag length used will also reduce the impact of omitted variables
correlated over time and space (e.g. business cycle fluctuations). Still, other important
determinants of income levels, such as technological differences or the quality of institutions
of a region, are likely to be highly persistent over time. Such differences will probably be
smaller within countries than between countries and I try to capture them by additionally
including country dummies in the regression. It should be noted that these dummies also
capture the part of market access that is common across a country and thus identification
relies on intranational variation only.
As a further check on the consistency of the estimates, I instrument log market access by log
distance from Luxembourg and the United States. The first measure is a good proxy for
within-EU access, the second for extra-EU access. In a first stage regression, both explain
approximately 80% of the variation in market access.
Results of the various specifications are presented in table 5. Column one presents results for
the IV-estimation using lagged values of market access, column two adds country-dummies to
the regressors and column three shows the results of using above distances as instruments.
Using lagged values doesn’t change the baseline results of section 5.2 by much due to the
only small changes in the spatial variation of market access over time. Including country
dummies raises the explanatory power of the regression enormously and reduces the
coefficients on the access measures somewhat. However, both stay statistically and
economically significant. Using distances as instruments also confirms the robustness of the
results presented here17.
5.4
Full sample and manufacturing wages
A potential concern with the results presented so far is that Austria, Finland and Sweden had
to be excluded due to data limitations. As the last two countries are peripheral but high
income countries, this will bias our results in favour of finding an important role for our
access measures. Indeed, as seen in section 5.1, both market and supplier access decrease with
17
Note also that regression the log of per capita GDP directly on the two distance measures lowers the R2 of the
regression to 47%, showing the superiority of an access-based approach.
distance from Luxembourg. Including the three countries reduces the time period for which
data are available to 1988-1997.
Secondly, wages have been proxied by per capita GDP. As argued above this should bias our
results on the economic importance of market access downwards, if anything. This section
investigates this claim. Availability of manufacturing wages is very limited. There is data for
a subsample of countries containing average total manufacturing wages for 1995-1997 (in
1995-ECU) for the regions of Austria, Sweden, Finland, Belgium, Spain, France, the
Netherlands, Portugal, Italy and Ireland. A further problem arises when trying to calculate
wages per employee as data on manufacturing employment is not always available. I thus
present two sets of results. The first uses wages per capita where the denominator is total
population of a region, yielding 112 observations. The second set uses actual data on
manufacturing employment which reduces the number of observations to only 53 (no
employment data is available for Austria, Belgium, France, the Netherlands and Portugal).
Table 6 presents results for all three estimations. Column one contains estimates for a
regression of log of per capita GDP for 1995-1997 (in 1995 ECUs) on market access 19891991 for the 194 EU15 regions. As expected from above considerations, both the explanatory
power of the regression and the magnitude of the coefficient on market access is reduced.
Columns two and three shows the result for the wage estimations, where again, current market
access is instrumented by its 1989-92 value. Consistent with prior reasoning, the coefficient
on market access is indeed increased even though central high income countries as Germany
and Luxembourg had to be excluded (this may also explain the lower R2 of the regressions).
5.5
First Differences
Besides making predictions about wage levels, the theoretical model presented in section 3
also implies that changes in market and supplier access should trigger changes in wages. A
cursory look at the development of wage gradients in the European Union proved to be at
least not inconsistent with this implication. Indeed, market access of peripheral regions has
improved relative to that of central regions over the past two decades. This is mainly due to a
decrease in the importance of distance within the European Union (see the changes of the
distance coefficient “distint” in table 2). Figure 3 shows this development by plotting market
access growth rates against distance from Luxembourg.
The aim of this section is to investigate whether the cursory evidence presented so far stands
the test of more formal econometric investigation. To this purpose, column one of table 7
presents results of a regression of changes of per capita GDP on changes in market access.
For comparison, results from a fixed-effects estimation are displayed in column two. The first
differenced regression confirms the above impression that regions with larger market access
growth rates have indeed grown faster. However, the estimated coefficients are reduced
somewhat as compared to sections 5.3 and 5.4. This might be explained by the fact that
market access may influence per capita GDP through other channels, which may be of a more
long run nature than the reduction of value added through transport cost (e.g. Redding and
Schott (2003) point out the potential effect on human capital accumulation). Also, as the
importance of manufacturing industry declines as compared to the service sector, changes in
market access may have less impact on changes in per capita GDP, though the level of market
access is still important for the level of income. Finally, the decrease in the coefficient might
also be due to the reduction of omitted variable bias as time-constant determinates of per
capita income are eliminated through first differences or fixed effects.
Though the results of the different econometric experiments in this and the previous section
have reduced concerns about omitted variable bias, it could still be that regional income
increases are driven by changes in other variables which happen to be positively correlated
with market access growth and show considerable variation within countries. The next section
considers such variables.
Also, the force that drives the findings of this section is the decline in the estimates of the
distance coefficient within the European Union. As these estimates are derived from a gravity
equation using international instead of interregional trade flows, one might be concerned
about robustness. Thus, in the following I will also consider time-invariant variables that
might drive results of the preceding sections.
6
Inclusion of control variables
-
Density of employment: Ciccone (2002) shows for five European countries that
employment density raises productivity. For a start, I use population density instead of
employment density as the former is more readily available. Following Ciccone, I
instrument density by area size to control for the endogeneity arising from the fact that
high density might be a consequence, not a cause of high productivity. Table 8 presents
results for the period 1992-97 of a regression of log of per capita GDP on log of market
access, log of population density and country dummies, where I use 1980-85 log market
access and log area size as instruments.
-
Technological spillovers: available data for Nuts2-regions from Eurostat Regio are 1)
number of patent applications; 2) R&D-expenditure and R&D-employess by sector (less
complete than patent data).
-
Human and physical capital
7
Conclusion
Large differences in income levels remain in the European Union despite ever deeper
integration. This paper examines one potential explication why such differences are not bid
away by firms taking advantage of cheaper production costs. Distance from markets for final
goods and suppliers of intermediate goods may reduce the maximum amount of factor
remuneration firms can pay which will disproportionately affect immobile factors such as
labour.
From a New Economic Geography model, econometric specifications are derived which are
used to investigate the relevance of access considerations. Results indicate a significant role
for market and supplier access for the level of wages in the European Union. Also, improved
access of peripheral regions due to declining transportation costs seems to have had a positive
impact on wage levels there, as shown by a first differenced regression.
Though the preliminary results suggest an important role for market and supplier access, the
analysis has to be improved in several ways before a final verdict can be given. First, the
results of the trade equation should be confirmed by both estimations using interregional trade
flows and actual data on transportation costs, where available. Secondly, inclusion of a series
of control variables will help to reduce remaining doubts about potential biasedness of the
results due to omitted variables. Finally, it would be desireable to find a more precise proxy
for supplier access than simple GDP data which would allow to make more meaningful
statements about the importance of closeness to sources of intermediate inputs.
References:
-
Barro, R.J. and Sala-i-Martin, X. (1995):”Economic Growth”, New York, McGraw-Hill.
-
Brakman, S., H. Garretsen and Marc Schramm (2000):”The Empirical Relevance of the
New Economic Geography: Testing for a Spatial Wage Structure in Germany”, CESifo
Working Paper 395, Munich.
-
De Bruyne (2002), “The Location of Economic Activity: Is There a Spatial Employment
Structure in Belgium?”, mimeo, KULeuven.
-
Feenstra, R., R. Lipsey and H. Bowen (1997), “World Trade Flows, 1970-1992, With
Production an Tariff Data”, NBER Working Paper No. 5910.
-
Feenstra, R. (2000), “World Trade Flows, 1980-97”, University of California, Davis,
mimeograph.
-
Fujita, M., P. Krugman and A.J. Venables (1999):”The spatial economy: cities, regions
and international trade”, MIT Press, Cambridge MA.
-
Hanson, G. (1998):”Market Potential,
Concentration”, NBER Working Paper 6429.
-
Head, K. and T. Mayer (2002):”Illusory Border Effects: Distance Mismeasurement
Inflates Estimates of Home Bias in Trade”, CEPII Working Paper 2002-01.
-
Krugman, P. and A.J. Venables (1995):”Globalization and the Inequality of Nations”,
Quarterly Journal of Economics, p.857-880.
-
Mion, G. (2002): “Spatial Externalities and Empirical Analysis: The case of Italy”,
mimeo, Université Catholique de Louvain.
-
Overman, H.G. , S. Redding A.J. Venables (2001): “The Economic Geography of Trade,
Production, and Income: A Survey of Empirics”, CEP Discussion Paper, 508 and CEPR
Discussion Paper 2978.
-
Redding, S. and P. K. Schott (2003):”Distance, Skill Deepening and Development: Will
Peripheral Countries ever get rich?”, NBER Working Paper 9447.
-
Redding, S. and A.J. Venables (2000):”Economic Geography and International
Inequality”, CEPR Discussion Paper, 2568.
Increasing
Returns,
and
Geographic
APPENDIX A: DESCRIPTION OF DATA
Regional Data:
Data on GDP, population, area, wages and employment for the 211 Nuts2-regions are taken
from Eurostat’s Regio database. GDP and wage data are expressed in current ECU and are
deflated to 1995-ECUs (the deflators used are calculated by Eurostat as current ECU value
series divided by constant 1995 series for each country).
The main dependent variable used in the empirical analysis is per capita GDP. Due to data
limitations, the following adjustments had to be made:
-
The French Overseas Territories, the Portugal regions Acores and Madeiras and Eastern
Germany (including Berlin) are excluded completely.
Data for Austria, Finland and Sweden is only available between 1988-1997. These
countries are included in part of the analysis, only.
Data on Ireland, Luxembourg and Denmark corresponds to the Nuts0-level (countrylevel).
Distances between regions are great circle distances between the main cities of the regions.
EU12: Belgium, (West) Germany, Denmark, Spain, France, Greece, Luxembourg, Ireland,
Italy, Luxembourg, Portugal, United Kingdom.
EU15: EU12 plus Sweden, Finland and Austria.
International Data:
Country GDP from World Development Indicators. Countries included in the trade equation
and in the calculation of market and supplier access: …
Manufacturing trade flows in current USD from the NBER World Trade Database (Feenstra
et al., 1997; Feenstra, 2000). USD deflator from World Development Indicators 2001.
APPENDIX B: ESTIMATION RESULTS
Table 1: Per Capita GDP Gradient in the EU15
lcgdp95
dist100
-0.041
(7.26)**
Constant
10.023
(255.71)**
Observations
194
R-squared
0.36
Notes: Table shows result of regression of log of per capita GDP (in 1995-ECU) of EU15 Nuts2regions on distance from Luxembourg (in 100s of km). t statistics in parentheses based on HuberWhite heteroskedasticity robust standard errors.
* significant at 5%; ** significant at 1%
Table 2: Trade Equation Estimation
1980-85,
1980-85,
1986-91,
1986-91,
1992-97,
1992-97,
OLS
Tobit
OLS
Tobit
OLS
Tobit
lexp
lexp
lexp
Lexp
lexp
lexp
0.871
0.880
0.897
0.901
0.900
0.900
(46.25)**
(42.59)**
(54.11)**
(51.10)**
(62.15)**
(56.92)**
1.287
1.304
1.255
1.263
1.230
1.232
(60.44)**
(62.92)**
(65.57)**
(71.44)**
(68.69)**
(77.81)**
-1.050
-1.036
-0.977
-0.970
-0.959
-0.957
(9.12)**
(3.18)**
(8.71)**
(3.44)**
(9.29)**
(3.68)**
-0.794
-0.808
-0.713
-0.720
-0.774
-0.775
(11.26)**
(13.09)**
(10.64)**
(13.38)**
(17.03)**
(15.65)**
-20.046
-20.646
-20.611
-20.899
-20.419
-20.482
(18.21)**
(8.53)**
(19.92)**
(9.96)**
(21.68)**
(10.63)**
-22.403
-22.766
-23.076
-23.250
-22.114
-22.161
(28.85)**
(26.26)**
(32.51)**
(30.35)**
(33.87)**
(32.32)**
Observations
2486
2486
2486
2486
2486
2486
% zeros
2.9%
2.9%
1.5%
1.5%
0.5%
0.5%
R-squared
0.91
-
0.92
-
0.93
-
lpgdp
lrgdp
distint
distext
EUint
EUext
Notes: lpgdp and lrgdp are partner`s and reporter`s GDP (in logs), distint is ln(dist ij)*EUint, distext is
ln(distij)*EUext. Robust t statistics in parentheses.
* significant at 5%; ** significant at 1%
Table 3: Summary Statistics on Market and Supplier Access
Average fraction of MA
1980-1985
1986-1991
1992-1997
85%
82%
80%
61%
61%
59%
-
8.1%
11%
-
-2.1%
5.4%
derived from EU12
Average fraction of SA derived
from EU12
Average yearly growth rate of
MA between periods
Average yearly growth rate of
SA between periods
Table 4: Baseline specification of wage equation
Period
lMA
(1)
(2)
(3)
(4)
(5)
(6)
1980-85
1986-91
1992-97
1980-85
1986-91
1992-97
lcgdp
lcgdp
lcgdp
lcgdp
lcgdp
lcgdp
0.670
0.729
0.730
(15.14)**
(15.05)**
(15.10)**
0.909
0.987
0.970
(16.71)**
(16.73)**
(16.59)**
lSA
Constant
18.203
18.719
18.359
16.913
17.790
17.422
(31.29)**
(30.70)**
(31.82)**
(37.57)**
(36.03)**
(37.12)**
Observations
171
171
171
171
171
171
R-squared
0.56
0.55
0.57
0.58
0.58
0.59
Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
Table 5: Wage regression with instruments and country-dummies
lMA
(1)
(2)
(3)
lcgdp (1992-97)
lcgdp (1992-97)
lcgdp (1992-97)
0.724
0.361
0.739
(14.94)**
(3.40)**
(15.82)**
dummybe
14.040
(11.55)**
dummyde
14.277
(11.62)**
dummydk
14.602
(11.28)**
dummygr
13.598
(10.17)**
dummyes
13.840
(10.56)**
dummyfr
14.080
(11.19)**
dummylu
14.655
(11.99)**
dummypt
13.552
(10.21)**
dummyuk
13.850
(11.00)**
dummynl
14.052
(11.51)**
dummyit
13.925
(10.88)**
dummyie
14.126
(10.93)**
Constant
18.286
-
(31.62)**
18.466
(33.15)**
Observations
171
171
170
R-squared
0.57
0.98
0.57
Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
Table 6: Results for full EU15-sample and using manufacturing wages
(1)
(2)
(3)
lcgdp
lcwage95
lemplwage95
0.614
0.853
1.121
(8.91)**
(5.03)**
(2.87)**
18.737
20.333
27.011
(18.62)**
(8.01)**
(4.51)**
Observations
194
112
53
R-squared
0.37
0.24
0.21
lMA
Constant
Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
Table 7: First Differences and Fixed Effects
dslMA
(1) First
(2) Fixed
Differences
Effects
dslcgdp
lcgdp
0.195
(24.50)**
lMA
0.198
(32.25)**
Observations
342
513
R-squared
0.63
0.75 (within)
Robust t-statistics in parenthesis
* significant at 5%; ** significant at 1%
Table 8: Density Effects
lcgdp
lMA
0.258
(2.26)*
ldensity
0.072
(2.03)*
dummybe
12.938
(9.98)**
dummyde
13.178
(10.06)**
dummydk
13.500
(9.84)**
dummygr
12.500
(8.86)**
dummyes
12.727
(9.18)**
dummyfr
13.035
(9.81)**
dummylu
13.605
(10.49)**
dummypt
12.444
(8.86)**
dummyuk
12.718
(9.53)**
dummynl
12.939
(9.92)**
dummyit
12.818
(9.44)**
dummyie
13.089
(9.62)**
Observations
171
Robust t statistics in parentheses. Dependent variable is log of per capita GDP (in 1995 ECUs)
* significant at 5%; ** significant at 1%
Figure 1: Evolving Wage Gradients in the EU12
dcoeff
-.054867
-.061006
1980
1997
year
Notes: Figure displays distance-coefficient of a regression of log per capita GDP (in 1995-ECU) on distance
from Luxembourg (in 100s of km) for Nuts2-regions within EU12 (excluding Austria, Finland, Sweden). The
coefficients are all statistically significant at the 1%-level.
Figure 2: Market and Supplier Access and Distance from Luxembourg
.000011
SA
MA
.000511
2.2e-06
.000167
0
3044
Distance to Luxembourg (km)
0
3044
Distance to Luxembourg (km)
Figure 3: Average Yearly Growth Rates of Market Access and Distance from
Luxembourg
MAygrowth
.120557
.092514
0
3044
Distance from Luxembourg in km