Data and Descriptive Statistics

The data we use for estimating the specification stems from Cambridge Econometrics and the information about roads and travel times is GIS data which was provided by the Office for Regional Science, Planning and Geographic Information (RRG).

We convert the travel time matrix into a trade freeness matrix according to equation (8) and using η= 1.5. Comparing the parameters used in the literature (see Badinger and Tondl, 2003 as well as Fingleton, 2001) this value reflects a medium speed of distance decay. In Section 4.4.4, a sensitivity analysis with linear to high speed of distance decay will illustrate the robustness of our results. In order to allow a

straightforward interpretation of the estimated coefficients, we normalize the variables measuring accessibility (PΛ

j6=iφij,t) and market access (

PΛ

j6=iφij,texpj,t) by their means.

Table 4.2 shows the descriptive statistics of our data set.

Table 4.2: Descriptive Statistics

Mean Std. Dev. Min Max

(1) (2) (3) (4)

gva share service .496 .617 .007 6.148

gva share manufacturing .496 .546 .001 5.001

exp .496 .467 .005 4.205 pop. density .42 .982 .003 9.326 road .455 .624 0 6.019 PΛ j6=iφij 1 .72 .048 2.775 PΛ j6=iφijexpj 1 .747 .047 3.664 PΛ

j6=iφij,texpj,t1expj,t>expi,t .762 .638 0 2.766

Notes: The regional shares of gross value added in the manufacturing and service sector as well as the regional share

of household expenditure are measured in percent. By definition the mean of the service, industry, and expenditure share have to be equal. We define the gva share of sectork as gvaki

PΛ

igvaki

100. Hence, the mean over all i is given by

1 Λ PΛ i gvak i PΛ i gvaki 100

= 100_Λ , with 195 observations in the first time period and 204 over the last three time periods. This gives us 100₄ (₂₀₄3 +₁₉₅1 )≈0.496.

The regional share of gross value added in the manufacturing and service sector as well as the regional share of household expenditure are measured in percent. By definition, the mean over the shares is given by a constant which is approximately 0.5%. The population density is measured as population (1000s) per area (km2). As mentioned before, road growth over the previous years in percent is scaled by area. The variable for geographic accessibility and market access are both normalized such that the means have to be one. However, the mean of the interaction between the expenditure weighted accessibility and the indicator matrix, which is equal to one only if the expenditure in regioni is smaller than in region j, is obviously not equal to one, since it reflects only a part of the whole expenditure weighted accessibility, namely the part which describes the trade freeness to larger markets.

Figure 4.3 illustrates the expenditure weighted accessibility for the year 1999. It indicates a very clear core-periphery pattern which is of course mainly due to the simple geographic arrangement. Scandinavia, Portugal or Southern Italy are at the outer edge of the European circle which of course gives them the lowest average accessibility. Still when drawing a circle around the centroid of the European map (the Benelux approximately represent the centroid), we observe variance in accessibility. For instance, northern Italy and northern England have a higher expenditure weighted accessibility than eastern Spain or southern Scandinavia even though their distance

from the centroid is almost the same. It is not only the immutable geography, but also the quality of infrastructure and the expenditure shares which drive this variable.

Figure 4.3: _{Expenditure Weighted Accessibility}

Note: Due to a change in the regional classification we miss compatible travel time data for Denmark. Data source: RGG and Cambridge Econometrics Regional Database.

More important for our analysis are the changes in the accessibility over time. Since we include region fixed effects in our specification (see regression equation (9)), the effects we estimate are identified from changes over time. Everything that is time-invariant – as for example the immutable geography – will be absorbed by the region fixed effects. Figure 4.4 depicts the changes in accessibility over the time from 1999-2006. The depicted accessibility measure – for the purpose of focusing on pure infrastructure changes – is not expenditure weighted but reflects the raw trade freeness. Interestingly, we observe the highest growth in accessibility in the regions at the outermost points of the European map. Portugal, Greece, Scandinavia and Eastern Germany improved their inter-regional infrastructure the most, that is they were in the highest quantile of accessibility growth. This is striking as they were rather in the lower quantiles when looking at the accessibility level in figure 4.3. Of course it is easier to improve the

Figure 4.4: Change in Accessibility

Note: Due to a change in the regional classification we miss compatible travel time data for Denmark. Data source: RGG.

accessibility from a low level of average trade freeness but still the question remains whether this prominent growth in accessibility at the outer part of Europe will relocate economic activity from the center to the investing regions. According to our theory it should have considerable effects.