Salvador Barrios
email: salvador.barrios@ec.europa.eu.

Luisito Bertinelli
email: luisito.bertinelli@uni.lu.

Eric Strobl
email: eric.strobl@shs.polytechnique.fr.

Antonio Carlos Teixeira

Thierry Mayer

Giordano Mion

Agglomeration Economies and the Location of Industries: A Comparison of Three Small European Countries

Keywords: agglomeration, spatial autocorrelation, Belgium, Ireland, agglomeration, spatial autocorrelation, Belgium, Ireland, Portugal JEL classification: R12, C21, R30 agglomération, autocorrélation spatiale, Belgique, Irlande, die Vorwärts-und Rückwärtsverbindungen, die Forschungs-und Entwicklungsarbeit sowie die Konzentration von Fachkräften auf dem Arbeitsmarkt wichtige Determinanten für eine Agglomeration darstellen können, wobei dies aber im Wesentlichen vom jeweils untersuchten Agglomeration Räumliche Autokorrelation Belgien Irland Portugal JEL classification: R12, C21, R30 Aglomeración Autocorrelación espacial Bélgica JEL classification: R12, C21, R30

We investigate and compare the spatial distribution of manufacturing activity and its determinants in Belgium, Ireland, and Portugal using comparable, exhaustive micro-level data sets. We find some similarities between Portugal and Belgium, but little for Ireland. Moreover, there is some evidence that forward and backward linkages, R&D activity, and labour market pooling of skilled labor can be important determinants of agglomeration, although this crucially depends on the country examined.

It has long been recognized that economic activity is unevenly distributed across space. For example, [START_REF] Fujita | Economics of Agglomeration: Cities, Industrial Location, and Regional Growth[END_REF] illustrate this feature by pointing out that the Japanese core regions with a mere 0.18 per cent of the total area account for about 29 per cent of East Asia's GDP. There is also plenty of similar evidence for other regions in the world, for instance, the Blue Banana in Europe, the Manufacturing Belt in the US, industrial districts in Italy (Pyke et al. (1990)), Route 128 and Silicon Valley in the US (Saxenian (1996)). But are these examples the rule or the exception? And how general and how strong is the tendency of industries to agglomerate? While a now large body of theoretical studies have provided micro-foundations to explain why agglomeration may arise, theorists have pointed to the necessity of "buttressing [their] approach with empirical work" (Fujita et al. (1999, p.345)). Ideally, empirical studies would rely on micro level data in order to illustrate the mechanisms explaining how and why activities concentrate across space, and also provide results that are comparable across countries. Unfortunately, up to now, existing empirical studies on these matters have either used micro level data while being limited to one country, 1 or have used aggregated data at the macro/regional level in order to obtain cross-country evidence. 2 The present study is an attempt to push the analysis a step further by reconciling the use of micro level data and the need for comparative cross-country evidence. The objective of the present study is to provide thus a rigorous analysis of whether, how, and why agglomeration patterns of similar sectors differ across three European countries, namely Belgium, Ireland and Portugal. Specifically, we provide evidence for the agglomeration of manufacturing activities across space using exhaustive and comparable micro-data for three different countries, namely Belgium, Ireland, and Portugal. This type of empirical work is arguably important for the realm of location theory since it provides results that can help to gauge the relevance (or to invalidate) of existing theoretical models, as well as to indicate areas in which further theoretical analysis is called for. Importantly, the type of study undertaken here can also allows one to check if industry agglomeration forces are invariant across countries -in this case empirical research on different countries would be just about studying the same economic mechanism with different data -or differ significantly across countries -in this case empirical analysis should devote more attention to explaining country-specific features.

In particular, regarding the latter approach, one should note that the three countries under scrutiny arguably make for an interesting comparison group. On the one hand, these countries differ in aspects that may constitute important factors of the process of agglomeration. For example, while Portugal and Ireland are peripheral countries, Belgium is located in the core of the EU. Much of the theoretical literature about the impact of economic integration in the EU context has provided numerous results concerning the influence of economic integration on the location of economic activities. 3 Moreover, the countries under scrutiny differ considerably in terms of industrial expenditures devoted to R&D, the benefits of which have long been claimed to be bounded geographically. 4 Finally, Belgium, Ireland, and Portugal also have rather varied industrial labour force compositions as well as quite different institutional labour market settings which, for instance, may influence labour mobility and, by the same token, the location of economic activities. 5 One the other hand, these countries are similar in aspects that may influence agglomeration. For instance, they are all members of the European Union. This fact implies, inter alia, that they all have implemented the European Single Market Program, which provoked a strong need for modernisation and adaptation to increased competition with a consequent impact on the distribution of economic activities.

Moreover, these countries, because they have small open economies, have strong exportled sectors, which may well manifest itself in similar agglomeration patterns.

Last, but not least, our study is important to the location theory because, as Combes and [START_REF] Duranton | Testing for Localisation using Micro-Geographic Data[END_REF] have recently pointed out, ``a deeper understanding of the similarities among industries of European countries may have profound implications for on going research because one needs to be able to understating those patterns with reference to a large body of existing literature (mostly based on US data''. To our knowledge, this is the first study that explicitly addresses the important issue of crosscountry comparison using a unified microeconomic dataset.

Our analysis of the patterns of agglomeration in these three countries relies on the index developed by [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] -from now on referred to as the EG index. Importantly, this index, unlike more traditional measures, has explicit theoretical foundations and is based on (plant-level) microeconomic behaviour. Moreover, once plant-level data is available, [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] claim "the index is designed to facilitate comparisons across industries, across countries, or over time" (Ellison and Glaeser (1997), p.890). Despite this assertion, no explicit comparative study across countries has been undertaken so far. Rather, existing applications of the EG index (and of its extensions) have been limited to the intersectorial comparisons across studies using different aggregation levels (see [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] for the US as well as the study by [START_REF] Maurel | A Measure of the Geographic Concentration in French Manufacturing Industries[END_REF] for the French case). An important limitation of this index is that it is based on the distribution of activity over discrete spatial units and, consequently, if agglomeration forces are not geographically bounded and extend to neighbouring areas, the index will be biased downwards (and detect less agglomeration) the smaller the size of the area considered. This issue relates to the so-called Modifiable Area Unit Problem (MAUP) and more specifically to the choice of the appropriate spatial unit. We thus also use t the Moran's I statistic to reduce this scale problem by selecting a high spatial level when autocorrelation is detected in small spatial units. While the Ellison and Glaser index is complemented with an indicator of spatial autocorrelation, the Moran I's statistic, its measurement of agglomeration is built on a discrete version of space. Since space differs from one country to another ,one cannot exclude that marked agglomeration similarities or dissimilarities among countries reflects some spurious effect due to the application of the EG index to countries with different internal geographies 6 .

Of course, whatever patterns of concentrations are established, it is also important in any analysis to seek to understand the underlying forces driving these. To this end, we gather a consistent set of explanatory variables across countries and sectors aiming to proxy the different theoretical explanations as to why agglomeration of economic activities occurs. The main theoretical arguments can roughly be classified according to so-called Marshallian externalities (for a survey, see [START_REF] Duranton | Micro-foundations of urban agglomeration economies[END_REF]). More specifically, Marshall's first theory claims that the existence of scale economies in input production could induce firms to locate close to their inputs. Elaborating on Marshall's concept, the New Economic Geography literature stipulates that such input-output linkages, alongside increasing returns, may give firms an incentive to locate near markets (and near their suppliers) in order to save on transport costs. In this context, market-size effects expand in a self-reinforcing process of agglomeration as if plants agglomerate for the reason stated above, this will in turn attract more firms (see [START_REF] Fujita | Economics of Agglomeration: Cities, Industrial Location, and Regional Growth[END_REF].

Labor market pooling may also be an agglomerative force because industrial agglomeration should encourage the formation of thick local labor markets so that employees could find it easier to match with employers and vice-versa (see [START_REF] Helsley | Matching and Agglomeration Economies in a System of Cities[END_REF][START_REF] Helsley | Matching and Agglomeration Economies in a System of Cities[END_REF][START_REF] Monfort | Local Labor Markets, Skill Accumulation and Regional Disparities[END_REF][START_REF] Monfort | Local Labor Markets, Skill Accumulation and Regional Disparities[END_REF]. Finally, knowledge spillovers could lead to agglomeration because they can generate externalities that are bounded geographically, increasing the productivity of firms in the region where the new economic knowledge was created. Firms may thus locate where they are likely to learn from other firms (see [START_REF] Henderson | The Sizes and Types of Cities[END_REF][START_REF] Henderson | Urban Development: Theory, Facts and Illusion[END_REF], and Glaeser 1999).

Nevertheless, despite these numerous theoretical contributions, evidence concerning the determinants of agglomerations also remains scarce and relatively recent.

In particular, among the few, [START_REF] Dumais | Geographic Concentration as a Dynamic Process[END_REF] decompose the index developed by [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] to determine the contribution of plant entry and exit on agglomeration. [START_REF] Holmes | Geographic Concentration and Establishment Scale[END_REF] also decompose an index of agglomeration, isolating the impact of establishment scale on agglomeration. [START_REF] Barkley | Industry Characteristics linked to Establishment Concentrations in Nonmetropolitan Areas[END_REF], Rosenthal andStrange (2001) andTeixeira (2002), in contrast, shed light on the causes of agglomeration by using standard parametric estimation techniques. In an approach similar to these latter three studies we, in the current paper, also try to disentangle the The rest of the paper is organized as follows. In Section II, we provide some details about the tools being used to analyze the patterns of agglomeration. Section III proceeds with a description of our data sources. Observed patterns of agglomeration using these data and tools are presented and discussed in Section IV. Section V explores the sources of agglomeration econometrically. Section VI concludes.

SECTION II: Measuring Agglomeration [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] proposed an index of spatial concentration, which has the desirable feature of neutralizing the possible influence of industrial concentration resulting from internal economies of scale. Its expression for a particular industry i is given by:

( ) ( )( ) i c c i c c i i H x H x G - - - - = ∑ ∑ 1 1 1 2 2 γ
where G i is a measure of geographic concentration, defined as the sum of squared deviations of s ic (the share of industry i's employment in area c) to x c (the share of aggregate manufacturing employment in area c), i.e.,

(

)

2 ∑ - = c c ic i x s G . The term ∑ = j ij i z H
2 represents the classical Herfindahl-Hirschman index defined as the sum of squared plant employment shares of industry i, with j=1...N, the plant-indices. In a situation where there is random location, random market shares and the number of plants in a sector increases, H i tends to 0. However, it must also be noted that the EG index suffers from two major drawbacks:

Firstly, as noted by the authors themselves, the major theoretical limitation is that the EG index cannot distinguish between spillovers and natural advantages to explain the reasons why plants agglomerate. This is simply due to the fact that in their location model there is an observational equivalence between natural advantages and spillovers. In this regard one should add that it also is not able to distinguish home market effect or any other mechanism, other than scale economies, leading to agglomeration. Following the original authors and other subsequent researchers using the EG index, we are thus also not able to investigate this aspect any further.

A second and potentially more important issue is related to the choice of spatial unit, the so-called Modifiable Areal Unit Problem (MAUP). 7 Theory tells us that pecuniary externalities may reach wider geographical areas than technological externalities (see for instance [START_REF] Lamorgese | Space, Factors and Spillovers Banca d'Italia[END_REF] for a discussion on this issue). This seems thus to imply that while all externalities are important at small spatial scales only pecuniary externalities play a decisive role on agglomeration at high spatial scales. Clearly, because these externalities have different geographic scales, a much denser agglomeration may result from technological externalities that require strong interactions between agents in activities where information is more important than from pecuniary externalities that can also arise through inter-regional trade.

Since the EG index is based on the distribution of activity over discrete spatial units and if, as discussed by [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF], agglomeration forces are not geographically bounded and extend to neighbouring areas, then the index will be biased downwards (and detect less agglomeration) the smaller the size of the area considered.

The use of Moran's I is a good solution to reduce the scale problem by choosing the regional rather that the local level when autocorrelation is detected 8 . Spatial autocorrelation considers the possibility that observations of a variable (here the location of industries) may not be independent across space. In this regard, clusters of events, people, or facilities are likely to be affected by positive spatial autocorrelation, whereas negative spatial autocorrelation refers to arrangements where people, events or facilities are dispersed.

The Moran statistic compares the value of a continuous variable at any location with the value of the same variable at surrounding locations. Formally, it is defined as:

( )( ) ( ) ∑ ∑ ∑ - - - ⋅ = ′ ′ ′ ′ c c c c c c c c x x x x x x w S N I 2 with ∑ ∑ ′ ′ = c c c c w S , N x x c c ∑ =
and c≠c', where x c represents the value of the observation in region c, N is the total number of observations, x is the mean of the variable across all observations and w cc' is a weight between region c and region c'. Our w cc' has been set equal to 1 when regions c and c' are contiguous and 0 elsewhere. Values of I significantly larger than the expected value of the Moran statistic, [ ] ( ) data had been collected so we took ISIC Rev.1 as the common classification for which no changes for Portugal data was required. 10 After having put this data together and keeping only those sectors that were common to the three countries, we ended up with 63 common four-digit ISIC sectors, which represent between 85 to 95 per cent of total manufacturing employment for these three countries.

In terms of the choice of spatial units the task of unification was more cumbersome. The only common spatial unit at the European level is the Nomenclature des unités territoriales statistiques/Nomenclature of territorial units for statistics (NUTS), where NUTS is a five-level hierarchical classification (three regional levels and two local levels). 11

However, the regional classification available to us did not always correspond to this spatial classification, especially for Ireland. As a result, our choice of spatial units was essentially restricted by data availability. In the end two spatial units were used, one regional and one local. For Belgium, NUTS3 (43 arrondissements) and NUTS5 (589 communes) spatial units have been selected. In the Irish case, at the regional level, NUTS4

(27 counties) spatial units were chosen, whereas for the local level an intermediate level between NUTS4 and NUTS5 (504 townships) spatial units was used. 12 Finally, for Portugal, no analogous regional level was available, therefore we used a somewhat rougher spatial unit than NUTS3 for the regional level, namely districts (18 distritos), and for the local level NUTS4 (275 concelhos-municipos). 13 It is also worth noting that some descriptive statistics on the sectoral share of employment show that specialization patterns are rather different across countries.

Portugal appears to be more specialized in more traditional industries like textiles and wood industries, while Ireland highlights an important share of its employment in hightech industries such as machinery equipment. The picture is, in contrast, quite mixed for Belgium, as shown in Table 2.. As noted earlier, in terms of calculating the EG index we have two spatial units at our disposal, the regional and the local level, and we initially calculated the EG index using both of these. Doing so we found that the correlation between EG indices at the regional level and at the local level is relatively high in Belgium and in Portugal (statistically significant spearman rank correlation coefficients of 0.7 and 0.5, respectively), but very low for Ireland (0.1 and not significant) 14 . This would suggest that the underlying pattern of agglomeration differs strongly, depending on the spatial units employed, and perhaps that the analysis should have proceeded using both comparatively. However, as noted earlier, one concern with regard to the EG index is the potential bias introduced via spatial autocorrelation, which is likely to be larger for smaller units. To examine this we calculated in Table 3 the Moran index for local spatial units using binary decay effects based on contiguity matrices. Except for Ireland, one finds that about a third of all sectors are characterised by positive and statistically significant spatial autocorrelation. 15 Clearly this represents a non-negligible number of sectors and suggests that some care should be taken when interpreting EG index results obtained from local spatial units. We thus henceforth proceed using the regional rather than the local level as our spatial unit.

Our results on the EG index at the regional level for the three countries are given in Tables 4-6. When comparing results among countries, a striking feature is that Portugal's level of agglomeration is much higher than for Belgium and Ireland. More precisely, the weighted average EG index is 0.027 for Belgium and 0.038 for Ireland, whereas for

Portugal it stands at 0.133. 16 In the case of Portugal, one obvious reason why the average EG index is higher is related to the fact that the industrial concentration, measured by the Herfindahl-Hirschman index, is quite low in Portuguese industry (see Barrios et al., 2005).

Whereas the Portuguese aggregate level of concentration clearly departs from the Belgian and Irish ones, the picture changes when it comes to comparing the patterns of agglomeration of individual sectors. Taking the rank correlations in Portugal, and to a lesser degree, in Ireland. As a potential explanation one should note that since most of these products are perishable, it is likely important for their producers to locate close to their consumption market. In a way, these results also confirm the given that there are indivisibilities in production, there is also a trade off that producers have to solve -the less constraining indivisibilities are, the more dispersed activities will be relative to all other activities.

A further interesting observation is related to high-tech sectors. 17 We find that high-tech sectors have a lower average EG index than the rest. This result is consistent across the three countries under scrutiny. One should note that similar results have also been found using German [START_REF] Alecke | New Empirical Evidence on the Geographic Concentration of German Industries: Do Clusters Really Matter?[END_REF]) and UK data [START_REF] Devereux | The geographical Distribution of Production Activity in the UK[END_REF]).

Three factors, not necessarily mutually exclusive, may explain such a finding. First, the widely held belief that well known high-tech clusters, such as Silicon Valley in the US or Sofia Antipolis in France, are representative of general agglomeration processes is untrue.

A second explanation derives from the possibility that our sectorial disaggregation is too rough to capture very specific high-tech clusters. 18 Finally, it may be due to the fact that our choice of spatial unit is not appropriate to identify high-tech clusters. Indeed, taking the Belgian local level spatial units, the average high-tech EG index is 0.0035, and 0.026 for the non high-tech sectors. Thus, whereas for non high-tech sectors, the EG index has been halved, it has tripled for high-tech sectors. Still, agglomeration of high-tech sectors remains far below the rest of the manufacturing industry.

Ellison and Glaeser (1997) use a non-statistically based value range of the EG index according to which they classify sectors as not very concentrated (smaller than 0.02), relatively concentrated (between 0.02 and 0.05), and highly concentrated (larger than 0.05). However, using the variance of the index across sectors it is straightforward to check whether sectors are statistically significantly concentrated by using the two variable is roughly normal, so that 95 % of its realizations are around the expectation, and in the interval limited by two standard deviations, below or over it. More precisely, since the expected value of the EG index is zero one can check whether the absolute values of it for an individual sector is larger than twice the standard deviation of the index under the null hypothesis of random location. Since [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF] give the analytic expression of the variance of their index, it seems quite obvious to use this rule. The main thing is to use the correct variable: The EG mean and its analytical variance hold under the hypothesis of random distribution of the firms of a sector.

We denote the significant sectors in this regard using an asterisk in Tables 456.

Accordingly, about 25 per cent of the EG indices are found to be significant in the Irish, 49 per cent in the Belgian, and 75 per cent in the Portuguese case. 19 One may want to note that in this regard the number of significant sectors rises with population size of the countries. Also unreported results on the standard deviations reveal that the variance of the EG indices decreases with country size. This result immediately proceeds from the expression of the variance of G i used by Ellison and Glaeser (1997, p.907), which increases with the Herfindahl-Hirschmann index. Thus, as smaller countries tend to have higher industrial concentration due to fewer firms, they will also have larger Herfindahl-Hirschmann indices. As the variance of the EG index depends positively and linearly on the variance of G i , everything else being constant, smaller countries will have a larger variance of their EG indices. This result is consistent with unpublished results on Italy, where about 90 per cent of the EG indices turn out to successfully pass the two standard deviation rule. 20 The same is also true for [START_REF] Maurel | A Measure of the Geographic Concentration in French Manufacturing Industries[END_REF] who work on

French data and find that for their index (which is very close to Ellison and Glaeser's), 270 out of 273 sectors are statistically significant. A major issue in doing so is to assemble the desired control variables using comparable data across countries and we use mostly Eurostat data in this regard. 21 Our chosen explanatory variables are as follows:

Total purchases of goods and services (total inputs): this variable, expressed in millions of Euros, includes the value of all goods and services purchased during the accounting period for resale or consumption in the production process (excluding capital goods).

The goods and services concerned may be either resold with or without further transformation, completely used up in the production process or, finally, be stocked.. This variable is intended to capture the forward and backward linkages that have become important in the so-called New Economic Geography type models of agglomeration (see [START_REF] Fujita | Economics of Agglomeration: Cities, Industrial Location, and Regional Growth[END_REF]. Accordingly, producers may want to choose locations that have good access to large markets and to suppliers of intermediates, while a concentration of producers tends to offer a large market and a good supply of inputs and consumer goods, hence attracting new producers and new consumers. A positive effect on agglomerations is thus expected from this variable.

Sectorial share of skilled works: this variable measures the percentage of the total population that has obtained a higher education diploma and stems from the respective employers and employees (see [START_REF] Helsley | Matching and Agglomeration Economies in a System of Cities[END_REF][START_REF] Helsley | Matching and Agglomeration Economies in a System of Cities[END_REF][START_REF] Monfort | Local Labor Markets, Skill Accumulation and Regional Disparities[END_REF][START_REF] Monfort | Local Labor Markets, Skill Accumulation and Regional Disparities[END_REF]. An increasing effect on agglomeration is thus expected from this variable. Feldman (1996), [START_REF] Feldman | Innovations in cities: science-based diversity, specialization and localised competition[END_REF], Bottazzi and Peri (2003) and [START_REF] Jaffe | Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations[END_REF]) that knowledge spillovers could lead to agglomeration because they may generate externalities that are bounded geographically, increasing the productivity of firms in the region where the new economic knowledge was created. Hence, one might expect R&D intensive sectors to concentrate in order to fully benefit from these knowledge spillovers.

Purchases of energy products: this variable, expressed in millions of Euros, includes energy products only if they are used as fuel, and hence excludes energy products purchased for resale without transformation. To derive their index, [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF], start from a model, where agglomeration either results from externalities or common natural advantages. Although scholars have mainly been interested in the former explanation in order to provide a rationale for Silicon Valley-type of agglomerations, natural advantages play a crucial role in explaining the distribution of manufacturing activity, especially when the concerned industries are natural resources intensive. For instance, [START_REF] Ellison | The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?[END_REF] show that for the US manufacturing activity, about 20 per cent of observed geographic concentration can be explained by a small set of natural advantages in the United States. Here we consider energy as a generic variable to account for natural advantages. Ideally, one would have liked to include purchases of natural resources as well, but no comparable data is available for the three countries. Note however that in [START_REF] Ellison | The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?[END_REF], they use 3 energy variables (electricity, coal, gas) in their regression that are all statistically significant.

Gross investment in tangible goods: expressed in millions of Euros. Included are new and existing tangible capital goods, whether bought from third parties or produced for own use (i.e., capitalised production of tangible capital goods), having a useful life of more than one year including non-produced tangible goods such as land. Purchased goods are valued at purchase price, i.e. transport and installation charges, fees, taxes and other costs of ownership transfer are included. Own produced tangible goods are valued at production cost. The introduction of this variable follows the same idea as for the purchase of inputs and hence one would have an a priori positively expected impact on agglomeration.

Gross investment in machinery: this variable, available in millions of Euros, covers machinery (office machines, etc.), special vehicles used on the premises, other machinery and equipment, all vehicles and boats used off the premises, i.e. motor cars, commercial vehicles and lorries as well as special vehicles of all types, boats, railway wagons, etc.

acquired new or second hand during the reference period. Current maintenance costs are excluded.

Average plant size: this is the ratio of the number of persons employed (per 1000) by the number of enterprises (i.e., all units active during at least a part of the reference period). In a recent contribution, [START_REF] Holmes | Geographic Concentration and Establishment Scale[END_REF] show that there is a strong link between plant size and agglomeration. Plant size may also be interpreted as a proxy for the presence of increasing returns to scale, which are central in the models of the New Economic Geography.

As countries are of very different sizes, all variables listed above have been weighted

by the number of workers in each sector of the individual countries. We also experimented with total production weights, but this led to qualitatively analogous results. One should note that for some variables and some countries, information at the four-digit sectorial level was not available so that we had to fill these gaps by using their three-digit counterparts. Details of this are provided in the data appendix.

We first used simple OLS for each of our three country samples separately, as

shown in Table 8. The first notable feature of these is that our econometric specification explains little for Ireland, displaying a very a low R-squared and insignificance for all control variables of interest. 22 In contrast, the R-squared is substantially higher for both Belgium and Portugal. Nevertheless, for Belgium only total input purchases is significant, displaying the a priori expected positive sign. A similar result on purchased inputs is also found for Portugal, suggesting that backward linkages may be important.

For Portugal we also find that R&D expenditures play a significant positive role in explaining agglomeration, thus indicating that there are benefits to agglomerating in sectors where there are externalities generated through innovative activity. Finally, under OLS one also discovers that education in Portugal serves as a positive determinant of the localisation of activities, suggestive of the possibility of the importance of labour market pooling. In terms of our other, non-significant variables, one may want to note that some of them do have the expected sign. For instance, in congruence with our for large firms in the US case. Last, neither for the US case, nor for the three countries under scrutiny in our sample there is evidence for agglomeration in sectors highlighting reliance on energy resources.

If one assumes the relevance of our empirical specification of the determinants of agglomeration despite the statistical insignificance of many of these, it may be of interest to try and disentangle how much differences in agglomeration across countries is driven by differences in the means of the determinants rather than their relative importance across countries. A convenient method to examine this can be borrowed from the labour literature due to Oaxaca (1973). Accordingly, if we consider two countries i and j and the estimates of β from regressing the EG index on a number of covariates using OLS for each separately, then the difference in the means of the EG index, EG i -EG j , between these two countries can be decomposed into:

EG i -EG j = (β i -β j )X i + β j (X i -X j ) F o r P e e r R e v i e w O n l y
where X is a vector of the means of the control variables and β is the estimated OLS coefficient appropriately subscripted for countries i and j. The first term is the part of the difference in means due to differences in the response of agglomeration to the determinants measured at the mean of country i's determinants. In contrast, the second component serves to measure any difference in the means of the determinants across the two countries measured at the 'returns' (i.e;, for a given β j ) of country j.

We executed this decomposition using the OLS estimates of our specifications along with the variable means for the three country pairs and report results in Table 9. As can be seen, the difference in means of EG index is highest between Portugal and Ireland, and this difference is both about equally due to higher level of mean of the determinants as well their greater responsiveness in Portugal. In contrast, while the difference in average agglomeration between Portugal and Belgium is nearly as high as between the country-pair above, the factors underlying this difference are substantially different.

More precisely, while the difference in the means of the determinants acts to decrease agglomeration relatively more in Portugal, this is counteracted by the EG index's greater (positive) responsiveness to these. A similar pattern also emerges when one compares the relatively similar means of agglomeration for Belgium and Ireland -while the means of the determinants in Ireland have a greater positive influence, its level of agglomeration is less (positively) responsive to these. It follows that, while the econometric results presented in Table 8 appear to explain part of the agglomeration patterns observed in each of the three countries considered separately together with existing differences in agglomeration forces across countries, a closer, bilateral, analysis of these differences shows that no uniform patterns emerges as to whether differences in agglomeration are due the responsiveness of agglomeration to the determinants considered or to the difference in levels of the determinants of agglomeration considered in Table 8. Finally, some remarks concerning the robustness of our results are in order. In particular, the estimated coefficients obtained in Table 8 are not always completely robust and this thus requires further clarification. First, there is the problem of data quality, in particular, our explanatory data, which stem from the Eurostat Regio database are not of an ideal quality. More precisely, as discussed in more detail in the appendix, on several occasions we needed to resort to alternative methods to fill any 'gaps' or otherwise would have been faced with severe sample size problems. Besides the data quality issue, there is more fundamentally the problem of whether proceeding with crossindustry regressions is the right way to capture the determinants of agglomeration.

Indeed, by its very nature, there is no spatial dimension in the EG index. If, however, such factors as inputs, R&D, and energy production are very concentrated spatially and the sector under scrutiny relies heavily on these inputs, R&D, energy source, then the production should be very concentrated. In order to check this rigorously, one would then need sector-region share-regressions, rather than regressions on sector-specific EG indices. This may then explain poor results of our regressions, and on these types of regressions in general, as can be deduced from Rosenthal and Strange ( 2001)'s analysis.

SECTION VI: Conclusion

This paper analyses the spatial distribution of manufacturing activity and its determinants across three European countries: Belgium, Ireland, and Portugal. To our knowledge, this is the first comparative study of the location of manufacturing activities using plant level data across countries. Our analysis mainly relies on the index developed by [START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF], which has been designed to allow for comparisons across industries and countries. Results confirm some previous findings on French, UK and US data, with traditional sectors ranking among the most agglomerated. Among the three We also econometrically attempted to investigate potential determinants of the observed agglomeration patters as suggested by theoretical contributions. Our results suggest, tentatively, that one may want to be careful when conducting cross-country comparisons of the spatial distribution of economic activities, as there may be a lot of country-specific factors that can drive the findings. More specifically, while we find some evidence that backward linkages, innovative activity, and the pooling of skilled labour can act to increase agglomeration, this result crucially hinges on which country is being examined. When compared with the available evidence for the US, our results also suggest that some industry agglomeration forces are invariant across very different economies. Furthermore, while the variables mentioned above appear to explain part of the agglomeration patterns observed in each of the three countries, a closer examination of cross-country differences in results shows that no uniform patterns emerges as to whether differences in agglomeration are due the responsiveness of agglomeration to the determinants considered here or to the difference in levels of these determinants. which can be at different locations. The locations, reflecting the spatial units used, take place at the municipalities (NUTS 5) and districts levels. Portugal is divided into 275 municipalities and 18 districts.

F

Eurostat data

Treatment of missing data

Whenever data was missing or unavailable at a given sectorial level, we extrapolated with data from the next available aggregation level. Only for R&D expenditure in Ireland, no data was available. In this case, we had recourse to national data sourcesnamely the Annual Business Survey, which is a survey of plants larger than 10 employees and covers about 80 per cent of the total population of these. Henceforth, we studentized this variable so as make cross-country pooled regressions feasible.

Belgium:

Sectors (nacecode) with no data : 1592,192,193,242,246 Sectors with missing data : 1591 (inputs, energy, R&D exp.); 1753, 1754, 231, 232 (inv. in tang.)

Ireland:

Sectors (nacecode) with no data : 1591, 1596, 1597, 1598, 182, 191, 192, 23, 232, 246, 247, 265, 266, 271, 272, 273, 282, 2912, 334, 36,362, 365, 366 Sectors with missing data : 154, 158, 262, 263 (inputs, inv. in tang., inv. in , 334, 36,362, 365, 366 Sectors with missing data : 154, 158, 262, 263 (inputs, inv. in tang., inv. in

  agglomeration found by running parametric estimations and using as dependent variable the EG index and a number of other data intended to proxy the various theoretically proposed determinants of agglomeration.

  potential story, popular in the economic geography literature. Accordingly, industries will choose their location by taking into account distance to and size of the customers. Ideally, production would then have to settle near each consumer. However,

  deviation rule. The two-standard deviation rule supposes the studied random

  the introduction, there are many theoretical but few empirical analyses of the determinants of agglomeration. To provide an empirical analysis of the determinants of agglomeration for our three countries we proceeded in the spirit of the studies of Kim et al.(2000),Rosenthal and Strange (2001), and Teixeira (2002) and estimate a simple regression where the EG index is regressed on a number of variables intended to proxy the various theoretical explanations put forward in the theoretical literature on agglomeration.

  . It is intended to capture labor pooling effects, which is viewed in the literature as an agglomerative force because it allows for easier matching between

  , Ireland clearly stands out with significantly different patterns of spatial distribution of manufacturing activity. More precisely, despite being two peripheral countries, Ireland and Portugal appear to have rather different distribution of industries across space.

  Thus the EG index tends to the measure of

	geographic concentration G i times an industry invariant term, and its mathematical
	expectation tends to 0.
	An important aspect of the EG index with respect to the current study is its
	distinctive feature of being comparable across time and sectors regardless of plants' size
	distribution. A further important property of the EG index lies in its theoretical
	foundation as it is based on a location model, i.e., where firms choose location following F a Bernoulli process according to the presence of natural advantage and/or spillovers. The o r EG index thus asks the question whether concentration is greater than would be
	P expected to arise randomly.
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

Table 7 ,

 7 Irelandappears to clearly diverge from Portugal and Belgium, whereas the two latter have a positive (although not very high) and statistically significant spearman rank correlation coefficient. Several explanations can be put forward possibly explaining this result. First, two countries. In particular, both countries have strong export-led economies, as it is generally the case of small open economies. As has been shown elsewhere, exporting firms tend to be larger on average and have different patterns of agglomeration(Bernard and Jensen (1995),[START_REF] Holmes | Geographic Concentration and Establishment Scale[END_REF]). It should also be noted that FDI has played a large role with multinationals increasingly choosing Ireland in order to get access to the EU market(Barry and Bradley (1997)). Multinationals' presence may, in turn, have influenced the location pattern of industries if externalities arise between domestic and foreign plants (seeBarrios et al. (2006) for evidence concerning Ireland).

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y
	Dairy Products (3112), Slaughtering, Preparing and Preserving Meat (3111), Grain Mill
	Products (3116), and Manufacture of Food Products, N.E.C. (3121) in Belgium and

whereas Belgium and Portugal have roughly similar population levels of around 10 million, Ireland has 3.5 million. Smaller population size in turn implies fewer plants, meaning that agglomeration patterns are harder to disentangle for particular sectors. Put differently, few plants imply that the marginal impact of one plant's location on the EG index is more important, which could well explain our results. A second explanation relates to fact that Ireland is an island, hence the centrifugal and centripetal forces at work are likely to act differently. In particular, cross-border effects, which are likely to be important particularly for Belgium, but also to a lesser extent for Portugal, are obviously not relevant in the case of Ireland. That is, Belgium is located at the heart of Western Europe and is geographically close to countries such as France, Netherlands, and Germany with intense trade relationships with all of these. One thus could easily expect that industry clusters exist across borders, an example of which are the traditional steel industries, which are common to French and Belgium bordering regions. Another feature, common to both Ireland and Portugal, is the peripheral location with respect to the rest of Europe. Coupled with the fact that these two countries entered the European Common Market relatively late (in 1973 for Ireland and 1986 for Portugal), one might thus expect to find common features of agglomeration across these Comparing the sectorial indices individually within and across countries also reveals a number of interesting patterns. As shown in Tables 4-6, the most agglomerated sectors in Belgium and Portugal are Misc. Products of Petroleum and Coal (3540), Shipbuilding

and Repairing (3841)

. This may not be very surprising given that this sector is obviously very dependent on location. It thus is supportive of Heckscher-Ohlin type of models, where trade and specialization depends on the factor endowments of countries/regions.

We also find that traditional sectors as Tanneries and Leather Finishing (3231), Manufacture of Wood and Cork Products NEC (3319), Jewelry and Related Articles (3901), Pottery, China and Earthware (3610), Knitting Mills (3213), Manufacture of Carpets and Rugs (3214) appear to be highly agglomerated, especially in Portugal, but also in Ireland.

[START_REF] Ellison | Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach[END_REF]

and

[START_REF] Maurel | A Measure of the Geographic Concentration in French Manufacturing Industries[END_REF]

find similar results for the US and France, respectively. In contrast, we discover that sectors related to food and beverages tend to be dispersed. This is particularly true for Manufacture of

  Total intra-mural Research and Development expenditure: this variable is expressed in millions of Euros. Intra-mural expenditures are all expenditures for R&D performed within the unit, regardless of the source of funds. It must be distinguished from expenditures for a wide range of related activities. The following are therefore excluded:

expenditures on education and training; expenditures on other scientific and technological activities; expenditures on other industrial activities; expenditures on purely financing activities. Intra-mural expenditures are valued at production cost and include all operating costs including the labour cost and capital expenditure. R&D typically captures knowledge spillovers. It has been shown elsewhere (see for instance Audretsch and

  The database that has been used for the present study covers all plants established in Belgium in 1998. For each plant, we have the number of jobs, the industry it belongs to (up to five-digit classification), and the township it is located in. Employment data come from the national office for social security (ONSS), which collects employment data for all wage earners in Belgium. The notion of a plant is clearly distinguished from the employer, the latter corresponding to the notion of a firm in a general sense. If the employer has only one activity in one location, then it is considered a plant. But if the employer carries out its activity in two or more locations (branches or operation units) Information at the plant level include time invariant variables such as the nationality of ownership, sector of production, and detailed regional location of each plant, as well as the level of employment in each year. of activity, sales, ownership structure and number of employees as well as worker's age, skill, occupation, tenure and schooling. A company may be a single or a group of plants,

	Appendix: Data
	Belgium
	Social Security data on employment
	o r F o r F o r F o r F o r F o r
	P P P P P P
	e e e e e e
	e r e r e r e r e r e r
	R R R R R R
	e e e e e e
	v i e v i e v i e v i e v i e v i e
	w w w w w w
	O n l O n l O n l O n l O n l O n l
	y y y y y y

and/or carries out two different types of activity, each operation unit is seen as a separate plant. However, if several operation units of the same firm are located in the same township, only one plant is taken into account.

Ireland

Forfás Employment Survey

This is an annual plant level survey collected by Forfás since 1972, the policy and advisory board for industrial development in Ireland. The response rate to this survey is argued by Forfás to essentially be nearly 100 per cent, i.e., our data can be seen as including virtually the whole population of manufacturing plants in Ireland.

Portugal

Ministry of Employment

This is an annual plant level survey collected by the Portuguese Ministry of Employment since 1985, for all the companies operating in Portugal. It matches employers and employees and reported data include the companies' location, age, sector

  Note: areas are average ones. One square kilometer corresponds to 0.3861 square miles.

	Table 1a: Average Areas
		Regional level	Local level
	Belgium	43 arrondissements: 710skm	589 communes: 52skm
	Ireland	27 counties: 2603skm	504 Townships: 139.4skm/ 3445 DEDs: 20.4skm
	Portugal	18 distritos: 4887skm	275 concelhos: 320skm
	United States	51 States: 70322skm	3141 Counties: 1142skm
	F o r
		P
		e
		e r
		R
		e
		v i e
			w
	energy); 262, 263, 364 (inputs, inv. in tang., energy)	O n l	machines,
	Sectors with only zeros : 1592, 176, 231		y
	Portugal
	Sectors (nacecode) with no data : 1597
	Sectors with missing data : 1592, 314 (inv. in tang., inv. in machines, R&D exp.)
	Sectors with only zeros : 231

Table 1b

 1b

	Table 2: Share of total manufacturing employment
		ISIC 2-digit	Belgium Ireland Portugal
	: Average Population (Density) Manufacturing of food, beverages and tobacco	31	14.19% 19.81% 10.38%
	Population (in 1000) Textile, wearing apparel and leather industries Population Density (pop/skm) 32 9.31%	7.06%	34.94%
	regional Manufacture of wood and wood products local	33	4.81%	4.83%	9.05%
	Belgium Ireland Portugal United States Note: figures are averages for 2002 (respectively 2000 for the US) 240 17.5 144.5 7.5 / 1 576 37.5 5518 89.5 F o r Manufacture paper, paper prods., printing, publishing Manufacture of chemicals and chemical products Manufacture non-metallic mineral prods. except fuel Basic metal industries Fabricated metal products, machinery and equipment F o r	338 55.5 118 78.5 34 35 36 37 38	7.64% 15.25% 13.33% 6.08% 5.35% 4.58% 6.37% 0.39% 30.33% 39.71% 12.04% 5.06% 1.83% 7.22% 1.41%
	P Other manufacturing industries Total P	39	0.88% 94%	2.04% 98%	1.08% 83%
	e e
	e r e r
	R R
	e e
	v i e v i e
	w w
		O n l O n l
			y y

Note: percentages only include sectors that are effectively used in the present study, so they do not sum up to 100 per cent.

Table 3 : Moran indices

 3

			Regional Studies					Page 34 of 41
	1
	2
	3
	4
	5
	6 7			ISIC code	Moran I Belgium (communes) p-value	Moran I Ireland (DEDs) p-value	Moran I Portugal (concelhos) p-value
	8 9	Slaughtering, preparing and preserving meat Manufacture of dairy products Canning and preserving of fruits and vegetables	3111 3112 3113	0.0877 0.0111 0.0252	0.0002 0.5822 0.2373	-0.0061 0.0119 0.0021	0.5617 0.2038 0.7801	-0.0325 0.0083 -0.0129	0.3841 0.7029 0.777
	10 11	Can., Preserv., and process of fish, crustacean Manufacture of vegs. and animal oils and fats Grain mill products	3114 3115 3116	0.0333 -0.0068 -0.0092	0.0336 0.6499 0.6491	0.0088 -0.0009 -0.0046	0.341 0.9422 0.635	-0.0075 0.0168 0.0151	0.8979 0.4542 0.5574
	12 13 14	Manufacture of food products, N.E.C. Manufacture of prepared animal feeds Distilling, rectifying and blending spirits Malt liquors and malt	3121 3122 3131 3133	0.0514 0.0884 0.0939 -0.0065	0.0218 0 0 0.8142	-0.0031 0.0037 -0.0007 -0.0008	0.7565 0.658 0.9612 0.9565	0.0545 0.0455 0.0859 0.0825	0.0669 0.1028 0.0009 0.0015
	15 16	Soft drinks and carbonated waters industries Tobacco manufactures Spinning, weaving and finishing textiles	3134 3140 3211	-0.0191 -0.0084 0.2624	0.4113 0.7654 0	-0.0035 -0.0011 0.0048	0.7205 0.9266 0.5823	-0.0197 -0.0099 0.0026	0.6117 0.7388 0.8304
	17 18	Manufacture made-up textile goods except wearing apparel Knitting mills Manufacture of carpets and rugs	3212 3213 3214	0.0307 0.058 0.2873	0.0596 0 0	0.004 0.0022 -0.002	0.6657 0.1708 0.8381	0.0205 0.0763 0.0108	0.0794 0.0015 0.6116
	19	Manufacture of textiles, N.E.C.	3219	0.019	0.3227	-0.0016	0.8826	0.0364	0.2092
	20 21	Manufacture of wearing apparel except footwear Tanneries and leather finishing Manufacture prods. leather except footwear and apparel	3220 3231 3233	0.1238 0.0549 -0.0035	0 0.0018 0.7667	-0.005 -0.0018 -0.002	0.6345 0.8592 0.8485	0.0725 -0.0116 -0.001	0.0243 0.6764 0.9307
	22 23	Manufacture footwear except rubber or plastic Sawmills, planting and other wood mills Manufacture of wooden, cane containers, small cane ware	3240 3311 3312	0.0097 0.1366 0.0163	0.6119 0 0.4181	-0.0004 -0.004 -0.0009	0.9841 0.6544 0.9063	-0.0107 0.015 0.0287	0.8169 0.5871 0.3183
	24 25	Manufacture wood and cork products N.E.C. Manufacture furniture, fixtures except primarily mental Manufacture of pulp, paper and paperboard	3319 3320 3411	-0.0067 0.2945 -0.0198	0.8266 0 0.3972	-0.0052 -0.0044 -0.0027	0.615 0.6573 0.8032	0.0035 -0.0012 0.0053	0.4599 0.9273 0.7791
	26	Manufacture articles of pulp, paper, paperboardN.E.C.	3419	0.0505	0.0259	-0.0034	0.6139	0.0642	0.0358
	27 28	Printing, publishing and allied industries Basic industrial chemicals except fertilizers Fertilizers and pesticides	3420 3511 3512	0.1619 0.0888 0.029	0 0 0.1103	-0.0045 -0.0025 -0.0019	0.6678 0.7832 0.8393	0.0286 0.1839 0.0396	0.1963 0 0.1178
	29 30	Syn. resins, plastic mat. man-made fibers exc. glass Paints, varnishes and lacquers Drugs and medicines	3513 3521 3522	0.0168 0.0323 0.0186	0.3717 0.1214 0.3288	-0.0004 -0.001 0.0187	0.9806 0.943 0.0534	-0.0025 -0.0087 0.0569	0.9712 0.8561 0.0441
	31 32 33	Soap, cleansing preparations, perfumes, cosmetics Chemical products, N.E.C. Misc. products of petroleum and coal Manufacture of rubber products N.E.C.	3523 3529 3540 3559	0.073 0.2181 -0.0027 0.0068	0.0012 0 0.8043 0.6887	-0.0036 0.0114 -0.0006 -0.0037	0.7215 0.2053 0.9681 0.7058	0.1492 0.0284 -0.0038 -0.002	0 0.3329 0.985 0.9577
	34 35	Plastic products N.E.C. Pottery, china and earthware Glass and glass products	3560 3610 3620	0.0263 0.0072 0.1538	0.2354 0.6139 0	0.0174 -0.0047 -0.0015	0.0756 0.6314 0.8607	0.086 0.0533 -0.0049	0.0043 0.0765 0.9439
	36 37	Structural clay products Cement, lime and plaster Non-metallic mineral products, N.E.C.	3691 3692 3699	0.0385 0.0224 0.0233	0.0698 0.3071 0.2483	-0.0036 0.0013 -0.0028	0.7277 0.8586 0.7228	0.0413 0.1011 0.0801	0.1804 0.0008 0.0011
	38 39 40	Iron and steel basic industries Non-ferrous metal basic industries Cutlery, hand tools and general hardware Structural metal products	3710 3720 3811 3813	0.0186 0.0073 0.024 0.1251	0.3237 0.6743 0.0927 0	-0.0005 -0.0031 -0.0017 -0.0028	0.9524 0.7566 0.8833 0.7999	-0.0199 0.0398 0.1005 0.1328	0.6218 0.1906 0.0019 0
	41 42	Fabricated metal products except machinery and equipment N.E.C. Agricultural machinery and equipment Spec. indus. machinery and equipment except 3823	3819 3822 3824	-0.0211 0.0135 0.0268	0.3588 0.2019 0.1754	-0.0022 0.0048 0.0061	0.8459 0.6014 0.4598	0.0275 0.0033 0.0999	0.3482 0.8157 0.0021
	43 44	Office, computing and accounting machinery Electrical industrial machinery and apparatus Radio, tele., communications equipment and apparatus	3825 3831 3832	0.0236 0.0188 -0.0035	0.208 0.2782 0.9349	0.0116 0.0061 -0.0021	0.0883 0.4304 0.8475	-0.0059 0.0288 -0.0037	0.9284 0.255 0.999
	45	Electrical appliances and housewares	3833	-0.0038	0.9244	-0.0013	0.8967	-0.0184	0.5827
	46 47	Electrical apparatus and supplies, N.E.C. Shipbuilding and repairing Motor vehicles	3839 3841 3843	-0.0091 0.0746 -0.0263	0.735 0 0.2414	0.0014 0.0149 0.0075	0.8544 0.0676 0.3994	0.0978 0.1388 0.0195	0.0024 0 0.4778
	48 49	Prof., scientific, measuring and control equipment Photographic and optical goods Jewelry and related articles	3851 3852 3901	0.0133 -0.0101 0.0586	0.4888 0.5917 0	-0.0046 -0.001 0.0055	0.6027 0.8596 0.2706	-0.0053 -0.0219 -0.0062	0.9415 0.5252 0.8743
	50 51	Musical instruments Sporting and athletic goods Manufacturing industries N.E.C.	3902 3903 3909	0.017 0.0623 -0.0038	0.409 0.0016 0.9198	-0.0012 -0.0013 0.0047	0.9147 0.8727 0.5369	-0.0065 -0.013 0.0306	0.9065 0.7618 0.2957
	52
	53 54	Note: Moran indices are computed for first order contiguity matrices.	E(I)		-0.0017		-0.0003		-0.0036
	55
	56
	57
	58
	59

http://mc.manuscriptcentral.com/cres Email: regional.studies@fm.ru.nl

Table 4 : EG index for Belgium ISIC code E&G communes E&G arrondissements Rank E&G arr.

 4

	Misc. products of petroleum and coal	3540	0.4037*	0.3749*	1
	Jewelry and related articles	3901	0.1342*	0.1784*	4
	Pottery, china and earthware	3610	0.1136*	0.1398*	10
	Knitting mills	3213	0.1103*	0.1656*	6
	Can., Preserv., and process of fish, crustacean	3114	0.1065*	0.1585*	7
	Shipbuilding and repairing	3841	0.0847*	0.1766*	5
	Manufacture of vegs. and animal oils and fats	3115	0.0682*	0.1993*	3
	Fertilizers and pesticides	3512	0.0525	0.0982	13
	Basic industrial chemicals except fertilizers	3511	0.0436*	0.0502*	19
	Manufacture of carpets and rugs	3214	0.037*	0.1575*	8
	Spinning, weaving and finishing textiles	3211	0.033*	0.1368*	11
	Manufacture of textiles, N.E.C.	3219	0.0301*	0.0565*	16
	Canning and preserving of fruits and vegetables	3113	0.0287*	0.0361*	27
	Cutlery, hand tools and general hardware	3811	0.0275*	0.027*	30
	Drugs and medicines	3522	0.0273*	0.0991*	12
	Agricultural machinery and equipment	3822	0.0239	0.0397	24
	Musical instruments	3902	0.022*	0.0478*	21
	Glass and glass products	3620	0.0184*	0.0436*	23
	Manufacture footwear except rubber or plastic	3240	0.0161	0.0122	41
	Sawmills, planting and other wood mills	3311	0.016*	0.0344*	28
	Manufacture of prepared animal feeds	3122	0.0158*	0.0382*	25
	Manufacture of dairy products	3112	0.0145*	0.0104	43
	Structural clay products	3691	0.0144	0.0064	46
	Photographic and optical goods	3852	0.0119	-0.0483	63
	Tobacco manufactures	3140	0.0116	0.0244	31
	Office, computing and accounting machinery	3825	0.0102	0.0535	17
	Manufacture furniture, fixtures except primarily mental	3320	0.0099*	0.0137*	38
	Paints, varnishes and lacquers	3521	0.0096	0.0514*	18
	Manufacture made-up textile goods except wearing apparel	3212	0.0093	0.0372*	26
	Sporting and athletic goods	3903	0.0088	0.0574	15
	Cement, lime and plaster	3692	0.0087*	-0.0001	52
	Manufacture prods. leather except footwear and apparel	3233	0.0086	0.0485	20
	Non-metallic mineral products, N.E.C.	3699	0.0083*	0.0133*	39
	Prof., scientific, measuring and control equipment	3851	0.0079	0.0188*	35
	Tanneries and leather finishing	3231	0.0078	0.0796	14
	Soap, cleansing preparations, perfumes, cosmetics	3523	0.0074	0.0144	37
	Electrical appliances and housewares	3833	0.0074	0.0023	51
	Manufacture of rubber products N.E.C.	3559	0.0071	0.0149	36
	Slaughtering, preparing and preserving meat	3111	0.0068*	0.0055*	47
	Manufacture of wearing apparel except footwear	3220	0.0064*	0.0209*

Table 5 : EG index for Ireland ISIC code E&G township E&G county Rank E&G county

 5 Note: results were computed for 1998; stars stand for statistical significance according to the two standard deviation rule; sectors are ranked according to the local-level E&G index ranking; townships and counties correspond to the local respectively the regional geographic breakdown.

		Regional Studies			Page 36 of 41
	1
	2
	3
	4
	5 6 7	Photographic and optical goods Iron and steel basic industries Manufacture of wooden, cane containers, small cane ware Electrical appliances and housewares	3852 3710 3312 3833	0.488* 0.1342* 0.0949 0.0922	0.2957* 0.0432 -0.1267 0.0752	1 25 63 14
	8	Malt liquors and malt	3133	0.0863	-0.0441	61
	9 10	Can., Preserv., and process of fish, crustacean Syn. resins, plastic mat. man-made fibers exc. glass Motor vehicles	3114 3513 3843	0.0793* 0.0734 0.072*	0.1147* 0.1774 0.0297	9 3 30
	11	Manufacture of vegs. and animal oils and fats	3115	0.0683	0.065	17
	12 13	Manufacture of prepared animal feeds Manufacture of carpets and rugs Misc. products of petroleum and coal	3122 3214 3540	0.0483* 0.0438 0.0432	0.046* 0.0747 0.0104	23 16 39
	14	Musical instruments	3902	0.0399	-0.073	62
	15 16	Manufacture articles of pulp, paper, paperboardN.E.C. Glass and glass products Jewelry and related articles	3419 3620 3901	0.0393 0.038 0.0363*	0.0543 0.1297* 0.01	18 6 41
	17	Office, computing and accounting machinery	3825	0.036	0.0025	46
	18 19 20	Chemical products, N.E.C. Prof., scientific, measuring and control equipment Structural clay products Soft drinks and carbonated waters industries	3529 3851 3691 3134	0.0319 0.0272* 0.0251 0.02	0.0204 0.0373* 0.0303 -0.0289	34 27 29 56
	21 22 23	Slaughtering, preparing and preserving meat Canning and preserving of fruits and vegetables Manufacture of pulp, paper and paperboard Drugs and medicines	3111 3113 3411 3522	0.0173* 0.0168 0.0159 0.0156*	0.0411* 0.0448 0.2171* 0.0213*	26 24 2 33
	24 25 26	Manufacture wood and cork products N.E.C. Manufacture furniture, fixtures except primarily mental Manufacture of wearing apparel except footwear Sawmills, planting and other wood mills	3319 3320 3220 3311	0.015 0.0135* 0.0131* 0.013	0.0011 0.0507* 0.0515* 0.0359*	49 20 19 28
	27	Electrical apparatus and supplies, N.E.C.	3839	0.0111	0.0058	43
	28 29	Radio, tele., communications equipment and apparatus Plastic products N.E.C. Basic industrial chemicals except fertilizers	3832 3560 3511	0.0108 0.0103* 0.01	-0.0215 0.0039 -0.0396	53 44 59
	30	Manufacture of dairy products	3112	0.0079	0.0752*	15
	31 32	Manufacture made-up textile goods except wearing apparel Manufacture of rubber products N.E.C. Cement, lime and plaster	3212 3559 3692	0.007 0.0052 0.0037	-0.0072 0.0137 0.0103	52 37 40
	33	Soap, cleansing preparations, perfumes, cosmetics	3523	0.0029	-0.0242	54
	34 35	Cutlery, hand tools and general hardware Spinning, weaving and finishing textiles Printing, publishing and allied industries	3811 3211 3420	0.0027 0.0015 0.0014	0.015 0.1178* 0.1671*	36 8 5
	36	Structural metal products	3813	0.001	0.0011	48
	37 38 39	Agricultural machinery and equipment Manufacture of food products, N.E.C. Non-ferrous metal basic industries Fertilizers and pesticides	3822 3121 3720 3512	-0.0008 -0.0009 -0.0009 -0.0016	0.1005* 0.0199* 0.0034 -0.0302	11 35 45 58
	40 41 42	Fabricated metal products except machinery and equipment N.E.C. Spec. indus. machinery and equipment except 3823 Paints, varnishes and lacquers Manufacturing industries N.E.C.	3819 3824 3521 3909	-0.0016 -0.0028 -0.0036 -0.0044	0.0013 0.0134 0.0465 -0.0005	47 38 22 50
	43 44 45	Manufacture of textiles, N.E.C. Knitting mills Pottery, china and earthware Non-metallic mineral products, N.E.C.	3219 3213 3610 3699	-0.0056 -0.0097 -0.0111 -0.0131	0.0078 0.1753 0.0229 -0.0039	42 4 32 51
	46 47 48	Tobacco manufactures Distilling, rectifying and blending spirits Manufacture prods. leather except footwear and apparel Tanneries and leather finishing	3140 3131 3233 3231	-0.0133 -0.0136 -0.0144 -0.016	-0.029 0.0763 -0.0411 0.1082	57 13 60 10
	49	Shipbuilding and repairing	3841	-0.0175	0.0502	21
	50 51	Grain mill products Electrical industrial machinery and apparatus Manufacture footwear except rubber or plastic	3116 3831 3240	-0.0194 -0.0368 -0.0514	0.0243 -0.025 0.0809	31 55 12
	52	Sporting and athletic goods	3903	-0.0529	0.1185	7
	53
	54
	55
	56
	57
	58
	59

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Table 6 : EG index for Portugal ISIC code E&G concelho E&G distrito Rank E&G dist.

 6 Note: results were computed for 1998; stars stand for statistical significance according to the two standard deviation rule; sectors are ranked according to the locallevel E&G index ranking; concelhos and distritos correspond to the local respectively the regional geographic breakdown.

	Tanneries and leather finishing	3231	0.3941*	0.417*	4
	Manufacture wood and cork products N.E.C.	3319	0.3272*	0.3781*	6
	Office, computing and accounting machinery	3825	0.3134*	0.0766	33
	Jewelry and related articles	3901	0.2859*	0.3038*	10
	Glass and glass products	3620	0.2085*	0.305*	8
	Misc. products of petroleum and coal	3540	0.2032*	0.6313*	1
	Manufacture made-up textile goods except wearing apparel	3212	0.1655*	0.2034*	12
	Prof., scientific, measuring and control equipment	3851	0.0845*	0.1248*	27
	Fertilizers and pesticides	3512	0.0824*	0.1799*	17
	Spinning, weaving and finishing textiles	3211	0.0796*	0.1801*	16
	Manufacture furniture, fixtures except primarily mental	3320	0.0776*	0.138*	24
	Manufacture footwear except rubber or plastic	3240	0.0756*	0.1254*	26
	Drugs and medicines	3522	0.0747*	0.5357*	2
	Printing, publishing and allied industries	3420	0.0739*	0.1634*	18
	Manufacture prods. leather except footwear and apparel	3233	0.069*	0.0349*	46
	Manufacture of dairy products	3112	0.0657	0.0258	50
	Distilling, rectifying and blending spirits	3131	0.0651*	0.0268	49
	Can., Preserv., and process of fish, crustacean	3114	0.0583*	0.0325*	48
	Knitting mills	3213	0.0549*	0.2042*	11
	Soap, cleansing preparations, perfumes, cosmetics	3523	0.0539*	0.483*	3
	Paints, varnishes and lacquers	3521	0.0505*	0.0705*	35
	Electrical industrial machinery and apparatus	3831	0.0494	-0.0301	62
	Iron and steel basic industries	3710	0.0492*	0.0496	42
	Electrical appliances and housewares	3833	0.0471	-0.024	61
	Shipbuilding and repairing	3841	0.0461*	0.4065*	5
	Manufacture of carpets and rugs	3214	0.0452*	0.1344*	25
	Pottery, china and earthware	3610	0.0429*	0.2006*	13
	Syn. resins, plastic mat. man-made fibers exc. glass	3513	0.0354*	0.1391*	22
	Manufacture of vegs. and animal oils and fats	3115	0.0334	0.1385*	23
	Manufacture of food products, N.E.C.	3121	0.0332*	0.1503*	21
	Manufacture of prepared animal feeds	3122	0.0332*	0.0948*	30
	Manufacture of pulp, paper and paperboard	3411	0.0314*	0.1163*	28
	Basic industrial chemicals except fertilizers	3511	0.0263*	0.1839*	14
	Structural clay products	3691	0.0248*	0.1522*	20
	Agricultural machinery and equipment	3822	0.0241*	0.0821*	31
	Motor vehicles	3843	0.0217*	0.063*	38
	Cement, lime and plaster	3692	0.0214	0.1816*	15
	Plastic products N.E.C.	3560	0.0212*	0.0577*	40
	Non-metallic mineral products, N.E.C.	3699	0.0178*	0.0483*	43
	Non-ferrous metal basic industries	3720	0.0167*	0.0185	53
	Soft drinks and carbonated waters industries	3134	0.0164*	0.0695*	36
	Manufacture of wooden, cane containers, small cane ware	3312	0.014*	0.0019	58
	Fabricated metal products except machinery and equipment N.E.C.	3819	0.0137*	0.0222*	52
	Manufacturing industries N.E.C.	3909	0.0136*	0.0137*	54
	Cutlery, hand tools and general hardware	3811	0.0129*	0.0124*	55
	Structural metal products	3813	0.0113*	0.08*	32
	Manufacture of wearing apparel except footwear	3220	0.0112*	0.0625*	39
	Slaughtering, preparing and preserving meat	3111	0.0111*	0.0685*	37
	Manufacture articles of pulp, paper, paperboardN.E.C.	3419	0.0111*	0.0347*	47
	Chemical products, N.E.C.	3529	0.0102	0.0432	44
	Manufacture of rubber products N.E.C.	3559	0.0095*	-0.0056	59
	Sawmills, planting and other wood mills	3311	0.009*	0.0373*	45
	Spec. indus. machinery and equipment except 3823	3824	0.0067*	0.0101	56
	Electrical apparatus and supplies, N.E.C.	3839	0.0054	0.0575*	41
	Canning and preserving of fruits and vegetables	3113	0.0031	0.1566*	19
	Grain mill products	3116	0.0031	0.0741*	34
	Radio, tele., communications equipment and apparatus	3832	-0.0019*	0.023*	51
	Musical instruments	3902	-0.0051	0.3684	7
	Manufacture of textiles, N.E.C.	3219	-0.0114*	0.1087*	29
	Sporting and athletic goods	3903	-0.026	0.0061	57
	Malt liquors and malt	3133	-0.0306	-0.1542	63
	Photographic and optical goods	3852	-0.0483	-0.0138	60
	Tobacco manufactures	3140	-0.4349	0.3044	9

Table 7 :

 7 Rank correlation of EG indices between countries Standard errors in parentheses. ***, **, and * signify 1, 5, and 10 per cent significance levels. Data source is Eurostat. R&D expenditure is not studentized. All specifications performed using OLS estimators.

		Regional Studies	Page 38 of 41
		Belgium	(arrondissements)	Ireland (counties)	Portugal (distritos)
	Belgium (arrondissements)	1	-	-
	Ireland (counties) 0.196 (0.123)	1	-
	Portugal (distritos) 0.416 (0.000)	0.057 (0.656)	1
	(p-values in parenthesis)

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Table 9 :

 9 Oaxaca Decomposition of Ellison and Glaeser indexCountry iCountry j EG i -EG j (β i -β j )X i β j (X i -X j )

	Portugal	Ireland	0.084	0.043	0.041
	Portugal	Belgium	0.078	-0.015	0.093
	Belgium	Ireland	0.006	-0.010	0.016

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