A Treatise on the Scale-Dependency of Agglomeration Externalities and the Modifiable Areal Unit Problem

Martijn Burger Corresponding author: Faculty of Economics, Department of Applied Economics, Erasmus University Rotterdam, Postbus 1738, 3000 DR Rotterdam. E-mail: mburger@few.eur.nl Frank van Oort Faculty of Geosciences, Department of Economic Geography, Utrecht University & Netherlands Institute for Spatial Research (RPB). E-mail: oort@rpb.nl Bert van der Knaap Faculty of Economics, Department of Applied Economics, Erasmus University Rotterdam, E-mail: vanderknaap@few.eur.nl ___________________________________________________________________________

Version: April 15, 2007

Paper prepared for the Workshop on “Agglomeration and Growth in

Knowledge-Based Societies”, Kiel, Germany, April 20-21 Abstract Recently there has been an increasing interest in the question how agglomeration externalities influence local and regional employment and productivity growth. Driven by the fact that current empirical studies suffer from a spaceless view on agglomeration externalities that does not take into account their scale-dependency, we focus on agglomeration externalities in relation to employment growth at different geographical scales using spatial autoregressive modeling. We find different effects of agglomeration forces (specialization, urbanization, diversity) across sectors at different geographical scales. However, the Modifiable Areal Unit Problem (MAUP) – stating that research results are dependent on the initial spatial scale of measurement - seriously questions the validity of spatial agglomeration models. This has important implications for spatial-economic policies aiming at economic growth. Keywords : sectoral employment growth, agglomeration externalities, spatial autoregression, MAUP JEL Classification: C21, O18, R11, R12

1. Introduction

From the 1980s onwards, there has been an increase in the use of geographical models in

economic analyses. This development can be mainly ascribed to the failure of orthodox

economics to give appropriate explanations for the variation in the wealth and poverty of

cities and regions (Piore and Sabel, 1984). Inspired by the success of Silicon Valley,

Cambridge (UK) and The Third Italy compared to the decline of other regions in the West (in

particular, the old industrial areas), pressing questions were why industries choose to locate

in a particular area and which kind of concentration of economic activities is needed to foster

economic growth at both the firm and area level. This ‘rediscovery’ of space in economics

(Krugman, 1991) and business studies (Porter, 1990), has led to an extensive empirical

literature of how the spatial concentration of economic activities augments localized

productivity and growth (e.g., Glaeser et al., 1992; Henderson et al., 1995; Combes 2000;

Rosenthal and Strange, 2003).

Despite its novelty, this empirical research draws on a long tradition in spatial research

initiated by Marshall’s theory of agglomeration developed at the end of the 19th century. The

core of Marshall’s argument was that a local concentration of economic activity takes in a

number of key competitive advantages. As indicated by Marshall (1890), these advantages

include the availability of a skilled and specialized labor force, the presence of intermediate

goods and the possibility to swiftly exchange product, technological, and organizational

innovations (information and knowledge spillovers). Although Marshall only focused on

single-industry areas and sector-specific externalities (internal to the industry), later the

framework of agglomeration externalities would be expanded with external economies

accessible to all companies in a geographical concentration irrespective of the sector

concerned (see e.g., Hoover, 1948; Isard, 1956). In the economic and geographic literature,

the distinction made between the sector-specific localization economies and the more

universal urbanization economies would become generally acknowledged.

The concept of agglomeration externalities as formulated by Marshall and other early

theorists forms a generalized theory of agglomeration which has remained largely unaltered

over the years. In the present empirical literature, surprisingly little attention is paid to the

spatial configuration of cities and urban regions. As Phelps (2004) rightly notes, Marshall’s 2

localization economies were based on singular industrial towns or city neighborhoods (more

commonly referred to as industrial districts), while nowadays agglomeration and

agglomeration externalities are considered to be operating on different spatial scales

Rosenthal and Strange (2003: 377) accuse most empirical work on agglomeration

externalities of from being spaceless in the sense that ‘it has implicitly modeled the city as a

club’. In econometric models, agglomeration externalities are usually considered to be

spatially fixed at a metropolitan or regional scale. Although a growing body of literature

proves that the spatial extent of agglomeration can be dealt with by means of spatial

autoregressive analyses (e.g. Van Oort 2004), little is known about the scale sensitivity of

research results for variations in the initial unit of spatial analysis. This neglects the possible

availability of agglomeration externalities at different spatial scales, ranging from the

neighborhood to supra-regional levels (Olsen 2004).

The potential influence of spatial composition and aggregation effects has been labeled the

Modifiable Areal Unit Problem (MAUP) by Openshaw & Taylor (1979)1. But despite

repeated warnings in the related geographical literature (see e.g., Kephart 1988, Wrigley

1995, Petterson 2001), the urban economic modeling tradition on agglomeration externalities

has not taken up this issue seriously. Accumulating on the existing knowledge on the

geographical scope of economic externalities, this paper tests how serious the Modifiable

Areal Unit Problem is in current economic agglomeration analysis. We do so by using

employment data on three different spatial scales in the Netherlands on which different types

of agglomeration externalities can be present simultaneously. We examine the effect of

agglomeration externalities on sectoral concentration growth (1996-2004) in six broad sectors

(labor-intensive manufacturing, capital-intensive manufacturing, knowledge-intensive

manufacturing and process industries, wholesale trade, financial services and producer

services) on the levels of 483 municipalities (local level), 129 economic geographic areas

(district level), and 40 COROP areas (regional level, NUTS-3). Spatial autoregression

modeling will be used at all three spatial levels to control for spatial spillover effects in

growth.

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1 Earlier hints at the problem are already given by Gehlke & Biehl (1934), Robinson (1950), Yule & Kendall (1950), and Goodman (1959)

The remaining of this article is organized as follows. Section 2 provides an overview of the

agglomeration externalities literature. Attention will be paid to the non-unique findings across

empirical studies and problems with generalizing effects of agglomeration externalities on

economic growth. In particular, we focus on the scale-dependency of agglomeration

externalities and the Modifiable Areal Unit Problem. Section 3 contains a description of the

models and variables used. Finally, Section 4 is concerned with the estimation results,

followed by discussion and policy implications in Section 5.

2. Agglomeration Externalities and Spatial Bewilderment

2.1 A Short History and Taxanomy of Agglomeration Externalities

The origin of the agglomeration externalities concept can be traced back to the end of the 19th

century. At the fin de siècle, the neoclassical economist Alfred Marshall intended to overturn

the pessimistic (but influential) predictions on the co-evolution of economic and population

development made by Thomas Malthus and David Ricardo by introducing some form of

aggregate increasing returns for firms. In his seminal work, Principles of Economics (Book

IV, Chapter X), Marshall (1890) mentions a number of cost-saving benefits or productivity

gains external to a firm, from which a firm can benefit through co-location. These

agglomeration economies were considered to be industry-specific as they would not be based

on co-location of firms as such, but on co-location of firms all engaged in the same sort of

business. In other words, the externalities described by Marshall, nowadays often labeled

localization economies, are thought to derive from a local concentration of a firm’s own

industry. In specific, Marshall pointed at 1) the presence of a skilled workforce, 2) the

availability of intermediate goods, and 3) knowledge spillovers. A highly concentrated

industry can exert a pull on (and uphold of) a large labor pool including workers with

specialised training relevant for the industry. Obviously, this reduces search costs and

increases flexibility in appointing and firing employees. The availability of intermediate

goods refers to the fact that a sectoral concentration attracts specialized suppliers to these

areas, which in turn reduces transaction costs. These intermediate goods can concern both

public and business intermediate inputs tailored to the technical needs of a particular industry

or firm. Finally, knowledge spillovers denote the possibility to discuss new ideas or

improvements, related to products, technology and organization. These spillovers can stem

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from ongoing relationships between firms, but also from job mobility, local epistemic

communities and informal contacts between employees of different firms (Huber 2007).

As expansion to Marshall’s Principals of Economics (1890), subsequent theoretical work by

Ohlin (1933), Hoover (1937; 1948) and Isard (1956) did not only focus on the positive effects

of a geographical concentration of a specific industry, but more generally on how a single

firm is influenced by co-location and which spatial conditions cause a more than proportional

growth in productivity and economic activity (Van Oort 2004). Hoover (1937; 1948) and

Isard (1956) distinguish between two types of externalities: localization economies and

urbanization economies.2 Localization and urbanization economies can both be regarded as

cost-saving benefits or productivity gains external to a firm, from which a firm can benefit by

being located at the same place as one or more other firms or where suppliers, distribution

networks and support services are present. Morover, both types of externalities were

considered to be uncontrollable and unregulable for a single company and, above all, thought

to be immobile or spatially constrained. According to many regional scientists, these external

economies are the origin of the uneven distribution and growth of economic activities across

regions as in some areas firms can profit to a larger extent from these economies than in other

regions. However, the externalities differ with respect to the factors which they appoint as

firm-external sources of economic growth.

Localization economies usually take the form of the Marshallian externalities described

above (availability of specialized and skilled labor, presence of intermediate goods, and

knowledge spillovers). Whether due to firm size or a large initial number of local firms, a

high level of factor employment may allow the development of external economies within the

group of local firms in a sector. Counter to localization economies, urbanization economies

derive from the size of the urban economy. These economic externalities are not only

external to a firm, but also to its industry, making these agglomeration economies available to

all firms in a given location. According to Isard (1956), it is the availability of a large and

multi-functional labor pool and the presence of a good infrastructure and public facilities that

enforces economic growth. Relatively more densely inhabited localities are also more likely

to accommodate universities, R&D laboratories, trade associations, and other knowledge

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2 Although Hoover (1937; 1948) and Isard (1956) also considered internal economies of scale economies as source of agglomeration, the treatise of internal agglomeration economies is beyond the scope of this article.

generating institutions. It is the dense presence of these organizations that supports the

production and absorption of know-how, stimulating innovative behavior, and contributes to

disparities in local and regional economic growth (Harrison et al. 1997). Moreover, the

presence of a large internal market offers a larger degree of stability (Siegel et al. 1995).

However, too densely populated areas may also result in a dispersion of economic activities

due to pollution, crime or high land prices. In this respect, one can speak of urbanization

diseconomies.

In their original connotation, urbanization economies, or economies of concentration of

industry in general, were first and foremost related to the size of the urban economy. Later,

Jacob’s (1969) argument, that it is in fact the variety of the urban economy that augments

economic growth, would gradually become more important within the concept of

urbanization economies. According to Jacobs, it is not the size of cities, but the sectoral

variety present in cities, which augments economic growth through stimulating innovation,

vertical integration and cooperation between complementary industries A diverse sectoral

structure increases the odds of interaction, generation, replication, modification and

recombination of ideas and applications across different sectors. Spatial proximity between

the economic actors concerned potentially lowers the threshold of face-to-face interaction,

which is necessary to transmit the (often tacit) knowledge between the different types of

firms and organizations (Ponds et al., 2007). In addition, a diverse industrial structure

protects a sector from volatile demand and offers the possibility to switch between input

substitutes (Frenken et al 2007). A distinction between classical’ urbanization economies

stemming from size and urbanization economies stemming from diversity (or Jacobs

economies, after Jane Jacobs) is then also desirable as these agglomeration externalities can

be regarded as two different sources of economic growth.

2.2 Comparative Agglomeration Externalities

Having distinguished three different types of agglomeration externalities (localization

economies, urbanization economies, and Jacobs economies), the central question in a large

empirical literature focuses on which of these is best to promote economic growth Despite its

considerable size, the empirical literature has failed to offer a consistent answer to this

question. Evaluating studies that have used a comparative framework of agglomeration

externalities, Rosenthal and Strange (2004) report mixed evidence for which type of

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externality matters most for economic growth. For the United States, Glaeser et al. (1992)

find evidence supporting that diversity fosters employment growth, while Henderson et al.

(1995) concludes that for high-technology industries, both specialization and diversity are

conducive to growth. Rosenthal and Strange (2003) find a positive effect of localization,

urbanization and Jacobs economies, but also observe that in particular localization economies

attenuate quickly with distance. For France, Combes (2000) finds that diversity and

urbanization tend to enhance employment growth in services whereas it tends to retard

growth in manufacturing sectors, while localization externalities do not seem to boost growth

in either type of activity.3

We find the same degree of non-robustness and inconsistency for analogous studies on The

Netherlands. Van der Panne (2004) finds that localization economies stimulate regional

innovativeness, in particular for R&D intensive and small firms. Analyzing the effect of

agglomeration externalities on productivity growth (1987-1995) in 40 functional regions

(COROP) in the Netherlands, Van Stel and Nieuwenhuijsen (2004) found a positive effect of

Jacobs externalities in the services sector and no effect of localization economies at all. For

the same Dutch functional regions, Frenken et al. (2007) find a negative and significant effect

of Jacobs externalities on productivity growth, but a positive effect on employment growth.

Finally, Van Oort (2007) finds different effect of agglomeration externalities on economic

growth across sectors. Whereas local concentration and diversity was positively related to

local employment growth in producer services, the opposite appeared to be the case in

manufacturing. In addition, effects varied across spatial regimes (core/periphery) and

formulations of the extent of spatial dependence. These discrepancies across and within

studies is an indication for the fact that a simple theory might not be sufficient to elucidate a

complex phenomenon.

2.3 Sources of Non-Robustness in the Empirical Literature

This seemingly lack of robustness in the empirical literature implies that localization,

urbanization and Jacobs economies can exist next to each other and that not by definition one

type of agglomeration externality leads to more economic growth than the other. This lack of

73 A more elaborate comparison is provided by Neffke (2007) and Smit et al. (2007).

robustness can be traced back to at least three sources, namely 1) differences in methodology

and measurement across studies, 2) the context-specificity of agglomeration externalities, and

3) the scale-dependency of agglomeration externalities. These sources will be discussed

below.

2.3.1. Differences in methodology and measurements across studies

As Neffke (2007: 9) rightly observes, ‘there are large differences in methodology in

measurement over studies, so it is hard to compare empirical findings across different

studies’. Since studies differ in the way they measure economic growth and agglomeration

externalities (see Table 1), this may in turn lead to different outcomes. For instance, Smit et

al. (2007) show by means of a meta-analysis that the effect of localization externalities on

economic growth is dependent on whether these externalities are measured by an indicator of

relative specialization or an indicator of absolute concentration or number of employees. The

use of different control variables and the use of different spatial scales as unit of analysis

aggravate these incongruities even more. It can be argued that the concept of agglomeration

externalities suffers from being a ‘fuzzy concept’ (Markusen 1999): researchers often belief

they deal with the same observable fact, but a uniform definition and empirical

operationalization is lacking. Nonetheless, as empirical outcomes also appear to be

inconsistent and non-robust within studies or across studies that use a similar measurement

and methodology, differences in measurement and methodology are only part of the story.

Accordingly, we should pay attention to the context-specificity and scale-dependency of

agglomeration externalities. The context-specificity of agglomeration externalities is

discussed in the next subsection, while the scale-dependency of agglomeration externalities is

touched upon in subsection 2.3.3.

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Table 1: Measurement of Agglomeration Externalities across Empirical Studies

Localization Urbanization Jacobs Glaeser et al. (1992) Log own industry

employment Location quotient

Tested by regressing growth small industries on size top 4 industries

Employment other 5 top 6 city-industries

Henderson et al. (1995) Log own industry employment Share local industry in local economy

Log all other manufacturing employment

Herfindahl other industry employment

Rosenthal & Strange (2003) Own industry employment (3 times in different concentric circles)

Other industry employment (3 times in concentric circles)

Herfindahl other employment

Combes (2000) Log location quotient Log total employment density

Log Herfindahl other industry employment

Van der Panne (2004) Location quotient Number of firms Reciprocal Gini all

employment Van Stel & Nieuwenhuijsen (2004) Location quotient Not in study Share of the three

smallest other sectors in total employment

Frenken et al. (2007) Los-index (degree of technological clustering, based on input-output relations)

Log population density

Related variety or entropy (5-digit sector variation within 2- digit sectors)

Van Oort (2007) Location quotient Not in study Gini/Herfindahl all other employment Employment other 5 top 6 city-industries

Based on: Neffke (2007); Dutch studies added

2.3.2 The Context-Specificity of Agglomeration Externalities

Apart from the existence of methodological differences, it can also be argued that the effect

of agglomeration externalities on economic growth is very context-specific. Not only it can

be contended that the different types of agglomeration externalities might be related to

different aspects of economic growth (employment growth, productivity growth, firm

survival, new births; see e.g., Frenken et al. 2007), but it also seems that the effect of

agglomeration externalities on economic growth turns out to be contingent on 1) the sector,

2) the time period, and 3) the geographical area under consideration. First of all, the effect of

agglomeration externalities on economic growth is likely to be sector-dependent. Some

sectors generate and profit more from particular agglomeration externalities than other firms.

This can be related to, for instance, the nature of the product a sector produces, and the

knowledge-intensiveness of a sector. As these factors naturally differ across industries,

sectoral heterogeneity in the effect of agglomeration externalities is to be expected.

According to Krugman and Venables (1995), a distinction between manufacturing and 9

services is of utmost importance here. As services are quintessentially immaterial and non-

physical, and therefore non-tradable, they have to be produces closed to customers. On the

contrary, manufacturing is primarily concerned with the production of material and physical

goods, and for this reason can base their location decisions on other criteria. However, one

should take account of the existing heterogeneity within both the manufacturing and services

sector, as also within these two broad categories, different economic activities differ in the

way they are produced.

Secondly, it is likely that the effect and relative importance of agglomeration externalities

may differ across time periods. In this respect, their effect is expected be dependent on the

stage in the life cycle of a sector (product life cycle) and the phase of the economic cycle

(Combes 2000). In this respect, Krugman (1991) and Glaeser et al. (1992) suggest that

agglomeration externalities are particularly important in the early stages of an industry’s life

cycle. Gordon and McCann (2000: 518) argue in this respect that “agglomeration

externalities are seen as being particularly important to businesses unable to exploit internal

economies of scale”. Finding also time-varying effects of agglomeration externalities,

Duranton and Puga (2001) conclude that new firms and sectors tend to develop in cities with

a strong heterogeneous social-economic structure and move to specialized industrial districts

when they enter a more mature phase of the life cycle. In other words, agglomeration

externalities that promote the initial growth of a sector in a given area do not have to be the

same externalities that affect its future growth. Analyzing local employment growth (county-

level) in several British manufacturing sectors for the period 1841-1971, Neffke (2007) finds

across all industries a declining importance of agglomeration externalities over time.

Referring to the decreasing importance of physical proximity, Neffke concludes that this

finding can be attributed to the decreasing importance of the local level as relevant economic

unit of analysis. This conclusion is confirmed by Coe and Townsend (1998), who argue that

firms gradually move from localized advantages to regionalized advantages.

Thirdly, the effect of agglomeration externalities on economic growth may vary over

geographical contexts and is moderated by socio-institutional factors. According to Martin

(1999), spatial agglomeration models suffer from being too abstract and oversimplified as in

the end they neglect real places:

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‘While these studies may proved some support for the role of increasing returns and externalities in spatial

agglomeration, their neglect of a host of important forces that also influence the geographical distribution of

industry and economic activity (such as the role of local infrastructure, local institutions, state spending and

intervention, regulatory arrangements, foreign investment and disinvestment, and global competition), severely

limit their explanatory power’ (Martin 1999: 70).

The spatial scale on which the effect of agglomeration externalities on economic growth is

studied seems to be of pivotal importance.

2.3.3 The Scale-Dependency of Agglomeration Externalities

Various agglomeration externalities may differ with respect to their reach and the scale on

which they are present. As Rosenthal and Strange (2004) rightly remark, the discussion of

agglomeration externalities starts off with the general idea that geographical proximity to

other economic actors takes in a number of key advantages. Paradoxically, it is the

geographical proximity that is badly operationalized in spatial econometric models.

Traditionally, most empirical studies that try to investigate the relationship between

agglomeration externalities and economic growth use the functional region, often embodied

in Metropolitan Statistical Areas (MSAs), as unit of analysis (see e.g., Glaeser et al. 1992;

Henderson et al. 1995). The choice of this level as spatial unit of analysis is however

completely arbitrary and foremost a result of data limitations. Moreover, there are a number

of problems with using functional regions as unit of analysis.

First of all, treating agglomeration externalities as spatially fixed (as is done in many

empirical studies, e.g., Glaeser et al. 1992; Henderson et al. 1995) is very unsatisfactory, as it

assumes that economic activities outside a certain territory do not have any effect on the

economic activities within that territory (Rosenthal & Strange 2003). Despite the growing

awareness in the theoretical geographical literature on spatially expansive agglomerations,

which is reflected in the introduction of new terms like ‘economic suburbanization’, ‘edge

cities’, ‘borrowed size’, ‘polycentricity’, and ‘splintering urbanism’, this development has

hardly ever been addressed in the empirical literature on economic externalities. But spatial

zoning can be arbitrary. In geography, this problem is better known as the Modifiable Areal

Unit Problem (MAUP) (Openshaw 1984) and concerns both the problem of the aggregation

of smaller spatial units into larger units (scaling) and alternative allocations of zonal

boundaries (gerrymandering). For example, in Figure A, it is shown that measuring the 11

spatial concentration of firms can be affected by the zoning used (Arbia 2001). Already in the

1930s, Gehlke and Biehl (1934) indicated that correlation coefficients can differ by the

number and size of the spatial units under observation. As Openshaw and Taylor (1979), but

also Arbia (1989) have shown, outcomes of spatial research can be varied by changing the

zonal boundaries, which can quickly result in a misinterpetation of individual-based

inferences based on area-based inferences, which is more commonly referred to as a

‘ecological fallacy’.. Accordingly, the scale at which growth is analyzed is essential. Sectoral

employment growth in municipalities may have a different common denominator than

sectoral employment growth at the regional level. Despite repeated warnings in the related

geographical literature (see e.g., Kephart 1988, Wrigley 1995, Petterson 2001), the urban

economic modeling tradition on agglomeration externalities has not taken up this issue

seriously

Figure A: Modifiable Areal Unit Problem

* * ** * *

* * ** * *

Minimum concentration Maximum concentration Intermediate concentration

Based on Arbia (2001); An asterisk represent a firm. Figures a) and b) illustrate theaggregation or gerrymandering problem, figures b) and c) indicate the scaling problem.

Secondly and related to the previous point, agglomeration externalities may well operate on a

smaller scale than the region, such as municipalities, boroughs, or even neighborhoods (Van

Soest et al., 2006) and likewise may well reach beyond the geographic definition of a

functional region (Anselin et al., 1997). An adjacent problem here is that it is unclear at

which level spatial clustering takes place. When examining the theoretical and empirical

literature on economic agglomerations, the size of agglomerations remains in fact rather

unclear. Whereas Silicon Valley incorporates a large part of California, Route 128 is only a

small area surrounding the city of Boston. Similarly, the ‘Third Italy’ encompasses over 1/3

of the country, while Hollywood is just a district of the city Los Angeles. The apparent

varying magnitude of agglomerations often causes confusion with regard to the definition and

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demarcation of this concept. Focusing on only one spatial scale neglects the possible

availability of agglomeration externalities at different spatial scales (Olsen 2004). In

Hoover’s (1948) and Isard’s (1956) distinction between localization and urbanization

externalities the spatial scale on which these externalities operate was a guiding principle

(McCann 1995). Whereas urbanization externalities were thought to operate at the

metropolitan level, localization externalities were associated with a more local action radius.

In other words, externalities related to market-size might operate on a larger regional space

than technological and knowledge-related externalities, which seem to be more localized

(Martin 1999). It is a misunderstanding to assume that agglomeration externalities operate

only on one level (Scott 1982). Moreover, also within a particular type of agglomeration

externalities variation may exist. For example, within localization externalities labor market

pooling may work on a larger spatial scale than knowledge spillovers.

In sum, the spatial scope of agglomeration economies remains opaque, while their effects

seem to depend on the spatial scale on which they are studied (Van Oort 2004). This can

either be attributed to the Modifiable Areal Unit Problem or the possible availability of

agglomeration externalities at different spatial scales. It is the geographical scale of

agglomeration externalities that will be the focus of our empirical analyis, holding the

sectors, time period, and geographical area under consideration constant, and controlling for

spatial dependence and the reach of agglomeration externalities.

3. Spatial Autoregression Models for Sectoral Employment Dynamics

3.1. Data and Empircal Setting

A spatially detailed dataset on establishment employment (LISA 2006 with approximately

650.000 yearly observations) in the Netherlands is used to construct our dependent variable

and the indicators of the various types of agglomeration externalities. Secotoral concentration

growth (increase in the number of employees per square kilometre) is observed for the period

1996-2004. In order to analyze the scope of agglomeration externalities and their effect on

the different spatial scales, the dataset was aggregated to the level of municipalities (local

level, N=483, average surface area 70 km2), economic geographic areas (district level,

N=129, average surface area 264 km2) and labour market regions (regional level, N=40,

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average surface area 850 km2).4 To compare, the average size of the ZIP code areas analyzed

by Rosenthal and Strange (2003) is comparable with the size of the regions we use in our

analysis, while the French employment areas analyzed by Combes (2000) have an average

surface area of about 1600km2.

As we are interested in estimating separate sectoral spatial models, aggregations were made

across 2-digit basic sectors. Non-basic sectors were excluded from the analysis. More

specifically, we focus on six broad sectors: capital-intensive manufacturing, labor-intensive

manufacturing, knowledge-intensive manufacturing, wholesale trade, financial services, and

producer services. An overview of the 2-digit sectors included in the respective categories is

provided in Appendix I.

3.2 Variables

This research tries to shed some light on the geographic scale of agglomeration externalities

by looking at the effect of localization, urbanization and Jacobs externalities on economic

growth at different spatial scales. Our dependent variable, SECTORAL CONCENTRATION

GROWTH (1996-2004) is measured as the mean-corrected increase in the number of

employees per square kilometre for a given sector in the spatial unit of observation. As

indicated above, these spatial units can either be municipalities, economic geographic areas

or functional regions. Although the mainstream empirical literature has most often used a

relative measure of economic growth (like the percentage increase in sectoral employment),

we think that an absolute measure of growth controlled for the size of a spatial unit, can more

accurately answer the most pressing question in the clustering literature, namely why certain

industries choose to concentrate in a particular area. LOCALIZATION EXTERNALITIES or

agglomeration externalities stemming from sectoral concentration are measured by the

number of employees in a given sector in the spatial unit of observation divided by the total

national number of employees in that sector (in the base year 1996). Note that we have

chosen here for an absolute measure of concentration (global specialization) instead of a

relative measure of specialization (local specialization, like the commonly used location

quotients) as localizaiton externalities are most often associated with the clustering of a

certain sector in a particular area. URBANIZATION EXTERNALITIES or agglomeration

externalities stemming from market size are measured by means of the population density

144 Aggregations were based on the 2004 spatial classifications of Statistics Netherlands.

(1996), i.e. the number of inhabitants per square kilometre. JACOBS EXTERNALITIES or

agglomeration externalities stemming from diversity are measured by means of a Gini-

coefficient (1996).5 This indicator assesses how evenly employment in a particular area is

spread across economic sectors. More specifically, the Gini-coefficient measures the absence

of sectoral diversity in the spatial unit under observation:

, ,1 1

1 |2

n n

|g i g j gi j

GINI s sn = =

= ∑∑ −

(1)

in which si(j) represent the area’s i(j) shares of employment in sector g. This area-based Gini-

coefficient has a value of zero if employment shares among industries are distributed

identically to that of the total employment in the reference region. Lower values of the Gini-

coefficient thus implicate higher degrees of sectoral diversity. For this reason, JACOBS

EXTERNALITIES are in the tables referred to as LACK OF DIVERSITY.

Logarithmic transformations are applied to adjust for the non-normal statistical distribution of

the dependent and independent variables. Other variables that might be related to area-based

economic growth, such as wage rate and growth in wage rates, degree of competition, growth

in business sites, growth of the number of dwellings, and quality of the local labor force and

which may cause spatial heterogeneity (i.e., non-constant model across spatial observations)

are controlled for by using fixed effects at the supra-regional level (pseudo NUTS-2, N=10,

based on urban constellations; see Appendix II 6), if proven necessary. It is expected that this

level of fixed effects control is conceptually most suitable, because heterogeneity in the

aspects mentioned is embedded in this level (see Appendix II).

3.3 Model and Research Methodology

In most empirical research that has examined the relationship between agglomeraiton

externalities and economic growth, the specification of area-based sectoral employment

growth as a function of the extent of local specialization in that industry, the local market size

5 An often used alternative measure of diversity, which bears resemblance to the Gini Index, is the Hischman-Herfindahl Index (see e.g. Henderson et al. 1995).

15

6 We make here use of urban networks, which can be regarded as functional spatial units at the supra-regional level. These urban networks can be regarded readily accessible network of cities and areas which, given their mutually dependent specializations and their variety, create a favourable setting for economic production (Van Oort et al. 2007).

and local industrial diversity has been the most common way to investigate the relationship

between agglomeration externalities and economic growth (see e.g., Glaeser et al. 1992;

Henderson et al. 1995). As indicated above, not controlling for spatial dependency has been

a widely spread criticism on these models. In other words, it should be taken into account that

economic activities tend to shun (artificial) administrative boundaries and that it is therefore

natural to assume that economic activities outside a certain territory can affect the economic

activities within that territory. One way to account for the role of proximity and territorial

spillovers would be to reformulate the traditional models, in which areas are treated as clubs,

as spatial lag models (Anselin 1988):

y Wy Xρ β ε= + + (2)

in which y denotes an Nxi vector of spatially measured dependent variable sectoral

concentration growth, the spatial coefficient ρ refers to the strength of concentration growth

linkages over space, Wy is a spatially lagged dependent variable for weight matrix W, which

incorporates the distances between locations. X is a matrix of observations on the

independent or explanatory variables, β is the coefficient vector, and ε a coefficient for

disturbances. As we are also interested in explicitly modelling the scope of agglomeration

externalities, we expand the spatial lag model by including also spatially lagged independent

variables (Durbin-model):

y Wy X WXρ β λ γ ε= + + + (3)

in which λ now represents the coefficients of the spatial lag and WXγ signifies the spatially

lagged independent variables. In our research, the elements of the W matrix are the row-

standardized reciprocals of distance in kilometers between pairs of spatial units.7 We tested

for the significance of first (W_1), second (W_2) and third (W_3) order inverse distance

weights. Trial and error of the specifications revealed that the first order distance weights

captures the spatial correlation of sectoral employment growth at the local level best, while

second order distance weights are most significantly attached to district and regional level if

spatial dependence was observed at all. This indicates the (unsurprising) existence of a larger

16

7 It should however be noted that several alternative specifications of the W matrix are possible (like contiguity, functional distance) and it is not possible to say definitely which one is “best” (Griffith & Lagona 1998)

extent of the spatial lag at the lowest spatial level. Heteroskedasticity (the situation in which

the random regression error does not have a constant variance over all observations), when

indicated by the Breusch-Pagan or Koenker-Bassett test, was modeled by means of supra-

regional fixed effects.8 Estimating the final models, we apply the following procedure. First

of all, we run an OLS and check whether there is spatial dependence, spatial residual

correlation and/or spatial heteroskedasticity.9 If there is no spatial autocorrelation or

heteroskedasticity, then OLS suffices. If spatial autocorrelation and/or heteroskedasticity are

detected, the models are recalculated with the spatial lag of the independent and dependent

variables, including spatial error dependence and/or fixed effects.

Over the years, the consensus has emerged in the regional sciences literature that

agglomeration externalities enhance local and regional productivity and employment growth.

However, the causality of this relationship is far from clear. On the one hand, a spatial

concentration of economic activities is often associated with a number of benefits such as

labor market pooling, accessibility to intermediate goods and knowledge, and proximity to

consumers, which would augment productivity and employment. On the other hand, firms

and skilled workers may also be attracted to urban areas because of the presence of higher

productivity or higher urban wages respectively (Rosenthal & Strange, 2004). In line with

previous research on the relationship between agglomeration externalities and economic

growth (e.g., Glaeser et al., 1992; Henderson et al., 1995), the independent variables are

operationalized using reasonably lagged levels of past conditions (8 years) of the areas under

observation to control for this endogeneity problem.10

4. Empirical Findings: Shifting Boundaries, Shifting Results

4.1 The Scale-Dependency of Agglomeration Externalities

8 Inclusion of fixed effects was also assessed by means of a Hausman test. 9 The LM (ρ) tests for the significance of the spatial dependence coefficient. LM (λ) tests for additional spatial residual correlation in the spatial lag models. LM (BP) tests for homoscedasticity of regression errors. Using the Breusch-Pagan Lagrange Multiplier Test for normal distributed errors or the Koenker-Bassett Test for non-normal distributed errors if the Jarque-Bera Test on Normality of Errors is rejected. 10 In a related research (Burger et al., 2007), we try to shed light on the causality relationship between agglomeration externalities and economic growth by employing a Granger causality test using panel data (Hurlin & Venit, 2001).

17

Table 3 provides an overview of the (explorative) OLS and spatial models for all six broad

sectors at the three different spatial scales (the estimated models can be found in detail in

Appendix III). From the table and the regression output in the Appendices, it becomes clear

that most spatial models for sectoral concentration growth are complex in form. In general, it

can be inferred from these models that there are apparent differences across sectors.

Localization externalities are more positively related to employment growth in trade and

services than to employment growth in industrial employment. This is in line with earlier

findings by Van Oort (2007), but contradicts the conclusions of the research conducted by

Henderson et al. (1995). Similar to the research conducted by Moomow (1988) and Wrigley

(1995), we find that urbanization externalities are more positively associated with

employment growth in trade and services than with employment growth in manufacturing. In

line with Glaeser et al. (1992), Jacobs externalities are by and large the most positively

related to sectoral employment growth.11

Looking at the municipal level, the LM-statistics reflect that spatial dependence is present:

local sectoral growth patterns are dependent on the growth patterns of one’s neigbors

(W_GROWTH) and for most sectors agglomeration externalities reach well beyond the

borders of the own municipality. Only for capital-intensive manufacturing the degree of

spatial dependence appears to be minimal as no significant effect of either the lagged

dependent variable or lagged independent variables was found. Municipal employment

growth in financial services appears to be spatially dependent, yet unrelated to the lagged

versions of the agglomeration externalities. From the first two columns of Table 3 it can also

be concluded that the local and lagged versions of agglomeration externalities often do not

yield similar effects and are even sometimes diametrically opposed in their relation to local

employment growth patterns. For instance, for producer services, the local indicator for

urbanization externalities is positively related to municipal employment growth (β = 0.82, z =

2.21, p=0.027), while the spatially lagged indicator urbanization externalities is negatively

related to municipal employment growth (γ = -7.95, z = -5.14, p<0.01). Likewise, a local lack

11 Despite the inclusion of spatial lags and/or fixed effects, it appears that in the spatial lag estimations there often remains

some ignored spatial lag dependence, spatial error dependence and/or heteroskedasticity in the model, which would naturally

merit further attention. This is either possible through the addition of other independent variables which capture the variance

in the dependent variable better, including spatial regimes or by respecifying the fixed effects term. This is however beyond

the scope of this paper.

18

of diversity (β = 1.03, z = 2.31, p=0.021) shows an inverse relation to sectoral concentration

growth in producer services compared to its spatially lagged version (γ = -12.6, z = -4.13,

p<0.01). These findings indicate that the operation and availability of agglomeration

externalities might differ across different spatial scales.

Comparing the municipal level with the district and regional level estimations, it can be

observed that the spatial lag specifications at the municipal level are, as expected, more often

significant than at the higher spatial scales. Whereas the first order (inverse) distance weights

capture the spatial correlation of dependent variable at the municipal level while for the

district and regional level the second order distance weights fits the data best. This means that

the spatial correlation of the dependent variables attenuates relatively stronger with distance

at the district and regional level than at the municipal level. No spatial dependencies are

observed for capital-intensive manufacturing, wholesale trade, and producer services at both

the district and regional level. Moreover, if spatial dependencies are found and controlled for

by spatial lag estimation, the effect of the lagged agglomeration externalities variables on

district and regional employment growth is only marginal. This point outs that agglomeration

externalities do typically not reach further than (just beyond) the district or regional level.

Secondly, it can be deduced that local sectoral employment growth (in municipalities) is

often differently related to externality indicators than sectoral employment growth at the

district or regional level. In fact, no clear pattern can be observed. For example, for wholesale

trade, we find a positive effect of regional urbanization externalities on regional employment

growth (β = 0.88, t = 26.6, p<0.01). On the contrary, local urbanization externalities are

negatively related to local employment growth in wholesale trade (β = -1.80, z = -3.21,

p<0.01). In the same fashion, local Jacobs externalities turn up to be positively related to

local employment growth in wholesale trade (β = -1.80, z = -3.21, p<0.01), while there

appears to be no effect of regional Jacobs externalities on regional sectoral employment

growth in the same sector (β = 0.07, t = 0.67, p=0.505). Similar results are found for the other

five broad sectors we analyzed; only for labor-intensive manufacturing the effects of

agglomeration externalities on employment growth is relatively independent of scale. Again,

this supports the hypothesis that the functioning of agglomeration externalities may differ

across spatial scales.

19

4.2 Examining Agglomeratin Externalities and the Modifiable Areal Unit Problem

Although the findings can very easily be attributed to the scale-dependency of agglomeration

externalities, this is only part of the story. These models also appear to be non-robust in

nature, attributable to the Modifiable Areal Unit Problem (Openshaw & Taylor 1979). To

recap, the MAUP comprises the potential influence of spatial composition and aggregation

effects on empirical outcomes in macro-economic models. In terms of the MAUP, in our

research the difference between the local level of analysis on the one hand and the district and

regional level on the other, is clearly an example of a scaling problem, whereas the difference

between district and regional level is both a scaling and gerrymandering problem.

Focusing on results that cannot solely be explained by the scale-dependent effects of

agglomeration externalities, we find for wholesale trade that local urbanization externalities

are negatively associated with municipal employment growth (β = -1.80, z = -3.21, p<0.01).

Moreover, it also predicted that when a municipality’s neighbors are densely populated this

would detriment municipal employment growth in this sector (λ = -10.30, z = -4.56, p<0.01).

However, estimating the model at the regional level, it is surprisingly predicted that regional

urbanization externalities are positively associated with regional employment growth in

wholesale trade (β = 0.88, t = 26.6, p<0.01). Comparing the municipal level with the district

level (β = 0.43, t = 2.44, p=0.016), we find the same remarkable result. Likewise, we find for

financial services a positive effect of districtional localization externalities on districtional

employment growth (β = 0.53, z = 4.27, p<0.01) as well as a (weakly) positive effect of the

spatially lagged localization externalities on districtional employment growth (λ = 0.81, z =

1.66, p=0.097). However, the association between regional localization externalities and

regional employment growth in financial services turns out to be negative (β = -0.39, z = -

1.85, p=0.064).

However, there are more strange results that cannot easily be related to differences in the

availability of agglomeration externalities at different spatial scales. For example, for

knowledge-intensive manufacturing we find a positive effect of Jacobs externalities on

employment growth at both the municipal (β = -0.32, z = -4.20, p<0.01) and regional level (β

= -1.68, z = -3.18, p<0.01). Moreover, we also find for both spatial scales that neighboring

diversity boosts employment growth for that sector (municipal level: λ = -1.37, z = -4.45,

p<0.01; regional level: λ = -3.44, z = -1.73, p=0.047). However, looking at the district level,

20

which can be regarded as a spatial scale in between municipalities and regions, we find

unexpectedly no effect of Jacobs externalities on employment growth in knowledge-intensive

manufacturing (β = -0.09, t = -1.17, p=0.24) as well as no neighboring diversity effect.

Almost identical results are found in this respect for capital-intensive manufacturing

(localization externalities), financial services (urbanization externalities), and labor-intensive

manufacturing (Jacobs externalities).

Finally, it is at least striking that the spatial correlation of dependent variable is higher at the

regional level than at the district level. On a similar note, agglomeration externalities appear

to reach relatively further when analyzed on the regional level than when analyzed on the

district level, while regions are on average about four times the size of a district. For

example, for knowledge-intensive manufacturing and financial services neighboring

localization at the regional level is negatively associated with sectoral employment growth,

while at the district level no such effect is found. It remains however difficult to strictly

separate the effect of the different functioning of agglomeration externalities at different

spatial scales from the distortion of results by the Modifiable Areal Unit Problem. In fact, this

inconsistency and non-robustness of the models across spatial scales seriously questions the

validity of these models to examine the effect of agglomeration externalities on economic

growth.

21

Table 3. Summary of Regression Results by Sector, Agglomeration Externality and Spatial Level (see Appendix III, Table III.1.-III.6.) Municipality District Region Localization Externalities LOC W_LOC LOC W_LOC LOC W_LOCCapital-Intensive Manufacturing − − − 0 0 x − − xLabor-Intensive Manufacturing 0 0 − − − x − − − 0Knowledge-Intensive-Manufacturing − − − − − − − − x − − − − − − Wholesale Trade 0 0 + x 0 xFinancial Services 0 0 + + + + − − Producer Services 0 0 0 x + + + x Urbanization Externalities URB W_URB URB W_URB URB W_URBCapital-Intensive Manufacturing + + + 0 − − − x − − − xLabor-Intensive Manufacturing − − − − − − − x − 0Knowledge-Intensive-Manufacturing 0 + + + − − − x 0 0Wholesale Trade − − − − − − + + + x + + + x Financial Services 0 0 − − 0 0 0Producer Services + + − − − + + + x + + + x Jacobs Externalities JAC W_JAC JAC W_JAC JAC W_JACCapital-Intensive Manufacturing 0 0 − − − x + + xLabor-Intensive Manufacturing 0 + + + 0 x + + Knowledge-Intensive-Manufacturing + + + + + 0 x + + + + + Wholesale Trade + + + + + + 0 x 0 x Financial Services + + + 0 + 0 0 + Producer Services − − + + + − x − − x + = positive and significant effect; − = negative and significant effect; 0 = no significant effect; x = no spatial lag estimated. The number of signs indicates the degree of significance (either at 1%, 5% or 10% level). NB: JAC and W_JAC refer to the inverse of the Lack of Diversity variable used in the analyses; a + is indicating a positive and significant effect of diversity on sectoral area-based growth.

5. Discussion and Conclusions: Cleaning Up the Spatial Muddle

Human beings do not thrive when isolated from others. Likewise, agglomerations do not

operate on their own. No agglomeration is an island and therefore should not be treated as an

island in empirical research. Although every debate on the benefits of agglomeration starts

out with stressing the importance of geographical proximity, it is this same geographical

proximity that is badly operationalized in spatial econometric models. Spatial autoregression,

with enables to model territororial spillovers, offers a solution to this problem as it controls

for spatial dependencies. However, the effect of agglomeration externalities on area-based

sectoral employment growth also appears to be dependent on the initial spatial scale taken

into consideration. Holding methodology and context constant, we find that over three spatial

scales in the Netherlands the areal and spatially lagged versions of agglomeration

externalities often have contradictionary effects on sectoral employment growth. Likewise,

the effects of agglomeration externalities on sectoral employment growth varies over scales.

Although it can be inferred that agglomeration externalities most often do not reach further

than (just beyond) the district or regional level, sectoral employment growth in municipalities

has often a different common denominator than sectoral employment growth at the district or

regional level. No clear pattern can be observed. This would support the hypothesis that

agglomeration externalities are present at different spatial scales and their working differs

across these scales.

However, these models also appear to be inconsisent and non-robust in nature, which might

be acribed to the Modifiable Areal Unit Problem (Openshaw & Taylor 1979). Although

spatial autoregressive models provide a partial solution to the modifiable area unit problem

(Anselin & Cho 2002), zoning still appears to be of critical importance. Following Arbia

(1989; 2001), these descrepancies due to aggregation and scaling can only be alleviated under

very restrictive conditions that not only controlfor spatial interdependence, but that also

identifies the sub-areas under consideration in terms of size, shape and neighbouring

structure. These conditions are almost impossible to meet in empirical research. As a

consequence, the outcomes of spatial research are ambiguous on shifting boundaries, either

through aggregation or different zonation. This can not only quickly result in drawing wrong

conclusions with respect to the question which kind of concentration of economic activities is

needed to foster local and regional economic growth, but also in a misinterpetation of

individual-based inferences based on area-based inferences. In other words, on the basis of

these models it is almost impossible to draw conclusions with respect to what kind of

environment would be most beneficial for firms. Doing so, would naturally result in an

ecological fallacy.

Although the MAUP effect is difficult to separate from the effect of the scale-dependent

availability of agglomeration externalities, the obtained results are plausible in the sense that

a single highly simplified theory cannot accurately explain a complex phenomenon. This has

important implications for spatial-economic policies aiming at economic growth in the sense

that it is not recommended to draw any policy implications from this type of urban economic

models. This last point is motivated by the fact that one should also take into account that

these empirical models are sensitive to the period, place and sector under consideration (i.e.,

they seems to be strictly context-specific), how the agglomeration externalities are

operationalized, for which other effects is controlled, and which aspect of economic growth

one is studying (see e.g., Smit et al. 2007). In the end, the specification and eventual

outcomes of these empirical models depends too much on arbitrary selection and data

availability and not so much on theoretical considerations. This seriously questions the

construct, internal and external validity of these models.

How can we clean up this spatial muddle? One way to proceed is to move from a meso-

economic approach to a micro-economic approach. Using continuous space modelling (Arbia

2001) or multilevel analysis (Hox 2002), we should take the firm level more seriously by

taking individual firms, cohorts of individual firms or even entrepreneurs as lowest unit of

analysis. Continuous space models, in which the firm in space is taken as basic spatial unit,

can alleviate the Modifiable Areal Unit Problem in the sense that the models are freed from

zonation, while with multilevel modelling the scale-dependent effects of agglomeration

externalities on firm growth are appropriately taken care of. After identification of a model at

the micro spatial level, we can then more accurately draw policy implications at the meso-

level.

24

References:

Anselin, L. (1988), Spatial Econometrics: Methods and Models, Dordrecht: Kluwer.

Anselin, L., A. Varga & Z. Acs (1997), ‘Local Geographic Spillovers between University

Research and High Technology Innovations”, Journal of Urban Economics, 42, 422-448.

Anselin, L. & W.K.T. Cho (2002), ‘Spatial Effects and Ecological Inference’, Political

Analysis, 10, 276-297.

Arbia, G. (1989), Spatial Data Configuration in Statistical Analysis of Regional

Economic and Related Problems, Dordrecht: Kluwer.

Arbia, G. (2001), ‘Modellling the Geography of Economic Activities on a Contiuous

Space’, Papers in Regional Science, 80, 411-424.

Burger, M.J., F.G. van Oort & O. Raspe (2007), ‘On the Relationship between

Agglomeration Externalities and Economic Growth: Tests of Granger Causality’, Mimeo,

Erasmus University Rotterdam.

Castells, M. (2002), ‘Local and Global: Cities in the Network Society’, Tijdschrift voor

Economische en Sociale Geografie, 93, 548-558.

Coe, N.M. & A.R. Townsend (1998), ‘Debunking the Myth of Local Agglomerations: The

Development of a Regionalized Service Economy in South-East England’, Transactions of

the Institute of British Geographers 23, 385-404.

Combes, P.P. (2000), ‘Economic Structure and Local Growth: France 1984-1993’,

Journal of Urban Economics 47, 329-355.

Duranton, G. & D. Puga (2001), ‘Nursery Cities: Urban Diversity, Process Innovation

and the Life Cycle of Products’, American Economic Review, 91(5), 1454-1477.

Frenken, K., F.G. van Oort & T. Verburg (2007), ‘Related variety, unrelated variety, and

economic growth’, Regional Studies (forthcoming).

Gehlke, C. & K. Biehl (1934), ‘Certain Effects of Grouping Upon the Size of the

Correlation Coefficient in Census Tract Material’, Journal of the American Statistical

Association,

Glaeser, E.L., H.D. Kallal, A. Scheinkman & A. Shleifer (1992), ‘Growth in Cities’,

Journal of Political Economy 100, 1126-1152.

Goodman, L.A. (1959), ‘Some Alternatives to Ecological Correlation’, American Journal

of Sociology 64, 610-625.

Gordon, I.R. & P. McCann (2000), ‘Industrial Clusters: Complexes, Agglomeration

and/or Social Networks?’, Urban Studies 37, 513-532.

25

Griffith, D. & F. Lagona (1998), ‘On the quality of likelihood-based estimators in spatial

autoregressive models when the data dependence structure is misspecified’, Journal of

Statistical Planning and Inference 69, 153-174.

Harrison, B., M.R. Kelley & J. Gant (1997), ‘Innovative firm behavior and local milieu:

exploring the intersection of agglomeration, firm effects, and technological change’,

Economic Geography, 72, 233-258.

Henderson, J.V., A. Kuncoro & M. Turner (1995), ‘Industrial development in cities’,

Journal of Political Economy, 103, 1067-1085.

Hoover, E.M. (1937), Location Theory and the Shoe and Leather Industry, Cambridge,

MA: Harvard University Press.

Hoover, E.M. (1948), The Location of Economic Activity, New York: McGraw Hill.

Hox, J. (2002), Multilevel Analysis: Techniques and Applications, London: Lawrence

Erlbaum.

Huber, F. (2007), Social Networks and Knowledge Spillovers: Networked Knowledge

Workers and Localized Knowledge Spillovers, Frankfurt am Main: Peter Lang.

Hurlin, C. & B. Venet (2001), ‘Granger Causality Tests in Panel Data Models with Fixed

Effects’, Mimeo, University Paris IX.

Isard, W. (1956), Location and Space-Economy, Cambridge, MA: MIT Press.

Jacobs, J. (1969), The Economy of Cities, London: Jonathan Cape.

Kephart, G. (1988), ‘Heterogeneity and the Implied dynamics of regional growth rates: was

the nonmetropolitan turnaround an artifact of aggregation?’, Demography 25, 99-113.

Krugman, P. (1991), Geography and Trade, Cambridge, MA: MIT Press.

Markusen, A. (1999), ‘Fuzzy Concepts, Scanty Evidence and Policy Distance: The Case

for Rigour and Policy Relevance in Critical Regional Studies’, Regional Studies 33, 869-

886.

Marshall, A. (1890), Principles of Economics, London: MacMillan.

Martin, R. (1999), ‘The New “Geographical Turn” in Economics: Some Critical

Reflections’, Cambridge Journal of Economics, 23, 65-91.

McCann, P. (1995), ‘Rethinking the Economics of Location and Agglomeration’, Urban

Studies 32, 563-577.

Moomaw, R.L. (1988), ‘Agglomeration Economies: Localization or Urbanization?’,

Urban Studies 25, 150-161.

26

Neffke, F.M.H. (2007), ‘Time-Varying Agglomeration Externalities in UK Counties’,

Mimeo, Department of Economic Geography, Utrecht University.

Ohlin, B. (1933), Interregional and International Trade, Cambridge MA, Harvard

University Press.

Olsen, J. (2004), ‘On the Units of Geographical Economics’, Geoforum, 33, 153-164.

Openshaw, S. (1984), The Modifiable Areal Unit Problem, Norwich: Geo Books.

Openshaw, S. & P.J. Taylor (1979), ‘A Million or so Correlation Coefficients: Three

Experiments on the Modifiable Areal Unit Problem’, in N. Wrigley, Statistical

Applications in the Spatial Sciences, London: Pion, pp. 127-144.

Parr, J.B. (2002), ‘Missing Elements in the Analysis of Agglomeration Economies’,

International Regional Science Review, 25, 151-168.

Pettersson, O. (2001), ‘Microregional Fragmentation in a Swedish country’, Papers in

Regional Science, 80, 389-409.

Phelps (2004), ‘Clusters, Dispersion and Spaces in Between: For an Economic

Geography of the Banal, Urban Studies 41, 971-989.

Piore, M.J. & C.F. Sabel (1984), The Second Industrial Divide: Possibilities for

Prosperity, New York: Basic Books.

Ponds, R., F.G. van Oort & K. Frenken (2007), ‘Internationalization and Regional

Embedding of Scientific Research in the Netherlands’, Working Paper, Utrecht

University.

Porter. M. (1990), The Competitive Advantage of Nations, New York: Free Press.

Robinson, W.S. (1950), ‘Ecological Correlations and the Behavior of Individuals’,

Sociological Review 15, 351-357.

Rosenthal, S.S. & W.C. Strange (2003), ‘Geography, Industrial Organization, and

Agglomeration’, The Review of Economics and Statistics, 85(2), 377-393.

Rosenthal, S.S. & W.C. Strange (2004), ‘Evidence on the Nature and Sources of

Agglomeration Economies’, in J.V. Henderson & J.F. Thisse, Handbook of Regional and

Urban Economics: Cities and Geography.

Scott, A.J. (1982), ‘Locational Patterns and Dynamics of Industrial Activity in the

Modern Metropolis’, Urban Studies, 19, 111-142.

Siegel, P., T. Johnson & J. Alwang (1995), Regional Economic Diversity and

Diversification, Growth and Change, 26, 261-284.

27

Smit, M.J., H.F.L. de Groot & J. Poot (2007), ‘Determinants of Growth in Cities: A Meta-

Analysis’, Mimeo, Department of Spatial Economics, Vrije Universiteit Amsterdam.

Van der Panne, G. (2004), ‘Agglomeration Externalities: Marshall versus Jacobs’, in

Journal of Evolutionary Economics, 14, 593-604.

Van Soest, D.P., S. Gerking & F.G. van Oort (2006), ‘Spatial Impacts of Agglomeration

Externalities’, in Journal of Regional Science, 46(5), 881-899.

Van Oort, F.G. (2004), Urban Growth and Innovation: Spatially Bounded Externalities in

the Netherlands, Aldershot: Ashgate.

Van Oort, F.G. (2007), ‘Spatial and Sectoral Composition Effects of Agglomeration

Economies in the Netherlands’, Papers in Regional Science, 86(1), 5-30.

Van Oort, F.G., M.J. Burger & O. Raspe (2007), ‘Inter-Firm Relations and Economic

Clustering in the Dutch Randstad Region’, Working Paper, Utrecht University.

Wrigley, N. (1995), ‘Revisiting the Modifiable Areal Unit Problem and the Ecological

Fallacy’, in A.D. Cliff, P.R. Gould, A.G. Hoare & N.J. Thrift (eds), Diffusing Geography,

Oxford: Blackwell, pp. 49-71.

Yule, G.U. & M.G. Kendall (1950), An Introduction to the Theory of Statistics, London:

Griffin.

28

Appendix I Categorization of 2-Digit Sectors Used in Analysis of Concentration Growth Capital-Intensive Manufacturing Food & Beverage Industry

Tobacco Industry Paper Industry Synthetic & Rubber Industry Glass & Ceramic Industry

Labor-Intensive Manufacturing Textile Industry Apparel Industry Leather Goods Industry Timber Industry Metal Products Industry Furniture Industry Recycling Industry

Knowledge-Intensive Manufacturing & Process Industries

Oil-Processing Industry Chemical Industry Primary Metal Industry Machinery Industry Computer Industry Electronics Industry Audio & Telecommunications Industry Cars & Other Transport Industry

Wholesale Wholesale trade (cars) Wholesale trade

Financial Services Financial Institutions (banks) Insurance & Pension Funds Insurance & Financial Services

Producer Services Publishing & Reproduction Real Estate Intermediates Movable Estate Intermediates Computer Services Research & Development Other Business Services

29

Appendix II Categorization of Urban Networks (used as fixed effects variable) 1. Supra-Region Northeast Oost-Groningen Delfzijl e.o. Overig Groningen Noord Drenthe Urban Networks: Groningen-Assen and Delfzijl/Eemshaven

2. Supra-Region North-Northeast Noord-Friesland Zuidoost-Friesland Zuidwest-Friesland Urban Networks: Westergozone (Leeuwarden en Harlingen) & Zuid-Friese Stedenband (Drachten, Sneek en Heerenveen)

3. Supra-Region Northwest Kop van Noord-Holland Alkmaar e.o. IJmond Urban Network: Noordwest 8 (Den Helder, Hoorn, Enkhuizen, Alkmaar)

4. Supra-Region East Zuidoost-Drenthe Zuidwest-Drenthe Noord-Overijssel Twente Urban Networks: Bandstad (Enschede, Hengelo, Almelo), Zwolle-Kampen & Zuid-Drentse Stedenband (Meppel, Hoogeveen, Emmen, Coevorden, Hardenberg en Steenwijk)

5. Supra-Region Central-East Zuidwest-Overijssel Veluwe Arnhem-Nijmegen Achterhoek Urban Networks: Knooppunt Arnhem-Nijmegen, Stedendriehoek (Deventer, Zutphen, Apeldoorn) & Wageningen-Ede-Rhenen-Veenendaal.

6. Supra-Region Randstad-North Utrecht Haarlem Zaanstreek Groot-Amsterdam Het Gooi en Vechtstreek Flevoland Urban Network: Deltametropole North (Amsterdam-Haarlem-Zaanstad-Utrecht-Hilversum-Almere)

7. Supra-Region Randstad South Agglomeratie Leiden en Bollenstreek Agglomeratie Den Haag Delft en Westland Groot-Rijnmond Oost-Zuid-Holland Zuid-Oost-Zuid-Holland Urban network: Deltametropole South (The Hague-Rotterdam-Leiden-Delft-Gouda-Dordrecht)

8. Supra-regio Southwest Zeeuwsch-Vlaanderen Overig Zeeland Urban network: Scheldemondsteden (Middelburg, Vlissingen, Terneuzen)

9. Supra-regio South West-Noord-Brabant Midden-Noord-Brabant Noordoost-Noord-Brabant Zuidoost-Noord-Brabant Zuidwest-Gelderland Urban networks: Brabantstad (Breda, Tilburg, Den Bosch, Eindhoven, Helmond) & Brabantse Buitensteden (Roosendaal en Bergen op Zoom)

10. Supra-Region Southeast Noord-Limburg Midden-Limburg Zuid-Limburg Urban Networks: Venlo-Roermond & Maastricht-Heerlen

Appendix III : Regression Results by Sector 30

Table III.1 Capital-Intensive Manufacturing Table III.2 Labor-Intensive Manufacturing Table III.3 Knowledge-Intensive Manufacturing Table III.4 Wholesale Trade Table III.5 Financial Services Table III.6 Producer Services

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Table III.1. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Capital-Intensive Manufacturing Municipal Level (W_1) District Level (W_2) Regional Level (W_2)

Explanatory Variables OLS spatial lag estimation OLS OLS fixed effects OLS CONSTANT -6.14 (0.11)** -5.19 (1.57)** 3.27 (0.71)** 3.26 (0.88)** 0.69 (0.96) W_GROWTH -0.06 (0.81) LOCALIZATION -0.29 (0.02)** -0.12 (0.04)** -0.04 (0.07) 0.05 (0.09) -0.26 (0.10)* W_LOCALIZATION 0.14 (0.17) URBANIZATION 1.07 (0.10)** 0.77 (0.12)** -0.51 (0.07)** -0.53 (0.09)** -0.54 (0.09)** W_URBANIZATION 0.27 (0.60) LACK OF DIVERSITY 1.36 (0.14)** -0.14 (0.38) 0.28 (0.05)** 0.29 (0.05)** -0.73 (0.31)* W_LACK DIVERSITY 1.15 (1.12) Summary Statistics N 483 483 129 129 40 -2LL -1065 -847 -161 -155 -23.2 Akaike IC 2138 1728 332 337 54.4 LM (BP/KB) 130** 112** 21.6** 25.0* 0.95 LM (ρ) 324** 0.02 1.98 LM (λ) 222** 0.40 2.49 LM λ (ρ) 0.05 LR (ρ) 0.05Fixed Effects Yes Yes **p<0.01, *p<0.05, #p<0.10 LM (BP) tests for homoskedasticity of regression errors using the Breusch-Pagan Lagrange Multiplier Test for normal distributed errors or the Koenker-Bassett Test for non-normal distributed errors if the Jarque-Bera Test on Normality of Errors is rejected. LM (ρ) tests for the significance of the spatial dependence coefficient. LM (λ) tests for additional spatial residual correlation in the spatial lag models (critical value 3.85 at 5% level of significance). LM λ (ρ) tests and LR (ρ) test respectively whether there is spatial error dependence or spatial lag dependence left after the spatial lag estimation.

Table III.2. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Labor-Intensive Manufacturing Municipal Level (W_1) District Level (W_2) Regional Level (W_2) Explanatory Variables

OLS spatial lagestimation

OLS OLS fixed effects OLS spatial lag estimation

CONSTANT -5.06 (0.19)** 7.65 (0.79)** 2.37 (1.10)* 2.26 (1.43) -0.50 (1.79) -13.1 (9.35) W_GROWTH 0.83 (0.10)** -0.88 (0.28)** LOCALIZATION -0.38 (0.05)** 0.03 (0.11) -0.49 (0.11)** -0.48 (0.14)** -0.27 (0.24) -0.36 (0.24) W_LOCALIZATION -0.22 (0.38) -0.48 (0.75) URBANIZATION 0.89 (0.19)** -0.44 (0.20)** -0.74 (0.11)** -0.79 (0.15)** -0.20 (0.18) -0.60 (0.29)* W_URBANIZATION -1.83 (1.01)# -0.37 (0.71) LACK OF DIVERSITY 0.90 (0.27)** -0.39 (0.24) 0.01 (0.08) 0.03 (0.08) -0.35 (0.59) -1.33 (0.70)# W_LACK DIVERSITY -5.19 (1.85)** -4.61 (2.38)# Summary Statistics N 483 483 129 129 40 40 -2LL -1364 -1036 -212 -207 -50.3 -41.8 Akaike IC 2737 2108 432 441 109 118 LM (BP/KB) 16.2** 426** 33.0** 33.0** 26.1** 25.4*LM (ρ) 50.6** 0.73 3.74** LM (λ) 81.2** 0.23 4.03* LM λ (ρ) 17.0** 1.51 LR (ρ) 13.9** 8.38**Fixed Effects Yes Yes Yes **p<0.01, *p<0.05, #p<0.10, see Table III.1. for additional explanation

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Table III.3. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Knowledge-Intensive Manufacturing Municipal Level (W_1) District Level (W_2) Regional Level (W_2) Explanatory Variables

OLS spatial lag estimation

OLS OLS fixed effects OLS spatial lag estimation

CONSTANT -1.94 (0.27)** 1.02 (0.44)* 3.24 (1.25)* 4.13 (1.51)** -6.60 (2.09)** -19,0 (7.19)** W_GROWTH 0.02 (0.07) -1.24 (0.21)** LOCALIZATION -0.41 (0.09)** -0.32 (0.08)** -0.34 (0.10)** -0.30 (0.12)* -0.86 (0.24)** -1.17 (0.21)** W_LOCALIZATION -1.37 (0.31)** -1.93 (0.63)** URBANIZATION -0.13 (0.28) -0.14 (0.16) -0.76 (0.13)** -0.92 (0.16)** -0.08 (0.21) 0.29 (0.27) W_URBANIZATION -3.74 (0.62)** -0.83 (0.63) LACK OF DIVERSITY -0.38 (0.39) -0.41 (0.19)* -0.11 (0.08) -0.09 (0.08) -1.85 (0.61)** -1.68 (0.89)** W_LACK DIVERSITY -5.03 (1.16)** -3.44 (1.73)* Summary Statistics N 483 483 129 129 40 40 -2LL -1513 -875 -220 -216 -55.8 -38.8 Akaike IC 3034 1785 449 458 113.9 111.4 LM (KB/BP) 89.5** 238** 49.5** 50.2** 24.8** 11.9 LM (ρ) 1430** 0.23 8.61** LM (λ) 1386** 0.17 5.83* LM λ (ρ) 36.6** 1.85 LR (ρ) 0.07 18.1** Fixed Effects Yes Yes Yes **p<0.01, *p<0.05, #p<0.10, see Table III.1. for additional explanation

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Table III.4. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Wholesale Trade Municipal Level (W_1) District Level (W_2) Regional Level (W_2) Explanatory Variables OLS spatial lag

estimation OLS spatial error

estimation OLS

CONSTANT 3.37 (0.47)** 18.4 (1.94)** -1.22 (1.76) -1.19 (1.90) -2.05 (0.40) W_GROWTH 0.44 (0.23)# LOCALIZATION -0.65 (0.12)** 0.12 (0.29) 0.35 (0.17)* 0.32 (0.18)# 0.06 (0.03) W_LOCALIZATION -0.74 (0.86) URBANIZATION -2.08 (0.46)** -1.80 (0.56)** 0.41 (0.15)* 0.46 (0.17)** 0.88 (0.03)** W_URBANIZATION -10.3 (2.25)** LACK OF DIVERSITY -2.47 (0.65)** -2.11 (0.68)** -0.09 (0.09) -0.10 (0.08) 0.07 (0.10) W_LACK DIVERSITY -18.8 (3.85)** 0.18 (0.18) LAMBDA

Summary Statistics N 483 483 129 129 40 -2LL -1778 -1455 -229 -1455 24.0 Akaike IC 3563 2943 468 479 -40.1 LM (BP/KB) 11.1* 380** 58.6** 57.8** 2.16 LM (ρ) 16.0** 3.29# 0.81 LM (λ) 58.8** 1.74 0.46 LM λ (ρ) 69.8** 11.3 LR (ρ) 1.96 0.67 Fixed Effects Yes Yes **p<0.01, *p<0.05, #p<0.10, see Table III.1 for additional explanation.

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Table III.5. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Financial Services Municipal Level (W_1) District Level (W_2) Regional Level (W_2) Explanatory Variables

OLS spatial lagestimation

OLS spatial lagestimation

OLS spatial lagestimation

CONSTANT 6.17 (0.56)** -0.12 (0.56) 1.19 (1.36) 8.50 (5.17) 0.32 (2.61) -14.7 (7.14) W_GROWTH 0.08 (0.04)* 0.40 (0.16)* 0.62 (0.18) LOCALIZATION -1.08 (0.82) 0.08 (0.18) 0.69 (0.13)** 0.53 (0.12)** -0.14 (0.22) -0.39 (0.21)# W_LOCALIZATION -1.55 (5.24) 0.81 (0.49)# -1.06 (0.53)# URBANIZATION -3.07 (1.08)** 0.04 (0.26) 0.24 (0.12)# 0.31 (0.13)* -0.31 (0.23) 0.06 (0.33) W_URBANIZATION -2.71 (6.04) -0.63 (0.43) 0.38 (0.52) LACK OF DIVERSITY -2.13 (1.02)* -0.96 (0.24)** -0.08 (0.06) -0.11 (0.06)# -0.35 (0.64) -0.56 (0.70) W_LACK DIVERSITY -3.74 (6.38) 0.14 (0.18) -2.96 (1.41)* Summary Statistics N 483 483 129 129 40 40 -2LL -1877 -1005 -196 -184 -53.5 -45.7 Akaike IC 3763 2044 401 402 115 107 LM (BP/KB) 29.6** 369** 55.5** 55.5** 86.7** 35.4* LM (ρ) 2291** 10.9** 13.1** LM (λ) 2158** 11.1** 14.7* LM λ (ρ) 101** 2.11# 0.09 LR (ρ) 4.52* 4.91* 8.11** Fixed effects Yes Yes No **p<0.01, *p<0.05, #p<0.10, see Table III.1. for additional explanation.

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Table III.6. OLS and Spatial Autoregression Estimates for Concentration Growth (1996-2004) in Producer Services Municipal Level (W_1) District Level (W_2) Regional Level (W_2) Explanatory Variables OLS spatial lag estimation OLS OLS fixed effects OLS CONSTANT 9.30 (0.77)** 2.05 (1.50) -9.64 (2.47)** -10.0 (2.76)** 0.42 (1.02) W_GROWTH 0.43 (0.07)**LOCALIZATION -0.87 (0.20)** -0.03 (0.10) 0.26 (0.21) 0.28 (0.23) 0.39 (0.09)** W_LOCALIZATION -0.40 (0.69) URBANIZATION -4.04 (0.74)** 0.82 (0.36)* 1.63 (0.23)** 1.62 (0.25)** 0.78 (0.09)** W_URBANIZATION -7.96 (0.36)** LACK OF DIVERSITY -4.35 (1.06)** 1.03 (0.45)* 0.20 (0.11)# 0.20 (0.11)# 0.47 (0.23)* W_LACK DIVERSITY -12.6 (3.05)** Summary Statistics N 483 483 129 129 40 -2LL -2016 -1399 -256 -249 -9.51 Akaike IC 4041 2831 521 525 27.0 LM (BP/KB) 27.2** 2011** 33.0** 39.7** 2.60 LM (ρ) 1656** 0.15 1.61LM (λ) 1588** 0.99 0.71LM λ (ρ) 0.07 LR (ρ) 31.1** Fixed Effects Yes Yes **p<0.01, *p<0.05, #p<0.10, see Table III.1. for additional explanation

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