Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain

Regional Science and Urban Economics 34 (2004) 341 – 363 www.elsevier.com/locate/econbase

Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain Adelheid Holl* Department of Town and Regional Planning, University of Sheffield, Sheffield S10 2TN, UK Received 22 May 2001; accepted 28 March 2003 Available online 27 August 2003

Abstract Using micro-level data and geographic information system (GIS) techniques, this paper analyses the impact of road infrastructure on the location of new manufacturing establishments in Spanish municipalities from 1980 to 1994, a period when most of the major road network was being developed. Poisson panel data models are estimated as they naturally allow for large sets of location choices with frequent zero outcomes and control for unobserved municipality heterogeneity. The results show that new motorways affect the spatial distribution of manufacturing establishments at the municipality level. The strength of impacts differs across sectors and space. Most benefits are concentrated near the new infrastructure, with evidence that is consistent with negative spillover effects. Firms prefer locations closer to new motorways at the cost of more distant municipalities. D 2003 Elsevier B.V. All rights reserved. JEL classification: L6; R3; R4 Keywords: Manufacturing location; Road transport infrastructure; Geographic information systems (GIS)

1. Introduction Over the last two decades, transport infrastructure has re-emerged as a key policy issue in Europe. Spain stands out as a notable example. The country has developed an ambitious road building programme, increasing its motorway network from 1933 km at the beginning of the 1980s to 7748 km by 1995. At the same time there has also been a renewed theoretical and empirical interest in firm location and the spatial distribution of economic activity. Effective spatial and transport infrastructure planning requires knowledge on the impacts of road infrastructure provision on the location of new manufacturing * Tel.: +44-114-222-6180; fax: +44-114-272-2199. E-mail address: [email protected] (A. Holl). 0166-0462/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0166-0462(03)00059-0

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activity. With decreasing transport costs and the increasing importance of non-material flows, some analysts have cast doubts on the importance of transport infrastructure as a location factor. It has also been argued that in areas where the network is already very dense impacts on firm location might actually be small (Banister and Berechman, 2000). Using micro-level data on manufacturing plant openings in Spanish municipalities from 1980 to 1994 allows an examination of the impact of road infrastructure on the pattern of new manufacturing plant location over the period when most of the major road network was being developed. The central hypothesis is that transport network improvements affect the spatial distribution of firms. The econometric model implemented to test this hypothesis takes into account the quality and improvements in road infrastructure in addition to other location factors, such as factor costs, demand, and agglomeration economies. The empirical results show that new road transport infrastructure has changed the relative attractiveness of locations for new manufacturing establishments. The paper contributes to the existing literature on plant location and transport infrastructure impacts in the following ways. First, the spatial pattern of new plant location is studied at a very detailed geographical level. While most studies have used NUTS 3 or even NUTS 2 level data, this paper focuses on municipalities (NUTS 5). Very few studies exist at this level of analysis (notable exceptions, for example, are Guimara˜es et al., 2000; Figueiredo et al., 2002), and there has been no comprehensive analysis focusing on the impact of transport infrastructure on firm location at this level. Impacts from transport infrastructure are location specific and are likely to affect firms differently depending on distances to the new infrastructure. Using large geographic units can hide important spatial variations. Most of the existing literature has focused on inter-regional variations of impacts, but overlooked intra-regional distribution effects (Haughwout, 1999). However, as Vickerman (1995) points out, with the development of a higher order transport network such as the Trans-European Networks (TENs) intra-regional distribution effects are becoming increasingly pronounced depending on differences in access to the new networks. Similarly, the literature on agglomeration economies and spatial spillovers suggest that these forces operate within limited geographic scope (Ellison and Glaeser, 1997; Audretsch and Feldman, 1996; Wallsten, 2001). Second, a critical problem faced by all studies that examine the economic effects of transport infrastructure has been quantifying transport infrastructure in a meaningful way. Most studies have used stock measures of public capital and only few studies distinguish various types of infrastructure allowing for special conclusions on road transport. The approach taken here is to use computerised Geographic Information Systems (GIS) to calculate measures of accessibility based on the road network and its development over the period studied. Special attention is paid to the importance of access to different markets for different sectors, as well as to intra- and inter-regional aspects of road transport infrastructure. Third, rather than estimating an aggregate model only, ten manufacturing sectors are considered. The estimation results confirm that different sectors have different transport requirements and, therefore, a different locational behaviour with regard to transport infrastructure. Fourth, using panel data to implement Poisson fixed effects estimations allows controlling for unobserved municipality heterogeneity and potential problems of simultaneity in firm location and the placement of new motorway projects. A fixed effects

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approach has been followed by Papke (1991) and more recently by Becker and Henderson (2000) and List and McHone (2000), but most studies in the context of firm location modelling have relied on cross-sectional associations. The paper is organised as follows: Section 2 contains a brief review of issues related to transport and industrial location. Section 3 presents the empirical model and discusses the econometric issues involved. Section 4 describes the dependent and independent variables used in the estimations. Section 5 reports the empirical results, while Section 6 summarises the main conclusions of the paper.

2. Transport and industrial location The role of transport has a long tradition in location theory. In the classical Weberian model location patterns are determined by transport and factor costs (Weber, 1929). Lo¨sch (1959) emphasised the importance of market size. More recent new economic geography models again emphasise the importance of transport costs along with imperfect competition, market size and economies of scale in explaining the location of industry (Krugman, 1991). Transport infrastructure improvements work like market integration and can change the relative importance of concentrating (market size and agglomeration economies) and dispersing forces (factor costs and competition) and consequently the spatial distribution of economic activity. Better transport connections can make areas of lower economic activity more attractive for firm location as they gain better access to markets in the core areas. But, at the same time, competition from firms in economic agglomerations may increase as they now can more easily supply locations at a distance and benefit from cost and demand linkages. With mobility of firms, the distribution of benefits from infrastructure investment is a priori not clear (Venables, 1996; Puga, 1999). Empirical findings on the impacts of transport infrastructure are inconclusive. Most aggregate studies conclude that there is a positive relation between the level of infrastructure and the level of economic growth (see, for example, Aschauer, 1989; Mas et al., 1996). Holtz-Eakin (1994) has argued that the positive relation in such studies may be due to the failure to account for endogeneity of the stock of infrastructure capital. Recently, Haughwout (1998) has criticised approaches using aggregate production functions for neglecting the role of infrastructure for individual firms. With mobility of activities over space, local markets will capitalise spatial advantages into local prices and this will affect the productivity of individual firms. Mikelbank and Jackson (2000) emphasise differences in impacts over space. Due to its network character, transport infrastructure may lead to positive or negative spillover effects among proximate areas. The sign and magnitude of spillovers has important implications for the spatial distribution of benefits arising from new transport infrastructure. Boarnet (1998) shows for Californian counties that with relocation of economic activity, infrastructure benefits are found to come at the expense of competing regions. Infrastructure improvements in one region attract new investment and reduce the attractiveness of areas that do not benefit directly from the new infrastructure. Similarly, Chandra and Thompson (2000) in a study on earnings impacts of interstate highways in US non-metropolitan counties find that new interstate highways raise earnings in counties receiving a new interstate highway and reduce earnings in adjacent counties.

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Evidence of positive spillover effects raising the attractiveness of neighbouring areas is found in Mas et al. (1996) for the Spanish autonomous communities. Voith (1993) and Haughwout (1997) provide evidence that indicates that transport investments to and in central cities have positive effects on suburban house values. Haughwout (1999) studies employment growth in US counties and finds that state infrastructure investment affects the distribution of employment within states leading to a more dispersed pattern of economic activity. While there is evidence of important redistribution effects of transport infrastructure investment at the county and metropolitan level, at the state level Holtz-Eakin and Schwartz (1995) do not find any significant productivity effects on neighbouring states. This suggests that important road infrastructure impacts operate within rather than across regions. A geographically detailed analysis of locational effects of transport infrastructure improvements that can take into account different impacts occurring at different distances from the new infrastructure is, therefore, important.

3. The empirical model The theoretical and empirical literature on industrial location shows that firm location is not a random process, but the result of profit maximising location decisions by individual firms, where the location decision is based on the future profits that a firm expects to earn in that location. In a given sector of the economy, a representative municipality j firm has expected profits, X X pj ¼ pjk ðTjkD Þqjk  cj ðwj ; gj ðTjkS Þ; qj Þ  fj ð1Þ raP kaMr

P is the set of regional indices and Mr is the set of municipality indices in region r. Expected profits of a representative municipality j firm depend on the sum of expected revenues earned from sales in all markets and local production costs. By selling the output produced in municipality j, qjk, in location k within region r a firm obtains average revenues pjk(TjkD), which depend on the cost to transport output between location j and k, TjkD. Hence, revenues earned from sales in different locations are not only determined by firms’ market size in these locations, qjk, but also by access to these markets, TjkD. It can further be assumed that firms do not incur transport costs when selling output in the same municipality and revenues in the local market only depend on prices and demand, so that pjj(TjjD)=pjj. On the cost side, variable production costs cj are a function of prices of primary factors, wj, average costs of intermediate inputs, gj(TjkS), and the output qj to be produced. gj(TjkS) will vary across locations due to differences in input transportation costs, TjkS, which depend on the availability of suitable suppliers nearby. Finally, fj are firm level fixed costs. To analyse plant location choice empirically, Eq. (1) leads to McFadden’s (1974) random profit maximisation-based conditional logit model. Unfortunately, with many location choices, computational problems arise very soon making it necessary to draw a small random selection from the location set. Although the estimates are consistent (McFadden, 1974), the approach is clearly suboptimal, since potentially important

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information is disregarded and there is no knowledge of the small sample properties of the estimates (see Guimara˜es et al., 2003 for a more detailed discussion). But more importantly, the ‘independence of irrelevant alternatives’ property of the conditional logit model requires that the decision not to locate a plant in a given area is independent of rejecting other areas, including nearby ones. With many spatial units at a very detailed geographical level, this is problematic since unmeasured attributes will be correlated for close neighbours. A closely related modelling problem also stems from the high number of alternative locations. Knowledge of the neighbourhood may be essential for the entrepreneur to choose a certain location. In fact, when looking at the establishment of plants in general, it is likely that smaller establishments restrict their search for locations to geographically limited areas. Because searching for alternative locations is costly, entrepreneurs setting up a small plant are more likely to choose to locate close to where they reside or worked previously (Figueiredo et al., 2002). Alternatively, the location decision can be approached from a location perspective by modelling the number of new manufacturing plants opening in municipalities. In the model of firm birth in Becker and Henderson (2000), there is a supply of entrepreneurs in each location and each period that is determined by the local profit opportunities as defined by Eq. (1). The higher the local profit opportunities the more entrepreneurs will set up a plant. A corresponding ‘demand’ curve reflects how local profit opportunities of new plants are determined by the number of other new additional firms that open in a municipality. Under competition effects individual plant profits are reduced with additional plant openings resulting in a downward sloping ‘demand’ curve, whereas agglomeration economies positively affect per plant profits causing the ‘demand’ curve to slope upwards. The count of new plants in a municipality is determined by the intersection of the ‘demand’ and supply curves in the local per plant expected profit and number of new plants space and leads to the following reduced-form equation: nijt ¼ fi ðxjt Þ þ eijt

ð2Þ

where nijt is the number of new establishments of sector i located in municipality j in period t. xjt is the vector of municipality characteristics that affect expected profits. eijt is a random error term. The count of new establishments in each municipality is a nonnegative integer value. A common way to model count data is as a Poisson distributed random variable. The Poisson distribution explicitly handles the integer property of the dependent variable and includes zero observations as natural outcome. A spatially disaggregated analysis implies frequent zero counts in any given year where municipalities do not receive new industrial establishments. With almost 8000 municipalities in the present study, the number of locations is very large, making this approach particularly suitable. Conceptually, in the firm perspective of the conditional logit framework entrepreneurs scan alternative sites to choose the profit maximising location, whereas in the locality perspective of Becker and Henderson’s (2000) model of firm birth local entrepreneurs decide whether or not to open a plant given the local profit opportunities. In the present study of all manufacturing plant openings among municipalities, there is no a priori reason to assume one of the two models as more appropriate. In some cases, entrepreneurs may

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consider all alternative locations or choose amongst a number of sites, while in other cases they might simple decide whether or not to start a business locally. Guimara˜es et al. (2003) show that the coefficients of the conditional logit model can be equivalently estimated using Poisson regression. Hence, Poisson estimations are consistent with both models. Under the Poisson assumptions, the probability that municipality j in period t receives a count of nijt new establishments is: n

probðnijt Þ ¼

ekijt kijtijt nijt !

k > 0; nijt ¼ 0; 1; 2; . . . ; n

ð3Þ

where nijt is as defined above, and kijt is the Poisson parameter of the mean number of establishments located in municipality j in period t. As a property of the Poisson distribution, kijt equals the conditional mean and variance: Eðnijt Þ ¼ Varðnijt Þ ¼ kijt

ð4Þ

The expectation of nijt, kijt, is log-linearly dependent on explanatory variables. Thus lnðkijt Þ ¼ bV i xjt

ð5Þ

where bi is a parameter vector to be estimated and xjt represents the vector of observable municipality characteristics. The main advantage of the Poisson regression is its ease of implementation, even with large choice sets, and that it naturally deals with the ‘zero’ problem. However, the following econometric issues have to be taken into account. If location decisions take place within a restricted set of locations, estimation must take into account the existence of unobservable factors (e.g. the characteristics of the stock of entrepreneurs) that render some alternatives irrelevant. In general, it may be sensible to assume the existence of factors influencing location decisions such as geographic or climatic attributes or local amenities that are either not observable, difficult to measure or for which no data exists. Where such factors are important, but impossible to be controlled for directly, cross-section analysis will give inconsistent estimates for the effects of the observable explanatory variables. With panel data, a common approach to control for unobserved attributes is the implementation of fixed effects models. For the Poisson regression, the fixed effects approach has been developed by Hausman et al. (1984) and implemented in the context of firm location modelling by Papke (1991) and more recently by Becker and Henderson (2000) and List and McHone (2000). The Poisson parameter of this model is kijt ¼ exjt bi þaj

ð6Þ

where aj are the municipality-specific fixed effects. Following Hausman et al. (1984) the fixed effects are conditioned out of the likelihood function by conditioning the plant count distribution of each municipality on the total manufacturing plant openings in that municipality over the sample period. The conditional likelihood function takes the form: 2 3nijt ! P  xjt bi X n ! e t ijt prob nij1 ; nij2 ; . . . ; nijT j jt 4 P xjs b 5 nijt ¼ ð7Þ j n ! e i t ijt t s

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The estimations account for the unobserved municipality-specific propensity to attract new manufacturing establishments by exploring the time variation ‘within’ each municipality1. Fixed effects techniques also control effectively for potential problems of simultaneity in the relation between the number of new manufacturing plants and transport infrastructure improvements. A positive association could be occurring because transport network design is not random, but is conditioned by the spatial pattern of economic activity, i.e. the transport network is designed to link important economic centres. Therefore, new road infrastructure attracts manufacturing plants but the concentration of many firms may also promote motorway construction nearby. Cross-section analysis cannot answer whether the attractiveness or unattractiveness of a municipality for new manufacturing plant location is due to access conditions to the road network or to other unobserved location-specific characteristics that, at the same time, influence road project placement2. Because fixed effects specifications relate changes over time in the dependent variable to changes over time in the road network, they provide consistent estimates if the simultaneous nature of firm location and motorway placement is due to particular historic location-specific characteristics. This is the case in the Spanish motorway building programme. In 1984, the government decided the motorways that were to be built over the next 10 years by opting for a doubling of lanes of the existing principal trunk roads connecting the major cities (M.O.P.T., 1993)3. Hence, the advantage a municipality had in the motorway placement (being close to a principal trunk road connection) existed before the study period and can be controlled for by fixed effects methods. Another source of potential endogeneity arises if the government’s road building programme responded to contemporaneous changes in economic conditions. Using the dependent variable at the finest level of geographical detail possible (the municipality) reduces the severity of this problem. Motorway project placements can be assumed exogenous to changes at the municipality level since such decisions are mainly determined by factors and forces above the local level (Rietveld and Bruinsma, 1998). This is particularly so in the case of Spain, where the prime objective of the motorway building programme was to improve the links between the major cities, but was not geared towards responding to changes in local demand. Nevertheless, to see whether the government’s decision of where to build new motorways was influenced by prior manufacturing plant location at the municipality level, the following logistic regression model is estimated: ! 1983 47 X X probðmwj19841994 ¼ 1Þ ¼ L dt njt þ dj pntrj þ dr zrj ð8Þ t¼1980

r¼1

1 Guimara˜es et al. (2003) show that using fixed effects with time dummies in the Poisson distribution is equivalent to including dummy variables for each choice in the conditional logit model to control for the potential violation of the assumptions of the ‘independence of irrelevant alternatives’, but it is much easier to implement in the case of large choice sets. With 7938 alternative locations choices in this analysis the conditional logit approach is computationally prohibitive and Poisson estimations provide a tractable solution to the firm location problem. 2 See Pitt et al. (1993) for a more complete discussion of the shortcomings of cross-section estimations in analysing impacts of public programmes. 3 Planning of the road building programme started in 1983. The plan was finished and made public in 1984. The same year as implementation of the plan started.

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The probability of a municipality j receiving a new motorway mwj1984 – 1994 over the road construction period of 1984 – 1994 is explained as a function of its count of prior manufacturing plant openings njt, the location of a principal national trunk road within its municipality radius pntrj, and province dummies zrj to account for influences above the local level4.

4. Data and location determinants 4.1. Data In order to test the relationship between new transport infrastructure and firm location, the model is applied to data on new manufacturing establishments in Spanish municipalities5. Municipality level data on new manufacturing plants are available from the Register of Industrial Establishments (Registro de Establecimientos Industriales) compiled by the Spanish Ministry of Industry6. The data consists of new establishments that were set up between 1980 and 1994 in ten subsectors, covering the entire manufacturing sector7. A total of about 122 000 new manufacturing establishments were registered over this period. Table 1 provides basic summary statistics. With the variance of the count variable, i.e. the number of new manufacturing establishments, being considerably larger than the mean in all sectors, there is evidence of overdispersion in the data8. As outlined in the previous section firms choose locations according to the municipality’s characteristics that affect expected profits and that vary from those characteristics in different locations. Municipality characteristics include both characteristics specific to the individual municipality and wider area characteristics of the region (province) in which the municipality is located. The latter refer to factors working over labour market areas rather than individual municipalities. The focus is on how road infrastructure and its improve4

The sample for the estimation of Eq. (8) includes all those municipalities that received a new motorway within their municipality radius from 1984 to 1994 and all those municipalities that had no motorway at the beginning of the 1980s and did not receive one over the study period. 5 Island municipalities (Canary and Balearic Islands) are not included as well as Ceuta and Melilla. These areas are not linked by the inter-regional road network. Thus road infrastructure presumably has a much smaller importance than other modes of transport. All the data have been adjusted to the 7938 mainland municipalities according to the 1996 Nomenclature of the National Statistical Institute. 6 All new physical plants belonging to the industrial sector are required to enter in the Industrial Establishments Register, whether these are re-locations or completely new start-ups. To distinguish these two types would be of great empirical interest but beyond the scope of this paper, since this information is not collected in the Register. Information on each establishment includes year of registration, industry code at 5 CNAE-93 digits (Clasificacio´n Nacional de Actividades Econo´micas, 1993), location (province and municipality), number of employees, initial capital investment and electricity power subscription. 7 The ten sectors are: ‘primary metal industry’, ‘non-metallic minerals and mineral products’, ‘chemical products’, ‘metal products, machinery and equipment’, ‘transport equipment’, ‘food and beverage’, ‘textiles, clothing and footwear’, ‘paper and printing’, ‘wood and furniture’, ‘plastics and other manufacturing products’. 8 Where the reason for overdispersion is clustering of firms due to unobserved municipality heterogeneity it is controlled for by the fixed effects specification (Cameron and Trivedi, 1998). For any remaining violation of the Poisson mean-variance equality assumption robust standard errors are estimated following Wooldridge (1991, 1999) and Papke (1991).

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Table 1 Summary statistics for the number of new manufacturing establishments by municipality: 1980 – 1994 Sector

Mean

Std. Dev.

Minimum

Primary metal industry Non-metallic min. and mineral products Chemical products Metal products, machinery, equipment Transport equipment Food and beverage Textile, clothing and footwear Paper and printing products Wood and furniture Plastic and various industries Total manufacturing

0.007 0.066 0.021 0.299 0.024 0.159 0.148 0.061 0.162 0.075 1.024

0.100 0.425 0.211 2.218 0.321 1.136 1.720 1.028 0.917 1.360 7.144

0 0 0 0 0 0 0 0 0 0 0

Maximum 7 27 15 152 44 107 204 169 44 392 672

Sum 818 7921 2514 35 668 2877 18 970 17 644 7217 19 395 8975 121 999

Data source: Registro de Establecimientos Industriales, Ministry of Industry, Spain.

ments affect the location of new manufacturing plants, after adjusting for factors that are generally found to influence location choice. The variables are summarised in Appendix A Table A.1 together with their definition, the expected signs, and the source of the data. Whenever possible, variables are calculated annually for the period 1980– 1994. 4.2. Road transport infrastructure In Eq. (1) local profit opportunities are determined by accessibility to output and supplier markets in different locations. By reducing transport costs, transport infrastructure improvements change accessibility to output markets and inputs suppliers. Accessibility measures, calculated using GIS, provide a way to quantify the effect of transport infrastructure improvements on firm location through the links to output and input markets. Recent new economic geography models show the relevance of market accessibility measures as proposed in Harris (1954). In Fujita et al. (1999) and Redding and Venables (2001) wage equations are a function of demand and supply accessibility, while in Head and Mayer (2002) market accessibility is shown to affect firms’ expected profitability and is thus a central determinant of firm location. A growing consensus in the transport and regional development literature is that more sectoral and spatial differentiation of accessibility measures is desirable, as a single indicator of accessibility is of limited use since different aspects of accessibility have not the same importance for all types of firms. 4.2.1. Demand accessibility When considering demand accessibility, the literature emphasises important differences for firm location between intra- and inter-regional market accessibility (see, for example, Vickerman, 1995, 1996; Vickerman et al., 1999). Martin and Rogers (1995) show in a new economic geography general equilibrium model how intra- and inter-regional transport infrastructure improvements can have different impacts on industrial location. Improvements in intra-regional infrastructure always increase the attractiveness of a region for firm location, while improvements in inter-regional infrastructure increase the sensitivity of firms to differences in the economic characteristics between locations and can have

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potentially ambiguous effects. As in Head and Mayer (2002), two measures of demand accessibility, intra- and inter-regional, are included to disentangle the effects of improvements in accessibility to markets in different locations9. 4.2.1.1. Inter-regional demand accessibility. Harris (1954) shows that market accessibility is determined by the distance to and the size of market demand in alternative locations. In his market accessibility function, the potential demand for goods produced in location j is the sum of the market size in all other locations Mk divided by their distance djk to j. X Mk MAj ¼ ð9Þ djk k In the absence of detailed local data on expenditure or consumption patterns to represent the size of market demand in different locations, population represents a reasonable proxy to calculate a general measure of inter-regional demand accessibility. X POPk GIMAj ¼ ð10Þ sjk kaL 438

POPk is the population of the municipalities in the destination set L438 defined as the 438 largest Spanish cities with more than 10 000 inhabitants. This covers over 75% of the total Spanish peninsular population. sjk is the distance between municipality j and k measured in travel time where sjk=1 for all municipalities that are less than half an hour travel time apart10. Inter-regional demand accessibility is, however, not the same for every sector. What matters for industry location is not only consumer demand as proxied by the general demand accessibility measure based on population, but also demand for intermediates. Sector-specific inter-regional demand accessibility is better captured by the following market potential measure: X Fik SIMAij ¼ ð11Þ sjk kaP iF

PiF is the destination set and includes those capital cities in provinces which receive the largest road flows of merchandise trade, Fik, in sector i, and account for 75% of the national total of these flows. A municipality j’s sector-specific inter-regional demand 9 In a context of a spatially highly disaggregated analysis it is important that road infrastructure measures are not limited to administrative boundaries. By calculating accessibility measures at the level of the municipality, a very detailed inter-and intra-regional accessibility surface can be derived. This gives a much more realistic characterisation of accessibility than if based on NUTS 2 or NUTS 3 regions, where intra-regional accessibility is implicitly assumed to be the same within the entire region. Alternatively, Vickerman et al. (1999) use raster-cells of 10 km. This also produces a very detailed accessibility surface independent of variations in the size of the spatial units. Because socio-economic data is only available for administrative units, I choose to keep the analysis at the municipality scale. 10 Travel time is calculated as the shortest path travel time along the Spanish road network and expressed in units of 30 min.

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accessibility SIMAij is calculated as the sum across municipalities k in the destination set PiF, discounted by the travel time sjk defined as above. Information on type, origin and destination of merchandise transported by road is based on the 1995, 1996, and 1997 permanent questionnaire survey of transport operators collected by the Ministry of Public Works (Encuesta Permanente de Transporte de Mercancı´as por Carretera, Ministerio de Fomento)11. As mentioned in Section 2, the effect of improvements in inter-regional demand accessibility on firm location is a priori not clear as two countervailing forces are at work. Expected revenues for firms locating in municipality j depend on access to markets in other regions (Eq. (1)). On the one hand, with improved market access, firms in municipality j have lower transport costs, which increase revenues for a given amount of output sold and may increase demand for firms in municipality j. On the other hand, when products sold by different firms are substitutes, reductions in transport costs can also increase competition from firms in other regions. This in turn can lead to a reduction in the quantity firms in municipality j can sell in all markets, and, hence, decrease expected local profit opportunities. 4.2.1.2. Intra-regional demand accessibility. Province capitals are administrative and economic centres and the principal demand location within provinces. Assuming the province capitals as the only relevant market place in region r, the inverse of intra-regional demand accessibility reduces to ACPj=sjk where sjk is the shortest travel time from each municipality j to the province capital k. Locating in a municipality at greater distance from province capitals implies greater transport costs to sell output in region r and, therefore, lower profit opportunities. Thus, ACPj should have a negative effect on firm location. The pre-existence of intra-regional markets implies that improvements of road infrastructure do not to lead to significant changes in competition within regions. It can, therefore, be expected that road improvements will unambiguously increase intra-regional profit opportunities. 4.2.2. Supplier accessibility In Eq. (1) profits also depend on proximity to suppliers because of the transport costs of shipping inputs. Municipality j’s supplier accessibility SAij is defined analogously to the sector-specific inter-regional demand accessibility. SAij ¼

X VAik sjk kaP

ð12Þ

iVA

SAij is the sum of supply capacities, proxied by VAik, the value added in sector i and location k, discounted by travel time, sjk. Here, PiVA is the destination set of provinces, which produce the largest quantities of sector value added and account together for 75% of the national total sectoral value added. An increase in supplier accessibility reduces the cost of intermediate goods and inputs, and therefore reduces the costs of production in municipality j and increases local profit opportunities. Thus improvements in supplier accessibility are expected to increase the attractiveness of a municipality for manufacturing plant location. 11

Lack of representativeness at the municipality levels requires aggregation to the province.

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4.2.3. Motorway access For a spatially detailed analysis of firm location, the accessibility measures presented so far are incomplete in the sense that they do not capture the fact that the motorway network itself can function as an extension of the market through high linkage and flow patterns (Reggiani, 1998). With the current changes in industrial organisation towards increasingly interconnected production, transport and communication processes, location near the motorway network can provide advantages in terms of access to both market and supplier flows that are in addition to those captured by the accessibility measures based on the principal locations of demand and supply. The simplest way to take this effect into account is by including a municipality’s motorway access, ACMj=djm, computed as the straight air-line distance djm from each municipality j to the nearest inter-regional motorway m. This captures the extent of the physical transport corridors. Differences in access to the motorway network are likely to affect the regional distribution of new manufacturing plants, since firms locating close to the transport corridors are also close to potential customer and supplier flows. This can result in negative spillovers where the redistribution comes at the expense of areas further away (Boarnet, 1998; Chandra and Thompson, 2000). Infrastructure is provided at a particular place and if it enhances firms’ profit opportunities, then it will also enhance the attractiveness of that location relative to other places. Thus a possible effect of the new transport infrastructure is to increase manufacturing plant openings in the transport corridors and cause a densification of firms in the vicinity of the projects, but reduce the numbers of new firms locating elsewhere. To see how new motorways affect the spatial distribution of new plant location, motorway access dummy variables indicate whether municipalities fall in the first 10 km from a motorway (this is the base interval or ‘treatment group’), or in distance intervals outside the first 10-km transport corridor (the ‘control group’)12. Comparing manufacturing plant openings in municipalities outside the 10-km transport corridors to those in municipalities within the 10-km transport corridors provides an indirect test for the hypothesised negative spillover effects13. With fixed effects estimations, the coefficients are based on ‘within’ municipality variations, hence, on the change in municipality location in relation to the inter-regional motorway network14. That is whether municipalities receive a new motorway within 10 km over the road building programme period. Negative coefficients for municipalities outside the 10-km transport corridors would indicate that manufacturing plant openings drop relative to municipalities within the first 10 km of the new transport corridors and, therefore, be consistent with the negative spillover hypothesis. 12

The average municipality diameter is about 10 km. The base interval or ‘treatment group’, therefore, captures those municipalities that are most likely to receive a new motorway within their municipality boundaries, whereas those in the ‘control group’ are likely to be adjacent municipalities and those beyond. 13 A direct spillover test would have to include neighbouring municipalities’ road access conditions as an independent variable in the estimations. This then could test whether municipalities are more likely to receive more/less manufacturing plant counts if their neighbours received a new motorway. This requires a full information matrix that identifies all municipalities neighbours or distances between each municipality pair of the total 7938 municipalities. Creating such a data set is beyond the scope of this paper. 14 See, Henderson (1996) for a more complete discussion of the interpretation of dummy variables in fixed effects estimations in the context of air quality regulation and industrial location.

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4.3. Other location determinants Other location determinants, which have been found significant in previous industrial location studies, are also included in the estimations. As reflected in Eq. (1) differences in municipality size impact on firms’ expected revenues earned in local markets. This is accounted for by including total municipality population. On the cost side, wages are calculated as annual averages of manufacturing wages at provincial level both by sectors and for total manufacturing. In order to take into account differences in labour force qualification, the percentage of the province labour force with higher education is included. Agglomeration economies are well-established location determinants. These are externalities derived from the spatial concentration of firms in the same sector (localisation economies) and proximity to a diversity of firms providing complementary services, intermediate inputs and information transfers between sectors (urbanisation economies). To capture localisation economies, an index of sector specialisation is used (see, Appendix A, Table A.1). The structure of the regional industrial environment is further captured by an index of area specialisation based on the degree to which areas are specialised in few sectors, i.e. lack of diversity (see, Appendix A, Table A.1). The province industry share in total national industrial employment reflects the size of the regional industrial base. This can influence the potential size of agglomeration forces, but it is also likely that competition is highest in areas of industrial concentration15. Finally, to account for time specific effects stemming from the business cycle, the annual national GDP growth is included as control variable16. For consistent estimates, the control variables capturing non-transport location determinants have to be exogenous too. Focusing on the birth of manufacturing plants in individual sectors reduces problems of endogeneity of the control variables. New plants can be assumed to make their location decisions taking the existing economic environment as exogenously given. Their generally small size makes them less likely to have a significant impact on the control variables, particularly since the control variables are measured at the more aggregate geographical units of provinces, with the only exception of population17. Another issue of endogeneity arises where unobserved municipality heterogeneity is correlated with the regressors. In particular, time invariant unobserved factors specific to municipalities may 15 Other variables that have been used in the firm location literature are local land costs and taxes. For Spain, such data does not exist for the entire panel period. However, since 1994, when data is available, the annual values present autocorrelation above 90%, showing a very high persistence in the house purchase price levels and tax rates among municipalities. As long as these variables have been time invariant over the sample period, they are controlled for by the fixed effects specification. However, road infrastructure improvements can become capitalised in land values and house prices (Voith, 1993). Even though there is no empirical evidence available in Spain, where this effect takes place it will be reflected in the population variables. Since higher land values can be expected to deter new manufacturing plants, if anything, the coefficient estimates of the road infrastructure are downward biased by the omission of a land cost variable. 16 Using time dummies to control for time-specific effects stemming from the business cycle did not lead to qualitatively different results. 17 The average size of the new plants is slightly less than six employees. About two thirds of the new plants are employing less than five employees and only less than 1% of the new establishments had 50 or more employees. This makes it reasonable to assume that the province variables and even population change at the municipality level are non-sensitive to short term increases in the number of new manufacturing plants.

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jointly influence control variables and manufacturing plant location. This is effectively addressed by the fixed effects estimations. A remaining estimation issue could arise if time variant unobserved factors affect both the number of new manufacturing plants and the observed covariates. In this case nonlinear-instrumental-variable techniques as proposed in Mullahy (1997), Windmeijer and Santos Silva (1997) and Windmeijer (2000) would offer a way to deal with endogeneity in count data models. The usually proposed time variant factors, such as, for example, changes in human capital or the provision of transport infrastructure, are included as regressors in this analysis. It seems, therefore, reasonable to assume that the problem of endogeneity caused by unobserved heterogeneity is effectively dealt with using fixed effects techniques.

5. Empirical results Detailed results of the conditional fixed effects Poisson model are reported in Appendix A Table A.2 for the pooled sample and the ten manufacturing sectors. In addition to standard errors from the expected Hessian in parentheses, robust standard errors are reported in brackets (Wooldridge, 1991, 1999; Papke, 1991). These are robust for distributions in the linear exponential family and in particular to the violation of the equality assumption of the mean and the variance in a Poisson. Table A.2 also includes the results of a Wald test on the appropriateness of the fixed effects model. The models are supported for the pooled estimation and the individual sectors. A formal Hausman test rejects a random effects specification for all estimations. Starting with the results for the pooled sample in column 1 of Table A.2, larger local markets, as proxied by municipality population, attract more new manufacturing establishments. Higher wages decrease the expected number of new manufacturing plant openings, but the coefficient is not statistically significant with robust standard errors. While labour force qualification, that is the number of persons with higher education, exerts a significant positive influence on the attractiveness of municipalities for new manufacturing plants. Increases in area specialisation do not show a statistically significant effect on the pooled manufacturing plant location. In contrast, a higher industry share significantly deters new manufacturing plants, indicating a move away from the traditional industrial areas where competition is likely to be highest. At the same time, the statistically significant negative sign of the inter-regional demand accessibility variable indicates also dispersion of new manufacturing plants away from the more densely populated high accessibility areas. The coefficient for intra-regional demand accessibility is negative as expected, as greater distance to province capitals reduces firm birth, but in the pooled estimation the coefficient is not statistically significant with robust standard errors. New manufacturing establishments responded, however, positively to the new inter-regional road infrastructure constructed. All motorway access dummy variables exhibit a negative and significant effect, attesting to the preference for locating in the first 10 km around a motorway. The coefficients indicate an average 14% increase in expected birth for municipalities receiving a motorway within a 10-km distance. Detailed results for the separate sector estimations are given in columns 2 –11 of Table A.2. The sector specific variables of sectoral specialisation and supplier accessibility are

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added and inter-regional demand accessibility is based on the sector-specific inter-regional demand accessibility measure (SIMAij)18. These results show that there is considerable inter-industry variation in the effects of the independent variables on plant location. A Chow test is conducted to determine whether the results of the empirical model significantly differ by manufacturing sector. This tests whether or not the parameter values associated with the data set of individual sectors are the same as those associated with the combined data set. The test finds a chi-squared statistic significant at the 1% level for all individual manufacturing sector estimations, supporting the hypothesis that firms in different manufacturing sectors are not attracted and deterred by the same set of local and regional characteristics. Table 2 focuses on the road transport infrastructure variables. The results show that different sectors respond to different types of accessibility. Increases in inter-regional demand accessibility reduce the probability of a new manufacturing plant birth in six sectors. A 1% improvement of accessibility with respect to the national average reduces plant birth by about 2 –6% depending on the sector. In none of the sectors do improvements in inter-regional demand accessibility show a significant positive effect19. The results confirm the ambiguous effects of improvements in inter-regional market accessibility. In contrast, intra-regional demand accessibility exerts a significant effect on five out of the ten manufacturing sectors. In these sectors, a reduction in the travel time to the nearest province capital by half an hour leads to a 3 –6% increase in expected births. Improving a municipality’s supplier accessibility, as measured by proximity to the main centres of production in the same sector as the new plant, shows a positive effect on the probability of manufacturing plant birth. The effect is statistically significant in six sectors where a 1 percentage point increase in supplier accessibility with respect to the national average leads to a 0.1 – 0.5% increase in expected births. Compared with inter-regional demand accessibility, supplier accessibility never shows a statistically significant negative sign. Together with positive effects of sector specialisation in six sectors and weak results for the importance of diversity as reflected by the mostly insignificant coefficients for the area specialisation variable (Table A.2), the results indicate a general positive influence of proximity to firms in the same sector. This is particularly evident for the ‘paper and printing’, and ‘wood and furniture’ sectors. In the ‘transport equipment’ sector firms have preferred locations close to existing centres of production in their own sector, but with generally strong and diversified industrial bases. New manufacturing plants in all sectors, except for the ‘chemical products’ sector, show a specific preference for locations close to motorways. The negative coefficients for the motorway access dummies are consistent with the negative spillover hypothesis. Manufacturing plant openings drop in municipalities outside the 10-km corridors relative to municipalities within the first 10 km from the motorways as a result of the motorway construction. Compared with the first 10 km, being outside the 10-km transport corridors reduces the expected number of new manufacturing plant openings by 12– 94%, depending 18 A more general version of the Harris (1954) accessibility measure is obtained by including a distance P decay parameter, b, so that MAj ¼ Mk =djkb . Note that Eqs. (11) and (12) are particular cases for which b = 1.

k

Estimations with accessibility measures based on industry-specific b coefficients, calculated from data on Spanish road transport operations, yield qualitatively the same results. 19 The same results are also obtained when inter-regional demand accessibility is based instead on the general inter-regional demand accessibility measure GIMAj.

356

Variables

Manufacturing pooled

Primary metal

Minerals

Chemical products

Inter-regional demand accessibility Intra-regional demand accessibility Supplier accessibility Motorway access Motorway access: 10 – 20 km Motorway access: 20 – 30 km Motorway access: 30 – 50 km Motorway access: >50 km

0.008*** 0.008

0.037 0.015 0.002

0.026* 0.037** 0.002*

0.0005 0.007 0.0003

0.135** 0.141* 0.147* 0.153*

0.300 0.941* 0.387 0.432***

0.072 0.243* 0.159*** 0.162***

0.201 0.239 0.064 0.088

Metal products, machinery

Transport equipments

Paper and printing

Wood and furniture

Results from columns 1 – 11 of Table A.2 0.018* 0.018 0.025* 0.020** 0.037* 0.015 0.030** 0.014 0.001* 0.005* 0.0003 0.008

0.040* 0.058* 0.002*

0.056* 0.031* 0.002*

0.119*** 0.153** 0.033 0.031

0.031 0.025 0.243*** 0.381*

0.141** 0.180** 0.098 0.120***

0.063 0.148 0.066 0.276***

Food and beverage

0.075 0.012 0.127*** 0.263*

Textile and clothing

0.241 0.190** 0.310* 0.218***

Notes: Significant coefficients are indicated by *, **, ***, for significance at the 1, 5 and 10% level, respectively, using robust standard errors.

Plastics and other manufacturing 0.015 0.049 0.001* 0.097 0.077 0.252*** 0.288**

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Table 2 Transport infrastructure effects

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Table 3 Logit estimates of the probability of receiving a new motorway Municipality located on a principal national trunk road Manufacturing plant openings in 1980 Manufacturing plant openings in 1981 Manufacturing plant openings in 1982 Manufacturing plant openings in 1983

5.681* (0.206) 0.043 (0.056) 0.003 (0.066) 0.058 (0.059) 0.006 (0.047)

Notes: Robust standard errors are reported in parenthesis. Significant coefficients are indicated by *, **, ***, for significance at the 1, 5 and 10% level, respectively. Estimation includes unreported province dummies and a constant.

on the distance to the new infrastructure and the sector20. These large coefficients support the hypothesis that locations outside the 10-km corridors have become less profitable and, hence, less attractive for firm location, with the development of the motorway network. Moreover, in the ‘primary metal’, ‘non-metallic minerals and mineral products’, ‘metal products, machinery and equipment’, and the ‘wood and furniture’ sector, the negative spillovers affect the municipalities closer to those within the 10-km motorway corridors more strongly than those at greater distance. The negative coefficients for the motorway access dummies for municipalities outside the 10-km motorway corridors also indicate that, as Vickerman (1995) argued, intra-regional distribution effects are becoming increasingly pronounced with the development of higher-order transport networks. Finally, Table 3 shows the results of the logistic regression of Eq. (8). This confirms that the spatial design of the motorway network in Spain has been strongly determined by the previous existence of a principal national trunk road, a factor that is effectively controlled for by the fixed effects estimation. However, the motorway construction was clearly not influenced by prior manufacturing plant location at the municipality level. With none of the annual manufacturing plant count coefficients being individually or jointly significant, receiving a new motorway between 1984 and 1994 can be regarded as an exogenous event for municipalities.

6. Conclusion This research has focused on the impacts of road infrastructure on the location of new manufacturing establishments. Unlike most previous studies, micro-level data has been used 20 Closer inspection of Table 2 reveals that the effect of transport infrastructure improvements on firm location is not linear. A linear specification would underestimate the stronger effects nearer to the infrastructure. Using distance linearly yields estimates that indicate that moving 10 km away from the inter-regional motorways reduces the predicted number of new plants by 0.9 – 3.8%. Experimentation with a quadratic approximation shows that effects near motorways are larger (3.9 – 9.9%) and fade away slowly (0.1 – 0.5% every 10 km). These polynomial approximations, however, cannot capture the existence of complicated nonlinearities and only specifications with discrete distance effects are presented here.

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to explore variations in plant location at a detailed geographical scale. Over the period from 1980 to 1994, most of the inter-regional road network in Spain has been developed. One important impact of the new transport infrastructure constructed is that it has changed the relative attractiveness of municipalities for new manufacturing establishments. From the empirical results the following should be emphasised. First, road transport infrastructure matters. In the context of a country such as Spain, where the motorway network has only been developed recently and where considerable inter- and intra-regional differences exist, access to road transport infrastructure plays an important role in manufacturing plant location. Second, motorways affect the spatial distribution of new manufacturing establishments by increasing the attractiveness of municipalities close to the new infrastructure. Depending on the distances to the new infrastructure, municipalities are affected to a different degree with indirect evidence that is consistent with negative spillover effects. Third, empirical support is found for the hypothesis that road infrastructure has a differential impact across manufacturing sectors. The importance of different types of accessibility varies among sectors, but the positive effects of closeness to firms in the same sector suggests that geographical specialisation has also been an important force. Consistent with predictions of new economic geography models for transport costs falling below some critical level, the new road infrastructure constructed appears to have facilitated sectoral concentration that was accompanied by geographical dispersion. It has allowed firms to locate at greater distance from the main population and industrial centres, however, along the motorway corridors where they can still benefit from good accessibility, and close to firms in the same sector to take advantage of localisation economies. The results raise some policy implications. There is no unique set of site characteristics to form a favourable environment for all types of new manufacturing plants. However, access to road infrastructure together with a better understanding of input – output linkages should definitely be key components in any integrated programme of spatial development. Moreover, emphasis has to be placed on the redistributional implications of large transport infrastructure projects. In particular their tendency to attract development to the created corridors has to be taken into account. It is here, where synergies between the transport sector and local economic development are likely to show the best potential to be explored. This paper has presented a new empirical approach to the analysis of the impact of transport infrastructure investment by combining micro level data with GIS. This illustrates that with the now more readily available micro level data sets, together with the increasing possibilities that GIS opens up, new interesting insights into the role of transport infrastructure and the location of economic activity can be gained. Acknowledgements This research has been carried out with financial support from the ESRC, grant R00429834466. The support is gratefully acknowledged. I wish to thank Peter Bibby for his helpful support with GIS programming and Harvey Armstrong, Max Craglia, Paulo Guimara˜es, John Henneberry, Ricardo Mora, and Roger Vickerman for their thoughtful and constructive suggestions. I am grateful to Leslie Papke for providing her Gauss estimation procedure. Two anonymous referees provided valuable comments on an earlier version of the paper.

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359

Appendix A Table A.1. Independent variables: definition, expected effects and data sources Variables

Definition

Geographical scalea

Expected effect

Data source

Population

Absolute size of population (in hundred thousands) Average annual manufacturing wage (base=national average) Percent of labour force with higher education Employment based SPECij indexb Employment based SPECj indexc Share in total national industry employment GIMAj index of potential population accessibilitye SIMAij index of potential accessibility to main transport flow destinationse Travel time to the nearest province capitale SAij index of potential accessibility to main locations of productione Municipalities within 10 km of nearest motorway=1f Municipalities between 10 and 20 km of nearest motorway=1f Municipalities between 20 and 30 of nearest motorway=1f Municipalities between 30 and 50 of nearest motorway=1f Municipalities beyond 50 km of nearest motorway=1f Percent growth in annual national GDP

NUTS V

+

INE

NUTS III



BBVd

NUTS III

+

EPA/INE

NUTS III NUTS III NUTS III

+  +/

BBVd BBVd BBVd

NUTS V

+/

NUTS V

+/

GIS own calculations GIS own calculations

NUTS V



NUTS V

+

NUTS V

+

GIS own calculations

NUTS V

/+

GIS own calculations

NUTS V

/+

GIS own calculations

NUTS V



GIS own calculations

NUTS V



GIS own calculations

NUTS 0

+

INE

Wages Labour force qualification Sector specialisation Area specialisation Industry share Inter-regional demand accessibility (general) Inter-regional demand accessibility (sector-specific) Intra-regional demand accessibility Supplier accessibility

Motorway access: 0 – 10 km

Motorway access: 10 – 20 km

Motorway access: 20 – 30 km

Motorway access: 30 – 50 km

Motorway access: >50 km

National GDP growth a

GIS own calculations GIS own calculations

NUTS III regions are provinces in Spain; NUTS V corresponds to municipalities.

b

Sector specialisation is defined as: SPECij = (eir/er)/(ei/e) where er and e are the total employment and eir and ei are the employment in sector i in region r and Spain, respectively. Alternatively, I have tested density measures for sector employment and the share of employment in the same sector. Both measures yield similar results. P c The area specialisation index is: SPECj ¼ 1=2 jðeir =er Þ  ðei =eÞj, where eir, er, ei, and e are defined as above. The fewer sectors are present in the area, the closer the value to 1, whereasi a value of 0 is obtained when the sectoral structure of a province is identical to that of the nation as a whole. d

BBV (Banco Bilbao-Vizcaya Foundation) data is only published biannually for odd years. The data for the missing even years were generated by linear interpolation and extrapolation. e

The location of each municipality with respect to the Spanish road network is calculated in GIS for four points in time: 1980, 1985, 1990, and 1995. Annual accessibility data were estimated by interpolation using annual information on the evolution of the total length of the motorway network in kilometres taken from the Statistical Yearbook of the Ministry of Public Works. Potential accessibility measures are standardised by the national average to facilitate quantitative interpretation. f

For distance to the nearest motorway it is assumed that the relevant information on which firms base their location decision is not only the motorway construction in a given year but the plans approved by the government. The data is calculated for three different periods: 1980 – 1985, 1995 – 1990, 1990 – 1995.

360

Variables

Manufacturing pooled

Primary metal

Minerals

Chemical products

Metal products, machinery

Transport equipments

Food and beverage

Textile and clothing

Paper and printing

Wood and furniture

Plastics and other manufacturing

Population

0.240* (0.017) [0.039] 0.008 (0.003) [0.009] 0.034* (0.002) [0.007]

0.457*** (0.314) [0.266] 0.012 (0.017) [0.024] 0.020 (0.030) [0.032] 0.268*** (0.133) [0.147] 3.630 (3.860) [4.269] 0.177 (0.237) [0.219] 0.037 (0.024) [0.025]

0.370* (0.099) [0.062] 0.017 (0.007) [0.012] 0.051* (0.009) [0.012] 0.071 (0.107) [0.245] 0.326 (1.225) [1.545] 0.432* (0.091) [0.139] 0.026* (0.006) [0.009]

0.174 (0.141) [0.171] 0.019 (0.025) [0.027] 0.007 (0.017) [0.018] 0.684** (0.280) [0.346] 0.175 (2.348) [2.757] 0.336*** (0.147) [0.210] 0.0005 (0.012) [0.013]

0.397* (0.033) [0.070] 0.011 (0.006) [0.012] 0.013*** (0.005) [0.008] 0.594** (0.135) [0.302] 0.306 (0.592) [1.259] 0.397* (0.040) [0.107] 0.018* (0.003) [0.007]

0.149 (0.130) [0.121] 0.019 (0.013) [0.019] 0.011 (0.018) [0.020] 0.160 (0.184) [0.223] 6.361** (2.347) [3.241] 0.301 (0.152) [0.276] 0.018 (0.012) [0.013]

0.140** (0.045) [0.074] 0.055** (0.014) [0.023] 0.024** (0.007) [0.012] 0.498** (0.125) [0.211] 2.586** (0.804) [1.251] 1.344* (0.061) [0.169] 0.025* (0.005) [0.009]

0.211** (0.049) [0.091] 0.003 (0.005) [0.008] 0.017 (0.007) [0.013] 0.637** (0.125) [0.298] 0.488 (0.837) [1.098] 0.365*** (0.060) [0.192] 0.020** (0.003) [0.009]

0.344* (0.053) [0.056] 0.039*** (0.016) [0.022] 0.060* (0.010) [0.020] 0.827* (0.228) [0.341] 0.475 (1.413) [2.398] 0.256 (0.088) [0.210] 0.040* (0.008) [0.010]

0.288* (0.060) [0.075] 0.003 (0.007) [0.009] 0.016** (0.006) [0.008] 0.604** (0.151) [0.213] 0.587 (0.769) [0.937] 0.374* (0.054) [0.090] 0.056* (0.005) [0.009]

0.257** (0.060) [0.110] 0.012 (0.008) [0.024] 0.0008 (0.009) [0.016] 1.049 (0.169) [0.996] 4.589 (1.223) [3.846] 0.286 (0.077) [0.282] 0.015 (0.006) [0.042]

Wages

Labour force qualification Sector specialisation Area specialisation Industry share

Inter-regional demand accessibility

0.046 (0.302) [0.891] 0.553* (0.020) [0.090] 0.008*** (0.001) [0.004]

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Table A.2. Fixed effects estimation of the Poisson model: pooled total and by manufacturing sector

0.008 (0.003) [0.009]

Motorway access: 10 – 20 km

0.135** (0.021) [0.062] 0.141* (0.023) [0.045] 0.147* (0.021) [0.043] 0.153* (0.018) [0.047] 0.085* (0.002) [0.005] 68 510.28 4725.61* 79 560

Motorway access: 20 – 30 km Motorway access: 30 – 50 km Motorway access: >50 km National GDP growth Log likelihood Wald test Observations

0.015 (0.054) [0.049] 0.002 (0.002) [0.002] 0.300 (0.295) [0.378] 0.941* (0.352) [0.374] 0.387 (0.284) [0.269] 0.432*** (0.255) [0.251] 0.046*** (0.024) [0.025] 1711.57 24.46** 5730

0.037** (0.010) [0.013] 0.002* (0.0004) [0.0008] 0.072 (0.077) [0.096] 0.243* (0.085) [0.094] 0.159*** (0.080) [0.083] 0.162*** (0.067) [0.085] 0.053* (0.007) [0.008] 12 776.38 365.41* 32 460

0.007 (0.027) [0.026] 0.0003 (0.0005) [0.0006] 0.201 (0.155) [0.157] 0.239 (0.186)

[0.193] 0.064 (0.156) [0.189] 0.088 (0.128) [0.128] 0.093* (0.012) [0.013] 4356.86 94.81* 12 570

0.037* (0.006) [0.009] 0.001** (0.0002) [0.0005] 0.119*** (0.044) [0.065] 0.153** (0.049) [0.068] 0.033 (0.042) [0.081] 0.031 (0.034) [0.066] 0.072*

(0.003) [0.005] 30 928.99 1181.68* 53 340

0.015 (0.022) [0.024] 0.005* (0.0008) [0.0009] 0.063 (0.170) [0.234] 0.148 (0.203) [0.233] 0.066 (0.147) [0.212] 0.276*** (0.122) [0.172] 0.294* (0.011) [0.020] 4503.71 904.47* 12 075

0.030* (0.007) [0.011] 0.0003 (0.0004) [0.0008] 0.075 (0.054) [0.068] 0.012 (0.056) [0.079] 0.127** (0.047) [0.063] 0.263* (0.042) [0.080] 0.054* (0.004) [0.006] 23 857.93 820.09* 51 750

0.014 (0.009) [0.020] 0.0008 (0.0003) [0.0006] 0.241 (0.047) [0.185] 0.190** (0.047) [0.098] 0.310* (0.052) [0.108] 0.218** (0.051) [0.103] 0.138* (0.005) [0.008] 16 155.14 1674.53* 32 265

0.058* (0.016) [0.020] 0.002* (0.0004) [0.0008] 0.031 (0.118) [0.136] 0.025 (0.130) [0.126] 0.243*** (0.113) [0.131] 0.381* (0.088) [0.088] 0.099* (0.007) [0.012] 7305.40 583.39* 16 290

0.031* (0.008) [0.010] 0.002* (0.0003) [0.0004] 0.141** (0.052) [0.059] 0.180* (0.057) [0.073] 0.098 (0.050) [0.067] 0.120*** (0.046) [0.066] 0.053* (0.005) [0.006] 22 970.96 575.35* 44 955

Notes: Significant coefficients are indicated by *, **, ***, for significance at the 1, 5 and 10% level, respectively, using robust standard errors.

0.049 (0.014) [0.082] 0.001** (0.0004) [0.0006] 0.097 (0.084) [0.109] 0.077 (0.097) [0.154] 0.252** (0.092) [0.128] 0.288* (0.064) [0.118] 0.048 (0.007) [0.014] 10 731.21 334.11* 22 050

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Intra-regional demand accessibility Supplier accessibility

361

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