thescopeofagglomerationeconomicsevidencefromcatalonia-外文文献(编辑修改稿)

thescopeofagglomerationeconomicsevidencefromcatalonia-外文文献(编辑修改稿)内容摘要：

nepossibility. Probably because of the conceptual difficulty, most studies treat industrial distancein a binary fashion, namely, firms that belong to the same industry and those that do not. Thisleads to the distinction between localization and urbanization economies first proposed byHoover (1936). Localization economies are externalities arising between firms with the sameindustrial activity, whereas urbanization economies are external effects taking place betweenfirms operating in different industries.8Romer (1986) places knowledge spillovers and learning by doing at the core of economicgrowth. Glaeser et al. (1992) test some growth implications at the local level, stressing the roleof knowledge spillovers as a mechanism explaining why cities form and grow. Like the distinction between localization and urbanization economies found in the more static Marshallianapproach, the distinction between intrasectoral and intersectoral effects has also been an issuein this dynamic externalities literature. MarshallArrowRomer (MAR) externalities refer toknowledge spillovers between firms inside an industry. MAR economies imply that sectorswhich are overrepresented in a city should experience higher than average growth rates sincetechnology levels rise as industry size grows. The opposing view, namely that it is not specialization but industry diversity that promotes innovation and growth, is usually identifiedwith Jane Jacobs’ hypothesis. Jacobs (1969) presents case evidence for the claim that it isthe interaction between not closely related industries that fosters growth through the crossfertilization of ideas.Empirical work has not produced conclusive results regarding the relative importance ofintersectoral or intrasectoral external effects. Applied work on the industrial scope of agglomeration economies has shown that the effects of localization/MAR and urbanization/diversityeconomies differ widely between industrial sectors. Though the evidence is not overwhelming,localization/MAR economies appear to have stronger effects for low and middle levels ofsectoral technology intensity, whereas urbanization and diversity economies have a strongerinfluence in hightech industries. Using US data, Henderson et al. (1995) first stressed thisresult, and similar evidence has been found by Combes (2020) and ViladecansMarsal (2020)for France and Spain respectively.The last goal of this paper is to add further empirical evidence to the debate on the ways inwhich industrial structure affects local industry performance. This issue has been raised at theempirical level, in the literature on city growth. Glaeser et al. (1992) identified two theories inthe literature, with opposite implications. The MAR theory predicts that local monopoly, asopposed to local petition, spurs growth, since it restricts the flow of ideas。 hence, theexternalities stemming from innovation are to a larger extent internalized. In contrast, Jacobs(1969) and Porter (1990) claim that local petition fosters the rapid adoption of technologiesand, therefore, favours growth. Glaeser et al. (1992) finds evidence to support Porter’s andJacobs’ theories. A related question is whether entering firms show a preference for small orlarge firms. Saxenian (1994) presents evidence that small firms are more open and innovative.7Also for Spain, Ala241。 243。 nPardo et al. (2020) pute spatial statistics on establishments’ births. They conclude thatexternalities seem to be strongest between 15 and 20 km. However, they note that in their analysis, establishments’ birthsare not conditioned on observable characteristics of municipalities.8The cost of making this binary distinction between localization and urbanization economies is that it sheds littlelight on how much agglomeration economies attenuate as activities bee increasingly dissimilar.578 J. JofreMonsenyPapers in Regional Science, Volume 88 Number 3 August 2020.If this is the case, the interaction between firms found in industrial agglomerations prisinga large number of small firms should be higher, implying larger external effects. Hence, itfollows that entering establishments should show a preference for this particular sort of location.3 Empirical application The dataThe empirical analysis is carried out at the municipal level for Catalonia. Catalonia is a regionfound in the northeast of Spain. In 1999, it had a population of million. Its 32,000 km2surface is partitioned into 946 municipalities.9The dataset used here was obtained from twodifferent sources: the Spanish National Social Security Registry10and the Registry of IndustrialEstablishments. The first contains data on municipal employment levels at the two digit sectoralclassification, and the second, which records all new and relocating manufacturing establishments in Spain, contains establishment level information including the two digit sectoralclassification and the municipality of each establishment. The analysis is restricted to establishments locating, either new or relocating, in the period spanning 1996–2020.11For each industryanalysed, some key numbers of locating establishments are provided in Table 1.As can be seen in Table 1, a striking feature of the data on the location of new establishmentsis that for all sectors, a high number of municipalities record no new establishment settling down(Table 1, column 4). In fact, in 299 out of 945 municipalities no manufacturing establishmentswere started up between 1996 and 2020. Comparison of the number of new establishments andthe number of municipalities experiencing births for each industry (first and third columns inTable 1) suggests that startups of establishments show substantial spatial concentration.Analysing the different roles that intrasectoral and intersectoral external effects may playacross industries with different。