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One aspect of agglomeration economies is economies of scale. When automobile production centered in Detroit in the early part of the twentieth century, this allowed more efficient production methods, which lowered the per-unit cost of output. Arrow (2000) emphasizes the tension between increasing returns to scale and equilibrium models, and as Young (1928) noted, increasing returns to scale is at the foundation of economic progress. Kaldor (1972), building on Young's insights, noted that static neoclassical economic models did not do a good job of depicting the economic progress that results from increasing returns to scale in production. This insight goes at least as far back as Adam Smith (1776), however, who noted the increased productivity that comes with an increased division of labor. Smith's example of the pin factory, where individuals specializing in one small part of a larger manufacturing operation increase productivity by, perhaps, hundreds of times, shows the benefits of agglomeration economies. The division of labor is limited by the extent of the market, Smith argued, so enlarging the extent of the market allows for a greater division of labor, which increases productivity and generates prosperity. By concentrating automobile production in Detroit rather than having automobiles locally built, the extent of the market is increased from one locality to an entire nation, and in some cases an entire world. The resulting agglomeration economies increase productivity and produce prosperity.

The interaction between the culture and tourism industries is naturally concentrated in cities. However, the effect of their co-agglomeration on urban tourism development depends on their level of integration. This study aims to answer the following questions: Can culture–tourism co-agglomeration promote the development of the tourism economy? Is the effect of culture–tourism co-agglomeration on tourism development moderated by culture–tourism integration? Does culture–tourism co-agglomeration have spatial spillover effects?

Taking 262 prefecture-level cities in China from 2009 to 2019 as the research sample, this study measures the degree of culture–tourism co-agglomeration using a co-agglomeration index and measured culture–tourism integration using a coupling coordination degree model. Using a threshold model and a spatial econometric model, this study examined the effect of culture–tourism co-agglomeration on urban tourism development.

Culture–tourism co-agglomeration had a positive effect on the urban tourism economy, and the effect differed according to geographical location and city grade. Moreover, culture–tourism co-agglomeration’s effect on the urban tourism economy was affected by the level of culture–tourism integration. When the level of culture–tourism integration crossed the threshold, the positive effect of culture–tourism co-agglomeration on the urban tourism economy will be enhanced. Finally, culture–tourism co-agglomeration had positive spatial spillover effects on surrounding cities.

This study integrated culture–tourism co-agglomeration, culture–tourism integration and urban tourism economy into the same research framework and innovatively analyzed the effect of the scale and quality of culture–tourism interaction on the urban tourism economy.

文化产业和旅游产业之间的互动性使其天然地在城市中集聚发展。然而, 文化和旅游协同集聚对城市旅游发展的影响取决于它们的融合发展水平。本研究旨在回答以下问题：文化和旅游协同集聚能否促进旅游经济的发展？文化和旅游协同集聚对城市旅游发展的作用是否受到文化和旅游融合发展水平的调节影响？文化和旅游协同集聚对城市旅游发展的影响是否具有空间溢出效应？

本文以2009-2019年中国262个地级及以上城市为研究样本, 采用协同集聚指数测度城市文化和旅游集聚水平, 采用耦合协调度模型测度城市文化和旅游融合发展水平, 并通过构建面板门槛模型和空间计量模型, 检验文化和旅游协同集聚对城市旅游发展的影响。

文化和旅游协同集聚对城市旅游发展具有正向的促进作用, 而且这种影响会因为地理位置和城市等级的不同而存在差异。此外, 文化和旅游协同集聚对城市旅游发展的促进作用还受到文旅融合发展水平的影响, 当文旅融合发展水平跨越发展门槛后, 文化和旅游协同集聚对城市旅游发展的正向影响得到增强。最后, 文化和旅游协同集聚对周边城市具有积极正向的空间溢出效应。

本文将文化和旅游协同集聚、文化和旅游融合发展、城市旅游发展纳入统一框架, 创新性地分析了文化和旅游互动发展的规模和质量对城市旅游发展的影响。

The influence of agglomeration externalities on a firm’s profits depends on the efficiency benefits it derives from collocating and on the contributions it makes to its competitors production processes. We examine the economics of firm location choice with a duopoly model that allows for asymmetric contributions by firms to production externalities. In the model, the magnitude of a firm’s contributions to industry agglomeration externalities is determined by its R&D levels and by how closely it locates to the other firm. Through numerical simulations, the effects of firm heterogeneity on industry geographical organization are evaluated. Specifically, the relationships between industry agglomeration strength, R&D investment, location choice timing and Nash equilibria industry structures are explored. The findings from this exercise indicate that geographic collocation tends to occur in industries with symmetry in firm R&D spending and where high R&D firms are the initial entrants. Moreover, we find that under some industry conditions, strengthened agglomeration economies encourage firms to locate more distant from each other rather than collocate. Finally, our simulations also show that firms’ profits tend to increase yet total welfare decreases as agglomeration economies strengthen.

The purpose of this paper is to examine effects of regional economic integration on the concentration of manufacturing firms in provinces of Thailand on the border with Cambodia. It aims to clarify the interactions between dispersion and agglomeration forces within a firm’s location choice in the presence of economic integration and thereby to explain the feasibility of the border SEZs.

The theory of industrial clustering and New Economic Geography provides a theoretical framework to understand the locations of economic activities when regional economies are integrated. This paper employs provincial level data to calculate industry location quotients across a 10-year period from 2007 to 2017 in central Thailand and uses firm-level data from industrial censuses in 2006 and 2011 to estimate logit models for two border provinces with Cambodia and three eastern seaboard provinces. Two base models and extended models are tested to explain the persistent agglomeration of Thai firms in each manufacturing industry.

The authors found a positive correlation between the agglomeration level in 2006 and the choice of firms toward the border provinces in 2011. The disaggregated analysis shows that depending on the initial level of concentration in each industry, there can be agglomeration or dispersion effects. The advantage of low trade costs and labor costs of unskilled migrant workers are not significant factors attracting firms to the border. Firms in industries with increasing returns are more likely to stay in the hub.

The disaggregated analysis by industry provides very important implications for SEZ policy interventions. The important role of agglomeration economies limits the extent to which such policies can be successful. It would be an enormous challenge for policy makers to initiate forces which are strong enough to induce firms to relocate away from areas with high agglomerations. Policy interventions with attractive incentives should be very selective to industries already have a certain degree of concentration in the provinces so as to reinforce the agglomeration effects.

The research extends the empirical literature on SEZs by offering a unique case study of an emerging economy with a strong market foundation rather than a transitional or developed economy. It is also different from other research on SEZs when taking into account the effects of regional integration on border SEZ formation and firms’ location choices. In addition, this study employs firm-level data rather than provincial data to bring empirical insights and fill in the knowledge gap on agglomeration economies in Thailand with the presence of regional economic integration.

The purpose of this paper is to test geographic and economic distance of industrial agglomeration.

Based on a recent census database in China, we estimate the production function of Chinese firms, focusing on the impact of agglomeration economies.

The estimation results provide strong evidence that agglomeration effects decline with increasing geographic and economic distance.

Previous studies examine agglomeration effects at certain geographic and industrial level, but largely ignore that agglomeration benefit may be different at different levels of geography and industry. This paper contributes to the literature by examining the geographic and economic distance of agglomeration economies, and shows a clear pattern on geographic and industrial scope of agglomeration economies.

This paper analyses the impact of institutional proximity, cognitive proximity and geographical proximity (in the form of agglomeration economies) on the firm-level productivity of foreign-owned firms in Ireland. The analysis of agglomeration economies, consisting of internal economies of scale, localization economies, related variety and urbanization economies, has a strong pedigree in regional economics literature. Increasingly, however, alternative explanations of firm-level productivity performance have been explored with institutional and cognitive proximity often identified as other important determinants of performance. This paper presents an analysis of the importance of agglomeration economies (based on geographical proximity) versus institutional and cognitive proximity (which may be a-spatial).

A series of measures capturing regional level agglomeration economies are generated as well as measures of institutional and cognitive proximity. The impact of these effects on foreign-owned firm-level productivity is analysed using data from the Irish Census of Industrial Local Units 2009. The estimation method employed is general method of moments (GMM) which allows for the potential endogeneity of variables within the system of analysis.

The results reveal that institutional proximity has a positive impact on productivity. A possible reason for this result is that local units of the same nationality are sharing knowledge in relation to successfully conducting business in Ireland. However, cognitive proximity is found to be statistically insignificant. Agglomeration economies are also important with urbanization economies and the availability of skilled labour having a positive effect on productivity.

The key contributions of this paper are as follows; firstly, the paper provides the first test of the institutional and cognitive proximity hypotheses on productivity while also controlling for a series of internal and external agglomeration economies. Secondly, the analysis considers, firm level, regional level and national level indicators as determinants of firm's productivity. In combining micro and macro level indicators, the paper attempts to answer the call of Van Oort et al. (2012) for such analyses. Thirdly, the paper provides the first detailed examination of the role of ‘proximity’ on foreign-owned manufacturing firms in the Irish context.

The implementation of the innovation-driven development strategy is of practical significance for improving the quality and efficiency of economic growth and accelerating the transformation of economic development mode. The purpose of this paper is to study the impact of innovation-driven strategies on marine industry agglomeration and industrial transformation.

In traditional grey correlation analysis, when the positive and negative areas cancel each other out during the integration process, the calculation result of the correlation degree is often inconsistent with the qualitative analysis. For this reason, from the perspective of curve similarity, this paper constructs two response curves through the relative change area of the two curves and the relative area change ratio of similar degree, thus constructing an improved grey relational model.

The authors find that the innovation investment has a better correlation with marine industrial agglomeration. It also found that Guangdong Province has the highest degree of correlation between innovation indicators and marine industrial agglomeration. Much beyond the authors’ expectation, in the areas where marine industrial agglomeration is high, the synergistic effect is not obvious by using the location entropy method.

The improved grey correlation analysis method can effectively overcome the phenomenon that the positive and negative areas cancel each other in the integration process of the original algorithm, and it can also effectively measure the negative correlation between variables. This paper explores the impact of innovation drive on the agglomeration of marine industries, which is of great significance to the sustainable development of marine economy.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

This conceptual paper aims to develop the vision of a novel policy tool called the digital industrial cluster (DIC) thought for a future context shaped by digital economies and Industry 4.0. The DIC departs from the industrial cluster’s precepts and take them into the digital agglomeration phenomenon.

This research uses a discursive and theoretical approach. To create a clear picture related to the research topic, this paper reviews the literature on how industrial clusters deal with digital transformation. The review takes a particular interest in industrial clusters, information and communication technologies, Industry 4.0 and policy tools for digital agglomeration.

The research develops the vision of the DIC as a new policy tool, building its foundations on both industrial clusters and digital economies; furthermore, the DIC was compared with other policy tools based on digital agglomeration to understand the similarities, differences and advantages of the former. Finally, this paper discussed where the DIC could find its way toward implementation and what externalities could be expected from doing so.

This research could be useful for policymakers and cluster organizations looking to deploy policy tools that take advantage of industrial clusters and digital transformation. Besides, the theoretical foundations presented could lead researchers to empirical identification of early incarnations of the DIC.

This paper develops theoretical principles for a new policy tool that connects industrial clusters, digital agglomeration and Industry 4.0 for the first time.