The agglomeration of American R&amp;D labs

Buzard, Kristy; Carlino, Gerald A.; Hunt, Robert M.; Carr, Jake; Smith, Tony

doi:10.1016/j.jue.2017.05.007

Cited by 61 publications

(56 citation statements)

References 15 publications

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“…In this paper's case, we only know that the place of work is somewhere within a PUMA, so we use the Euclidian distances between PUMA centroids as the measure of the distance between workers. This data limitation prevents us from detecting agglomeration at very short distances, a phenomenon for which there is growing evidence (Buzard, Carlino, Hunt, Carr, & Smith, ). Nevertheless, as we demonstrate below, we are still able to detect differences in agglomeration between occupation groups at less granular levels.…”

Section: Methodsmentioning

confidence: 97%

Within‐industry agglomeration of occupations: Evidence from census microdata

Goldman

Klier

Walstrum

2019

Journal of Regional Science

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This study uses worker‐level data on industry, occupation, and place of work to explore differences in the spatial properties of production, administrative, and R&D occupation groups within industries. To measure differences, we calculate location quotients at the local labor market level and the Duranton and Overman (2005) agglomeration index for each group. We find appreciable differences in the spatial distribution of occupation groups within most manufacturing industries, with R&D occupations consistently exhibiting the highest degree of spatial concentration. Our results are consistent with the core theoretical and empirical results in the agglomeration literature.

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Section: Methodsmentioning

confidence: 97%

Within‐industry agglomeration of occupations: Evidence from census microdata

Goldman

Klier

Walstrum

2019

Journal of Regional Science

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“…If Sustainability 2020, 12, 1551 5 of 16 there is no common boundary, it means that they are "non-adjacent." Figure 1 shows an example of the vehicle-type spatial weight matrix [31]. When calculating Moran's I index, the spatial weight matrix is needed, and the vehicle-type spatial weight matrix is generally selected.…”

Section: Global Spatial Correlationmentioning

confidence: 99%

“…If there is no common boundary, it means that they are "non-adjacent." Figure 1 shows an example of the vehicle-type spatial weight matrix [31]. In the matrix shown in Figure 1, 1 and 0 mean that space unit i and space unit j are adjacent and non-adjacent, respectively.…”

Section: Global Spatial Correlationmentioning

confidence: 99%

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

Chen

Shi

et al. 2020

Sustainability

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With ever-increasing economic globalization and rapid advancement of science and technology, developing high-tech industries have become an important way for many countries to achieve sustainable and environmentally friendly economic development. In this article, we aim to empirically test the critical factors, which can influence the spatial spillover of a country's high-tech industries. Using data from the high-tech industries in China during the years of 2007-2016, we establish a space lag model and a space error model to examine the space fixed effect, the time fixed effect, and the space-time double mixed effect in spatial spillover in high-tech industries. We compare the results of these two spatial panel models with those from a general panel model and find that the spatial spillover effect within high-tech industries is rather significant. Moreover, we find that the spatial-time double mixed of the spatial lag model is the best fitting effect. Our empirical results also show that the research and development (R&D) investment and international trade can positively promote spatial spillover of high-tech industries among different regions. In terms of policy insights, our results imply that the government can establish a technology transfer platform to promote the spillover in high-tech industries. This can help achieve a sustainable and balanced development of high-tech industries.Sustainability 2020, 12, 1551 2 of 16 agglomeration effects in the high-tech enterprises. In this paper, we focus on the following important questions: what are the critical factors which can influence the spatial spillover of high-tech industries? How do these factors influence the spatial spillover? What are the potential reasons for the unbalanced regional development of high-tech industries? The answers to these questions are of great theoretical and practical significance because they can help improve the output of the high-tech industries and promote the sustainable and balanced development of regional economies for many countries. For example, China is a vast country with regions with unbalanced economic development. The high-tech industries are highly concentrated in only several developed regions like Guangdong Province. How do you achieve the spillover of high-tech industries to other underdeveloped regions? This question is important not only for China's economic development, but also for many countries in the world with unbalanced regional economic development.In this paper, we employ spatial panel models to study spatial spillover in high-tech industries. We adopt the spatial panel models because they can deal with the spatial dependence among regions. In contrast, traditional models such as Ordinary Least Squares (OLS) models ignore the spatial effect and lead to estimation errors. To be more specific, in this research, we employ the spatial lag model (SLM) and the spatial error model (SEM), which are two of the most widely used spatial panel models. The SEM focuses on the influence of the error of random interferenc...

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“…It follows from (36) that j N j increases with j . As a result, A < D , (7) and (12) imply that the science park hosts more researchers and production workers than the ‡at city. By implication, the …nal sector is more productive when a science park is formed because more Summarizing yields the following proposition.…”

Section: Stage 1 the Equilibrium Mass Of Intermediate …Rmsmentioning

confidence: 99%

On the Economics of Science Parks

Liang¹,

Mai²,

Wang³

2019

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Science parks play a growing in knowledge-based economies by accommodating high-tech firms and providing an environment that fosters location-dependent knowledge spillovers and promote R&D investments by firms. Yet, not much is known about the economic conditions under which such entities may form in equilibrium without government interventions. This paper develops a spatial equilibrium model with a competitive final sector and a monopolistic competitive intermediate sector, which allows us to determine necessary and sufficient conditions for a science park to emerge as an equilibrium outcome. We show that strong localized knowledge spillovers, high startup costs, skilled labor abundance, or low commuting costs make intermediate firms more likely to cluster and a science park more likely to form. We also show that the productivity of the final sector is highest when intermediate firms cluster. As the decay penalty, firms' startup and workers' commuting costs become lower, science parks will eventually be fragmented.

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The agglomeration of American R&D labs

Cited by 61 publications

References 15 publications

Within‐industry agglomeration of occupations: Evidence from census microdata

Within‐industry agglomeration of occupations: Evidence from census microdata

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

On the Economics of Science Parks

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