Maximizing the collective learning effects in regional economic development

Gao, Jian

doi:10.1109/iccwamtip.2017.8301509

Cited by 12 publications

(24 citation statements)

References 18 publications

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“…The importance of knowledge diffusion in the diversification of economic activities, however, is not limited only to the export of products. It has also been observed in the development of regional industries (Neffke et al, 2011;Gao et al, 2017), research activities (Guevara et al, 2016), and technologies (Kogler et al, 2013;Boschma et al, 2014), suggesting that relatedness between economic activities facilitates knowledge diffusion in general, not only in the context of international trade.…”

Section: Introductionmentioning

confidence: 98%

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

et al. 2019

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During the last decades two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are similar to their current exports, suggesting that knowledge diffusion among related industries is a key constrain shaping the diversification of exports. But does knowledge about how to export to a destination also diffuses among related products and geographic neighbors? Do countries need to learn how to trade each product to each destination? Here, we use bilateral trade data from 2000 to 2015 to show that countries are more likely to increase their exports of a product to a destination when: (i) they export related products to it, (ii) they export the same product to the neighbor of a destination, (iii) they have neighbors who export the same product to that destination. Then, we explore the magnitude of these effects for new, nascent, and experienced exporters, (exporters with and without comparative advantage in a product) and also for groups of products with different level of technological sophistication. We find that the effects of product and geographic relatedness are stronger for new exporters, and also, that the effect of product relatedness is stronger for more technologically sophisticated products. These findings support the idea that international trade is shaped by information frictions that are reduced in the presence of related products and experienced geographic neighbors.

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Section: Introductionmentioning

confidence: 98%

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

et al. 2019

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“…Social reinforcement effects also occur widely in other types of behavioral adoption processes, such as using menstrual cups [150], adopting seeding strategies [151], taking a new diagnostic method [152], joining social movement activities [153], retweeting politics hashtags [154], and learning a new industry [155].…”

Section: Social Reinforcementmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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“…Using 25 years of economic data in China between 1990 and 2015, they addressed the endogeneity concerns by using the difference-in-differences analysis. Results showed that the high-speed rail development increased the industrial similarity of connected pairs of neighboring provinces 45 .…”

Section: Introductionmentioning

confidence: 99%

“…The reduction was largely driven by the concurrent drop in fixed asset investments of those bypassed counties 44 . Gao et al 45 introduced the concept of inter-industry and inter-regional learnings of regional economic development. Using 25 years of economic data in China between 1990 and 2015, they addressed the endogeneity concerns by using the difference-in-differences analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Regional Economic Development Indicator from Transportation Network Analytics

Gao

Liang

et al. 2020

Sci Rep

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With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we attempt to establish the relevance of mid-distance land transport patterns to regional economic status through transportation network analyses. We apply standard measurements of complex networks to analyze the highway transportation networks. A set of traffic flow features are computed and correlated to the regional economic development indicator. The multi-linear regression models explain about 89% to 96% of the variation of cities' GDP across three provinces in China. We then fit gravity models using annual traffic volumes of cars, buses, and freight trucks between pairs of cities for each province separately as well as for the whole dataset. We find the temporal changes of distance-decay effects on spatial interactions between cities in transportation networks, which link to the economic development patterns of each province. We conclude that transportation big data reveal the status of regional economic development and contain valuable information of human mobility, production linkages, and logistics for regional management and planning. Our research offers insights into the investigation of regional economic development status using highway transportation big data. * This is a pre-print of an article submitted to Scientific Reports. The final authenticated version is available online at: https://doi.

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Maximizing the collective learning effects in regional economic development

Cited by 12 publications

References 18 publications

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

Coevolution spreading in complex networks

Estimation of Regional Economic Development Indicator from Transportation Network Analytics

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