Estimating individual effects and their spatial spillovers in linear panel data models: Public capital spillovers after all?

Miranda, Karen; Martínez-Ibañez, Oscar; Manjón-Antolín, Miguel

doi:10.1016/j.spasta.2017.07.012

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…Wooldridge, 2010, pp. 287-288;Miranda et al, 2017). The kx1 vectors x i1 and x i2 contain the initial values of the x-variables for region i in the first and second period, respectively.…”

Section: Discussionmentioning

confidence: 99%

Research and development intensive clusters and regional competitiveness

Kosfeld

Mitze

2023

Growth and Change

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Modern cluster theory provides reasons for positive external effects that accrue from the interaction of spatially proximate firms operating in common and related fields of economic activity. In this paper, we examine the impact of R&D‐intensive clusters as a key factor of regional competitiveness on productivity growth. In relying on a hybrid approach of cluster identification, we examine effects of cluster specialization and diversity for a panel of German NUTS‐3 regions in 2003–2019. After controlling for regional characteristics and unobserved heterogeneity, a robust cluster strength effect (i.e., specialization) on productivity growth is found within the context of conditional convergence across German regions. With regard to the underlying mechanisms, we find that the presence of multiple R&D‐intensive clusters in specific technological fields is most strongly linked to higher levels of regional productivity growth. We also find that advantages from cluster specialization are strongest in key industrial sector such as automobile production, machinery, chemical and pharmaceutical products. Overall, our estimates particularly highlight the working of Marshallian externalities in productivity dynamics, while Jacobs‐type spillovers tend to be partially realized. These findings indicate that some but not all cluster‐based regional development strategies are promising policy tools to foster regional growth processes.

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“…Wooldridge, 2010, pp. 287-288;Miranda et al, 2017). The kx1 vectors x i1 and x i2 contain the initial values of the x-variables for region i in the first and second period, respectively.…”

Section: Discussionmentioning

confidence: 99%

Research and development intensive clusters and regional competitiveness

Kosfeld

Mitze

2023

Growth and Change

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“…Term C t is an N × K matrix of a control variable, where K is the number of controls. F td represents the common factors of dimension d ; λ d is an N × 1 vector where d = 1, 2, ⋯, D is the dimension of factors, representing factor loadings with dimension d . represents spatially weighted fixed effects [52, 53]. Finally, ε t is an i.i.d.…”

Section: Methodsmentioning

confidence: 99%

“…This factor structure allows the proposed model to capture time-varying unobservable elements, such as people’s fear and caution of new variants emerging in some countries with different loadings over cross-sectional units, and can better describe these than ordinary two-way fixed effects. Meanwhile, represents spatially weighted fixed effects [36, 37]. Finally, ε t is an i.i.d.…”

Section: Methodsmentioning

confidence: 99%

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Unravelling habituation for COVID-19-related information: A panel data study in Japan

Fukui¹

2022

Preprint

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This study investigates how human mobility has changed in the long-term in response to the COVID-19-related information in Japan. We use publicly available data from Google on human mobility in retail & recreation and residential spent time. These variables can be explained using daily data on the number of infected cases, whether the state of the emergency is declared or not, and the cumulated number of the vaccinated person. In the regression analysis, we use the 'interactive effects model' to control complicated unobservable factors that vary across time and cross-sectional dimensions. Our regression results find that people feared an unknown virus in the 1st wave, but the habituation trend for human mobility is noticed for the repeated similar infection information. However, from a different kind of information about the spread of new variants, people's habituation comes to a halt to some extent. Further, the spatial interaction of infection information is observed. We also show that people reacted appropriately to infection information even without a state of emergency declaration. Also, vaccination promotion encourages people to go out with security. When implementing policies to control human mobility, it is essential to consider the timing, and the degree of information penetration, carefully.

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“…Spatial spillover effects have been widely and frequently analysed in the fields of economics and economic geography, in which spatial dependence and regional interactions have been comprehensively discussed (Fingleton & Lopez-Bazo, 2006;Huang, Zhou, Wang, Chang & Ma, 2017). Spatial effects have also been widely considered in other fields in which location is highly relevant, including networking and transport infrastructures (Álvarez, Barbero & Zofío, 2016;Condeço-Melhorado, Tillema, de Jong & Koopal, 2014) capital inflow and outflow analysis (Miranda, Martínez-Ibañez & Manjón-Antolín, 2017).…”

Section: Spatial Spillovermentioning

confidence: 99%