Modelling industrial firm relocation with impacts of spatial dependence

2017 ◽  
Vol 22 (1) ◽  
pp. 80-103 ◽  
Author(s):  
Youngsoo An ◽  
Li Wan
Keyword(s):  
Spatial Dependence ◽  
Industrial Firm ◽  
Firm Relocation

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Spatial Dependence in Sovereign Wealth Funds' Investments

2016 ◽  
Author(s):  
Nicolas Debarsy ◽  
Jean-Yves Gnabo ◽  
Malik Kerkour
Keyword(s):  
Spatial Dependence ◽  
Sovereign Wealth Funds

Spatial Dependence and Space-Time Trends in Extreme Events

2020 ◽  
Author(s):  
John H. J. Einmahl ◽  
Ana Ferreira ◽  
Laurens de Haan ◽  
Claudia Neves ◽  
Chen Zhou
Keyword(s):  
Extreme Events ◽  
Spatial Dependence ◽  
Time Trends ◽  
Space Time

Historical Perspectives on Innovation in Indian Business

2018 ◽  
Author(s):  
Claude Markovits
Keyword(s):  
Case Studies ◽  
Technological Innovation ◽  
Historical Perspective ◽  
Specific Context ◽  
Colonial Era ◽  
Industrial Firm ◽  
Historical Perspectives ◽  
Entire World ◽  
Definition Of ◽  
Trading Network

This chapter deals with the question of innovation in Indian business from a historical perspective. After a brief survey of the literature, emphasizing how divided scholarly opinion was regarding the existence of forms of innovation in Indian business prior to the colonial era, the focus shifts to the British period. It is shown that Schumpeter’s definition of innovation equating it with technological innovation cannot be fruitfully applied to the Indian business scene. Two case studies are then proposed: Tata Iron & Steel, the largest Indian industrial firm, is shown to have been innovative in the specific context of India’s backward industrial scene, while the Sindwork merchants of Hyderabad are an instance of an Indian trading network which extended its range to the entire world. Concluding remarks interrogate post-Independence developments and stress the limits of the innovativeness of Indian business, prior to the recent liberal reforms.

scholarly journals Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 282
Author(s):  
Mabel Morales-Otero ◽  
Vicente Núñez-Antón
Keyword(s):  
Infant Mortality ◽  
Count Data ◽  
Spatial Dependence ◽  
Poisson Model ◽  
Mortality Rates ◽  
Estimation Methods ◽  
Neighborhood Structure ◽  
Specific Context ◽  
Count Data Models ◽  
Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

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