Dynamic of foreign direct investment in the states of Mexico: An analysis of Markov's spatial chains

Abstract

The aim of this investigation is to analyze the evolution of the spatio-temporal distribution of foreign direct investment (FDI) across Mexican states. The literature that analyzes foreign direct investment in Mexico is numerous and diverse; however, it is argued that the analysis of the spatio-temporal distribution of FDI conditioned to spatial interaction effects in Mexico is still absent. In this sense, by applying the spatial Markov chain approach as proposed by Rey (2001), we found a divergence process in the FDI inflows among Mexican states that seem to get stronger over time. In particular, during the period from 2006 to 2013, the process of divergence toward higher-FDI inflows quantiles occurs among states spatially associated with neighbors in lower-FDI quantiles.

Spatial spillover effect of carbon emission efficiency in the construction industry of China

“…They observed that the carbon emission intensity of neighboring provinces has a strong effect on local carbon emissions. Taking foreign direct investment (FDI) in Mexico as study the object, Torres Preciado et al (2017) analyzed the spatiotemporal distribution of FDI and confirmed that the process of transitioning toward higher FDI inflows occurred in regions spatially adjacent to regions with lower FDI. The spatial Markov chain has also been employed to describe the phenomenon of regional-reinforcing agglomeration (Pan et al 2015), as well as to analyze the spillover effect on cities' dynamic evolution of carbon intensity (Liao and Wei 2012).…”

Club Convergence and Spatial Effect on Green Development of the Yangtze River Economic Belt in China with Markov Chains Approach

“…Based on the existing literature on green development composite index [23,28], the "coordination level" is added to this study to construct an innovative evaluation system of "capacity level-coordination level-comprehensive level" (Figure 2). This research categorizes the urban green development into several dimensions, including resources, environment, economy, and society subsystems.…”

“…Given the rise in the research on green development evaluation, additional attention has been paid to spatial distribution and clustering characteristics of regional green development via visualization function of ArcGIS and exploratory spatial data analysis (ESDA) [18,[21][22][23]. These studies can hardly show the dynamic evolution process of the region's green development which necessitates applying the comparative analysis of the cross-section data in different years.…”

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

“…Not long ago, the data’s spatial dimension (geographic dimension) has been incorporated into the Markov chain (MC) framework. Rey ( 2001 ) had developed a spatial Markov chain (MC) methodology for analyzing the spatial–temporal dynamics of random phenomena, which had been applied in a diversity of several fields, including GDP disparities among European regions (Le Gallo, 2004 ), manufacturing in Brazilian regions (Schettini et al, 2011 ), regional wealth disparity in Zhejiang, China (Yue et al, 2014 ), pro-environmental behavior in Italian provinces (Agovino et al, 2016 ), foreign direct investment in Mexico states (Torres Preciado et al, 2017 ), electric vehicle charging in cities (Shepero & Munkhammar, 2018 ), proximity effects in the US on obesity epidemic rates (Agovino et al, 2019 ), air pollution index in Peninsular Malaysia (Alyousifi et al, 2020 ), drought class transitions in Southwest China (Yang et al, 2020 ), COVID-19 dynamics in Asian countries (Dehghan Shabani & Shahnazi, 2020 ). Despite the long history of using this methodology, two interesting problems raise to the surface.…”