scholarly journals Analysis of Emigration in Europe Using the Spatial Dynamic Shift-Share Method

2015 ◽  
Vol 15 (2) ◽  
pp. 7-26 ◽  
Author(s):  
Elżbieta Antczak ◽  
Karolina Lewandowska-Gwarda
Keyword(s):  
European Countries ◽  
Reference Area ◽  
Spatial Dynamic ◽  
Global Effect ◽  
Geographic Factors ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Global Mean ◽  
Dynamic Shift

Abstract The aim of the paper is to examine the pace of change in emigration levels in 24 selected European countries from 1999 to 2012, by age, country and the reference area. The spatial dynamic shift-share method is used in this research. The study analyses each country’s share and identifies structural as well as geographic factors in the volume of the net global effect. It also considers a spatial weights matrix. Results reveal that the global mean pace of change in the emigration is positive. The pace of phenomenon is the fastest among individuals aged 45–64 and also 35–44 years and in Czech Rep., Lithuania, Spain, Hungary and Germany.

Spatial Weights Matrix

2008 ◽  
pp. 1113-1113 ◽  
Author(s):  
Xiaobo Zhou ◽  
Henry Lin
Keyword(s):  
Spatial Weights ◽  
Spatial Weights Matrix

scholarly journals An Empirical Study Of Productivity Growth In EU28 - Spatial Panel Analysis

2014 ◽  
Vol 17 (4) ◽  
pp. 187-202
Author(s):  
Alicja Olejnik
Keyword(s):  
Productivity Growth ◽  
Fixed Effects ◽  
Spatial Modelling ◽  
Spatial Process ◽  
The European Union ◽  
Spatial Econometric ◽  
Spatial Panel ◽  
Additional Information ◽  
Spatial Weights ◽  
Spatial Weights Matrix

This paper investigates the spatial process of productivity growth in the European Union on the foundations of the theory of New Economic Geography. The proposed model is based on the study of NUTS 2 regions and takes into consideration a spatial weights matrix in order to better describe the structure of spatial dependence between EU regions. Furthermore, our paper attempts to investigate the applicability of some new approaches to spatial modelling including parameterization of the spatial weights matrix. Our study presents an application of the spatial panel model with fixed effects to Fingleton’s theoretical framework. We suggest that the applied approach constitutes an innovation to spatial econometric studies providing additional information hence, a deeper analysis of the investigated problem.

Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

2020 ◽  
Vol 05 (1&2) ◽  
pp. 1-13
Author(s):  
Mohamed R Abonazel ◽  
Keyword(s):  
Panel Data ◽  
Personal Income ◽  
Spatial Weights ◽  
Spatial Panel Data Models ◽  
Spatial Weights Matrix ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
Per Capita ◽  
Capita Personal Income ◽  
Inverse Distance

Over the last decades, the Per Capita Personal Income (PCPI) variable was a common measure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by using spatial panel data models for 48 U.S. states during the period from 2009 to 2017. We utilize the three following models: spatial autoregressive (SAR) model, Spatial Error (SEM) Model, and Spatial Autoregressive Combined (SAC) model, with individual (or spatial) fixe deffects according to three different known methods for constructing spatial weights matrices: binary contiguity, inverse distance, and Gaussian transformation spatial weights matrix. Additionally, we pay attention for direct and indirect effects estimates of the explanatory variables for SAR, SEM, and SAC models. The second objective of this paper is to show how to select the appropriate model to fit our data. The results indicate that the three used spatial weights matrices provide the same result based on goodness of fit criteria, and the SAC model is the most appropriate model among the models presented. However, the SAC model with spatial weights matrix based on inverse distance is better compared to other used models. Also, the results indicate that percentage of individuals with graduate or professional degree, real Gross Domestic Product (GDP) per capita,and number of nonfarm jobs have a positive impact on the PCPI, while the percentage of individuals without degree or bachelor’s degree have a negative impact on the PCPI.

scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

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