TY - JOUR
T1 - Spatial spillover effect of transportation infrastructure on regional growth
AU - Karim, Abdul
AU - Suhartono,
AU - Prastyo, Dedy Dwi
N1 - Publisher Copyright:
© 2020 Institute of Economics, Ural Branch of the Russian Academy of Sciences. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Increased connectivity between regions in Indonesia is believed to impact the productivity capacity of each region, as well as its economic growth. Moreover, the influence of connectivity on the surrounding area is commonly known as the indirect effect (spillover effect). This effect can increase the number of products, goods and services used as production factors. The study aims to examine the effect of transportation infrastructure on economic growth. We used spatial modelling to estimate the impact of transportation infrastructure on the economy of 34 provinces in Indonesia in 2017. We applied the spatial lag of X model (SLX), spatial autoregressive model (SAR), spatial error model (SEM), spatial autoregressive combined model (SAC), spatial Durbin model (SDM), spatial Durbin error model (SDEM), and spatial autoregressive combined mixed model (SAC mixed). According to the estimation results, the SAC mixed model is the best spatial model, as it has the smallest value of the Akaike information criterion (AIC) and significant coefficients of ρ (rho) and λ (lambda) parameters. The results show that the indicators “bus stations”, “domestic investment” and “foreign investment” have a direct effect on the economic growth in 34 Indonesian provinces. In addition, we revealed the presence of indirect effects (spillovers) between provinces in Indonesia for the same variables.
AB - Increased connectivity between regions in Indonesia is believed to impact the productivity capacity of each region, as well as its economic growth. Moreover, the influence of connectivity on the surrounding area is commonly known as the indirect effect (spillover effect). This effect can increase the number of products, goods and services used as production factors. The study aims to examine the effect of transportation infrastructure on economic growth. We used spatial modelling to estimate the impact of transportation infrastructure on the economy of 34 provinces in Indonesia in 2017. We applied the spatial lag of X model (SLX), spatial autoregressive model (SAR), spatial error model (SEM), spatial autoregressive combined model (SAC), spatial Durbin model (SDM), spatial Durbin error model (SDEM), and spatial autoregressive combined mixed model (SAC mixed). According to the estimation results, the SAC mixed model is the best spatial model, as it has the smallest value of the Akaike information criterion (AIC) and significant coefficients of ρ (rho) and λ (lambda) parameters. The results show that the indicators “bus stations”, “domestic investment” and “foreign investment” have a direct effect on the economic growth in 34 Indonesian provinces. In addition, we revealed the presence of indirect effects (spillovers) between provinces in Indonesia for the same variables.
KW - Moran's Index
KW - Regional productivity
KW - Spatial Durbin error model
KW - Spatial Durbin model
KW - Spatial autoregressive
KW - Spatial econometrics
KW - Spatial error model
KW - Spatial modelling
KW - Spatial spillover
KW - Transportation infrastructure
UR - http://www.scopus.com/inward/record.url?scp=85094852947&partnerID=8YFLogxK
U2 - 10.17059/ekon.reg.2020-3-18
DO - 10.17059/ekon.reg.2020-3-18
M3 - Article
AN - SCOPUS:85094852947
SN - 2072-6414
VL - 16
SP - 911
EP - 920
JO - Economy of Regions
JF - Economy of Regions
IS - 3
ER -