Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
Information and Communication Technology (ICT) continues to be a growing sector around the world. In the past, quality levels of ICT access, such as through Telecommunication Infrastructure Index (TII), were investigated by nations to help determine a country’s ICT infrastructure capacity. As such,...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_math/12 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1012&context=etdb_math |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Information and Communication Technology (ICT) continues to be a growing sector around the world. In the past, quality levels of ICT access, such as through Telecommunication Infrastructure Index (TII), were investigated by nations to help determine a country’s ICT infrastructure capacity. As such, the Department of Information and Communications Technology conducted the first National ICT Household Survey in 2019 to investigate the current state of ICT in the Philippines and see if there is a digital divide. The research aimed to determine how province locations play a role in TII and how this can be incorporated into a model that predicts TII levels in the country using Geographically Weighted Regression (GWR). The GWR model assigned different weights and models for each province based on the values of their nearby neighbors. Moran’s I showed that significant spatial autocorrelations exist across provinces on TII, percentage of household heads with secondary education, and income per capita among others. Results showed that the percentage of household heads with secondary education per province is a common determinant of TII of all provinces in the Philippines. Furthermore, income per capita was a significant determinant in many provinces with high TII. Lastly, the GWR model provided better results than other models tested with the baseline Ordinary Least Squares (OLS) model, with lowest Mean Square Error (MSE), lower Mean Absolute Error (MAE), and higher adjusted R2.
Keywords: ICT Access, TII, GWR |
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