Analysis of fixed broadband access and use in Thailand: Drivers and barriers

Srinuan, Chalita; Bohlin, Erik

doi:10.1016/j.telpol.2013.03.006

Cited by 26 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This means that the low probability of living in extreme multidimensional poverty related to having Internet access is more than ten times higher for the southwest region than for both the northwest and northeast regions. This regional variation reinforces the findings of studies that identify geographic location as a key factor in the impact that Internet access can have on several development variables (Pick and Nishida, 2015;Srinuan and Bohlin, 2013). Moreover, our estimates coincide with Sujarwoto and Tampubolon's (2016) assertion that the Internet deepens spatial inequality among regions in a given territory.…”

Section: Discussionsupporting

confidence: 87%

Exploring the impacts of Internet access on poverty: A regional analysis of rural Mexico

García‐Mora

Mora‐Rivera

2021

New Media & Society

View full text Add to dashboard Cite

The purpose of this article is to estimate the impact of Internet access on poverty for a sample of people living in contrasting regions located across rural Mexico. Using a quasi-experimental technique, our results suggest that Internet access is an additional mechanism that contributes to decreasing poverty levels. Consequently, it could assist in increasing the proportion of people living out of poverty in rural areas; however, this is not uniform across regions (higher positive effects are estimated for less developed regions). These findings highlight the prevailing regional heterogeneity in Mexico’s rural sector and suggest that governments must design better-targeted public policies that address the uneven development in Internet diffusion typical of the rural sector. These policy improvements would allow governments to maximize the potential benefits of the Internet, as this technology alone is not sufficient to break the cycle of poverty in middle-income countries like Mexico.

show abstract

Section: Discussionsupporting

confidence: 87%

Exploring the impacts of Internet access on poverty: A regional analysis of rural Mexico

García‐Mora

Mora‐Rivera

2021

New Media & Society

View full text Add to dashboard Cite

show abstract

“…Smaller businesses are more correlated with broadband provision than medium or large businesses often because operators avoid trying to serve large firms and institutions who seek specialist high-bandwidth infrastructure instead. Total population, median age and household density were also all positive demand-side determinants (Srinuan & Bohlin, 2013). Grubesic (2006b)) determined that large non-white populations and median age were negative demand-side determinants.…”

Section: Fixed and Mobile Ict Infrastructure In Contextmentioning

confidence: 99%

Who’s Superconnected and Who’s Not? Investment in the UK’s Information and Communication Technologies (ICT) Infrastructure

Oughton

Tyler

Alderson

2015

Infrastruct. Complex

View full text Add to dashboard Cite

The Information and Communication Technologies (ICT) infrastructure sector has dramatically expanded over the past decade as the demand for increased digital connectivity has increased from both companies and consumers. Broadband investment has been increasingly associated with positive economic growth and digital connectivity is seen as an essential ingredient with which to increase productivity, employment and create new enterprises. Hence, there is concern that companies and consumers in particular locations are disadvantaged if they are unable to obtain sufficient connectivity. At the present time there has been limited analysis of where new investment has taken place, why it has taken place in specific locations, and what the key economic and socio-economic drivers have been influencing this. The role of regulation in this process is also important to understand. This article draws on two unique, uncensored infrastructure datasets from the UK's telecommunications regulator Ofcom to assess the factors driving investment in fixed and mobile ICT infrastructure. The fixed infrastructure model utilised modem sync speed measurements from over 20 million premises, aggregated to 7004 Middle Super Output Areas (MSOA) (97.3 %) in England and Wales, to provide comprehensive micro-geographic analysis for the first time. The mobile model employed the average data transfer per premises as a network capacity-demand metric for 173 counties and Unitary Local Authorities (ULAs) (98.3 %) in England, Scotland and Wales. Using predictors at a range of spatial scales, multilevel modelling utilising Markov Chain Monte Carlo (MCMC) methods was used to estimate both the fixed and mobile broadband infrastructure models. The results confirm many of the prevailing postulates of existing telecommunications theory, namely, that dense, wealthy and well-educated areas are attractive investment hotbeds for telecommunication technologies. In the UK's fixed ICT infrastructure market, inter-platform competition was found to have a positive impact on investment compared to the mixed results found for intra-platform competition. On the whole, telecommunication investment in the UK appears to be driven by the same drivers as the much documented U.S. case, but further spatially granular research needs to be undertaken to examine the market dynamics between the incumbent and different forms of induced competition across the telecommunication network.

show abstract

“…The closest study to our research was carried out in Thailand, where Srinuan & Bohlin (2013) investigated the determinants of online activities conditioned on the availability of broadband at home. Their findings show that the availability of fixed infrastructure and various demographic variables affect the existence of broadband access at home, and moreover, each factor has a different impact on specific online activities.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we first estimate the probit model of the availability of broadband connection on various individual and household level characteristics. Unlike Srinuan & Bohlin (2013), where availability of fixed broadband connection at home is estimated, we add mobile connections. It is mostly because of the increasing usage of cell phones in Turkey (Torlak et al, 2011).…”

mentioning

confidence: 99%