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Record W3123319684

The Impact of Broadband Infrastructure on Economic Growth in Egypt and Some Arab and Emerging Countries

2011· preprint· en· W3123319684 on OpenAlexaboutno aff
Mona Farid Badran

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsBroadbandCompetition (biology)Telecom infrastructure sharingBusinessEmerging marketsForeign direct investmentBroadband networksOrder (exchange)TelecommunicationsQuarter (Canadian coin)The InternetIndustrial organizationInternational tradeInternational economicsEconomicsEngineeringFinanceGeography
DOInot available

Abstract

fetched live from OpenAlex

Infrastructure investments expansion typically adds to the productive capacity in an economy, and thus to its economic growth. Network industries account for between one–tenth and one-quarter of economy wide investments (OECD 2009 B). Telecom industry is one of the network industries, and broadband is a new technology that is widely used all over the world. It is the popular mode of access to the internet, as it refers to high speed internet access. This study aims at examining the impact of broadband infrastructure on economic growth in emerging countries and Arab countries. In addition, the impact of competition in telecom sector has been included in the growth equation estimated to control for the effect of competition in telecom sector, and thus broadband, on the economic growth in these countries. The empirical study reaches a conclusion in line with previous studies that there is a positive impact of broadband uptake on economic growth. In addition, the contribution of this paper is also in the construction of the competition index which was statistically insignificant, but became significant once we controlled for FDI as a percent of GDP. Thus, governments should create an enabling environment and open their markets for more competition in order to induce the establishments of more broadband and telecom networks in their respective countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.283
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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