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Enregistrement W3122715189

Broadband Adoption: Translating the Digital Divide Literature into Effective Government Policies and Actions

2016· preprint· en· W3122715189 sur OpenAlexaboutno aff
Stanford L. Levin, Stephen R. Schmidt, Graham Scott

Notice bibliographique

RevueRePEc: Research Papers in Economics · 2016
Typepreprint
Langueen
DomaineEngineering
ThématiqueICT Impact and Policies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBroadbandDownloadBusinessGovernment (linguistics)TelecommunicationsInvestment (military)Digital divideBroadband networksTelecommutingMobile broadbandThe InternetEngineeringComputer sciencePoliticsPolitical scienceElectronicsWireless
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

North America, many countries in Europe, and some countries in East Asia with advanced telecommunications networks have benefited from substantial investment aimed at increasing broadband availability for residences and businesses. In some countries, such as the U. S. and Canada, this investment has taken place as a result of market forces, with little attention from government, while in other countries government involvement has been relatively greater (whether in the form of directed private investment or direct public ownership). As a result of these differing approaches, broadband is widely available in many countries. For example, in Canada (2014) 99% of households have access to broadband speeds of 1.5 Mbps or faster, 96% of households have service with at least a 5 Mbps download speed, 81% have access at 30 Mbps or faster, and 71% have access at 100 Mbps. In the U.S, 96% of Americans had access to fixed broadband speeds of 3 Mbps/768 kbp and 83% of Americans have access to fixed broadband speeds of 25 Mbps/3 Mbps in 2013. While much press attention is focused on companies that may be able to provide 100 Mbps download speeds, or even 1 Gbps, such very high speeds at this time, and for the next few years, are not actually required even for the most demanding services such as watching high-definition video. This is evidenced by the fact that residential customers often do not subscribe to the fastest broadband speed available. Specialized users, such as medical facilities, can usually obtain extra-fast broadband if it is required. What has been mostly missing from the policy discussion and from actual policy is a major focus on achieving the adoption of broadband, as opposed to the availability of broadband networks. The number of households, for example, that actually subscribe to broadband is typically lower than the number of households that have access to broadband. In Canada, for example, while 99% of households have access to broadband, only 82% actually subscribe. A similar material gap between availability and adoption is evident in the U.S. This paper accordingly asks two principal questions: (1) What are the reasons why adoption of broadband lags behind availability in countries with advanced communications networks? (2) What policies or actions are likely to increase adoption? Given the importance of broadband in increasing economic efficiency, as well as providing communication opportunities and entertainment for individuals, and because broadband is often widely available at speeds that exceed those required by households and businesses, encouraging adoption of broadband should be a key policy objective. To this end, it is imperative to understand the impediments to broadband use so that effective policies can be designed and implemented. Studies have confirmed that it is not the price of broadband itself that is a significant deterrent to adoption. This result is even more striking because most studies do not distinguish between the cost of broadband access and the cost of equipment, such as a computer, that is required to use broadband. Other factors are, in fact, much more important. These include age, income, education, labor market skills, and employment status, among others. Also, people often say that they lack the requisite skills to use a computer or otherwise access broadband. Effective policies to encourage broadband adoption, then, must be directed to reducing the actual barriers that prevent people from using broadband. There are actions that policy makers can take, but they are not the often-identified simple measures such as cutting the price of broadband access. Effective policies will typically be more complex and difficult to implement, but there are some examples of the sort of policies that can be expected to work. Actions that have a prospect of increasing broadband adoption include: • Government programs can provide people with the skills to access and use broadband and can demonstrate the value of being connected to a digital economy. • Governments themselves can be leaders by moving information and services on line and encouraging people to use them. • The use of computers in education also spurs households to be connected. To get the full benefit of broadband networks, it is not just a question of playing games or watching videos on line. • Broadband can also be more deeply embedded in the economy to drive efficiencies and to achieve broader societal goals like reduced carbon output through telecommuting for work and through online shopping. There are also less obvious policies that will boost adoption. For example, facilities-based competition, also referred to as platform competition, for broadband service has been shown to increase adoption, probably as a result of strong competition among or between facilities-based providers. In addition, macro-economic policies are important because full employment and increasing productivity will boost incomes and improve labor market skills, spurring broadband adoption. Programs that help households acquire computers and the skills to use them can also increase broadband adoption.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,056
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,091

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,056
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0160,018
Études des sciences et des technologies0,0050,018
Communication savante0,0170,034
Science ouverte0,0020,011
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0140,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,283
Écart entre enseignants0,269 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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