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James E. Prieger

Researcher at Pepperdine University

Publications -  122
Citations -  2010

James E. Prieger is an academic researcher from Pepperdine University. The author has contributed to research in topics: Digital divide & Broadband. The author has an hindex of 20, co-authored 118 publications receiving 1818 citations. Previous affiliations of James E. Prieger include University of California & University of California, Davis.

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The Supply Side of the Digital Divide: Is There Equal Availability in the Broadband Internet Access Market?

TL;DR: In this paper, the authors look for evidence of unequal broadband availability in areas with high concentrations of poor, minority, or rural households and find that there is little evidence for unequal availability based on income or on black or Hispanic concentration.
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The broadband digital divide and the economic benefits of mobile broadband for rural areas

TL;DR: In this article, the authors examined the broadband digital divide between rural and urban households in the US and found that the broadband usage gap is proportionally greater for low-income households.
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The Broadband Digital Divide and the Nexus of Race, Competition, and Quality

TL;DR: The gaps in DSL demand for blacks and Hispanics do not disappear when income, education, and other demographic variables are accounted for, but lack of competition is an important driver of the Digital Divide for blacks.
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The broadband digital divide and the nexus of race, competition, and quality

TL;DR: In this paper, the authors examine the gap in broadband access to the Internet between minority groups and white households with geographically fine data on DSL subscription and examine quality of service and competition as components of the Digital Divide.
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A flexible parametric selection model for non‐normal data with application to health care usage

TL;DR: Applying the model to the hospitalization data indicates that the FPS model may be preferred even in cases in which other parametric approaches are available, and a new approach for sample selection problems in parametric duration models is developed.