Making the Connection: Broadband Access and Online Course Enrollment at Public Open Admissions Institutions

Skinner, Benjamin T.

doi:10.1007/s11162-018-9539-6

Cited by 23 publications

(33 citation statements)

References 37 publications

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“…To check whether there are other factors that might explain variation in our estimates of r 0 among counties, we investigated additional population characteristics 35 – 42 that might be expected to affect the initial spread rate of COVID-19: (i) proportion of the population over 65, (ii) adult obesity, (iii) diabetes, (iv) education, (v) income, (vi) poverty, (vii) economic equality, (viii) race, and (ix) political leaning (Supplementary Table 3 ). The first three characteristics likely affect morbidity 43 , although it is not clear how higher morbidity could affect the spread rate.…”

Section: Resultsmentioning

confidence: 99%

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Estimating and explaining the spread of COVID-19 at the county level in the USA

Ives

Bozzuto²

2021

Commun Biol

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The basic reproduction number, R0, determines the rate of spread of a communicable disease and therefore gives fundamental information needed to plan public health interventions. Using mortality records, we estimated the rate of spread of COVID-19 among 160 counties and county-aggregates in the USA at the start of the epidemic. We show that most of the high among-county variance is explained by four factors (R2 = 0.70): the timing of outbreak, population size, population density, and spatial location. For predictions of future spread, population density and spatial location are important, and for the latter we show that SARS-CoV-2 strains containing the G614 mutation to the spike gene are associated with higher rates of spread. Finally, the high predictability of R0 allows extending estimates to all 3109 counties in the conterminous 48 states. The high variation of R0 argues for public health policies enacted at the county level for controlling COVID-19.

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Section: Resultsmentioning

confidence: 99%

“…County-level variables were collected from several public data sources 36 – 42 . We selected socio-economic variables a priori in part to represent a broad set of population characteristics.…”

Section: Methodsmentioning

confidence: 99%

Estimating and explaining the spread of COVID-19 at the county level in the USA

Ives

Bozzuto²

2021

Commun Biol

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“…In principle, modern higher education differs from the previous model -it has ceased to be a "privilege" of youth and has become continuous in the process of all life. Due to subject orientation of education, students, graduate students and specialists understand that the engine of economic growth is information that helps gain knowledge and apply it in technology (Skinner, 2019;Henderson et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Interaction Of Digital Economy And Higher Education At The Modern Stage

Grevtsev¹,

Golubev²,

Попов³

2020

European Proceedings of Social and Behavioural Sciences

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“…2 Ortagus and Yang ( 2018 ) found a negative relationship between changes in state appropriation funding and changes in online enrollment at public 4-year institutions, suggesting that financial challenges may spur institutions to launch and grow online programs. Skinner ( 2019 ) found that online enrollments were smaller at open admission public institutions when nearby broadband speeds were at the lower end of the spectrum.…”

Section: Related Literaturementioning

confidence: 99%

Concentrated or Fragmented? The U.S. Market for Online Higher Education

Cheslock

Jaquette

2021

Res High Educ

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As the financial challenges facing the U.S higher education industry mount, colleges and universities seek new activities that can improve their financial situation. Online education programs are often viewed as a promising option due to growing student interest and the substantial net revenue generated by early entrants that leveraged economies of scale and scope. The number of schools that can experience similar outcomes will depend upon whether the online market is primarily concentrated, with a small number of higher education institutions enrolling most students, rather than fragmented, with large numbers of institutions enrolling meaningful amounts of online students. We examine this topic by providing the first detailed study of market concentration in online education. We first introduce a theory from economics, Sutton’s model of endogenous fixed costs, to the higher education literature to highlight key dynamics that shape concentration levels. Using institution-level enrollment data for the population of Title IV postsecondary institutions, we then empirically examine concentration levels in online education. We find that relative to in-person education, national online enrollment patterns are heavily concentrated. The online market has a distinctive structure, consisting of a small number of large national providers and a large number of small local providers. Online enrollment patterns became less concentrated between Fall 2012 and Fall 2018 due to major enrollment declines of large for-profit providers. Concentration levels, however, plateaued by the end of the period due to rapid enrollment growth at large national nonprofit providers.

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Making the Connection: Broadband Access and Online Course Enrollment at Public Open Admissions Institutions

Cited by 23 publications

References 37 publications

Estimating and explaining the spread of COVID-19 at the county level in the USA

Estimating and explaining the spread of COVID-19 at the county level in the USA

Interaction Of Digital Economy And Higher Education At The Modern Stage

Concentrated or Fragmented? The U.S. Market for Online Higher Education

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