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Does Place Really Matter? Broadband Availability, Race and Income
Does Place Really Matter?Broadband Availability, Race and Income Presentation by Ying Li, Ph.D. Research Analyst Joint Center for Political and Economic Studies And Mikyung Baek, Ph. D. Research and Technical Associate Kirwan Institute for the Study of Race and Ethnicity The Ohio State University At The National Broadband Map: Early Results from Social Science Research Washington, DC Tuesday, March 22, 2011
Research Questions• Explore the relationships between broadband availability and race & ethnicity, income, and place – To what extent is broadband readily available in low-income communities, especially those where minorities are more concentrated? – To what extent do urban and rural penetration rates show dramatic difference in broadband service deployment?
Three Case Studies• Los Angeles – majority-minority city with large Asian and Hispanic populations• Chicago – almost equal numbers of whites, African Americans and Hispanics• South Carolina – large low-income, rural and black populations
General Findings• “Race” was not a significant determinant of broadband deployment in low-income, high minority communities in all three regions.• “Income” was more significant in South Carolina and in select areas where residents were low-income, high minority like Inglewood, CA• Wireline and wireless coverage was uneven by income• Broadband speed might be an additional barrier in deployment in low-income, minority communities (finding in Inglewood needs exploration)• Adoption is still a prime issue because even with some level of competition, penetration rates are still low.
South Carolina: Broadband Providers and Population Density
South Carolina: Broadband Providers and Household Income
South Carolina: Broadband Providers and Blacks
Regression Analysis• Dependent Variable: broadband providers in South Carolina• Explaining Variables – Model 1: Race and Income, Adjusted R2 = 0.0388 – Subsequent models with more variables, urban/rural, pop. density, even lower Adj. R2• Need for more in-depth analysis, possibly using GWR, Geographic Weighted Regression
Data and Technical Issues• Dataset size – time/resource intensive• Availability of datasets by each geographical unit• Availability of residential subscription data• Wireless coverage data in GIS format• Problem with census block ID, inconsistent with concatenation of ST, CTY, Tract, BG, and Block IDs (e.g., New York data)
Going Forward• Availability <> Adoption, Why?• Cost• Type of service: wireline/wireless in relation to demographics• Speed