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The Affordable Connectivity Program (ACP) represents a historic investment by the federal government to address the digital divide. Its goals are both to increase the rate of broadband adoption and make it easier for low-income households to sustain service. But what have been the drivers of ACP enrollment? We know a great deal about trends in adoption and the geography of enrollment among eligible households. But those metrics – especially a focus on enrollment rates among eligible households – are only so useful in explaining ACP sign-ups. This invites analysis that investigates the drivers of ACP enrollment in a comprehensive way, which this paper proposes to do. The paper hypothesizes that ACP enrollment decisions are not solely individual (i.e., that a household's enrollment decision is structured only by income level), but also influenced by community-wide considerations, such as housing costs, share of occupied houses, presence of anchor institutions such as public libraries, and population density (i.e., whether a place is urban or rural). The paper develops a regression model that predicts ACP enrollment rates among eligible households at the 5-digit zip code geography as a function of the variables discussed above, as well as others such as the racial and ethnic make-up of a zip code area. The analysis also controls for existing levels of broadband subscriptions and computer ownership in a given area. The difference between predicted and actual ACP enrollment rates at the 5-digit zip code level is proposed as a metric of ACP performance. These findings are supplemented with a geographically weighted regression (GWR) model that allows the relationship between each explanatory variable and ACP enrollment to vary by locality. This allows for analyzing and visualizing how these relationships vary across space. The empirical findings show that high levels of economic distress are a strong determinant of ACP enrollment in a given area, but social and community-wide indicators are important as well. The GWR results demonstrate that important differences in these relationships exist across geographies. Understanding spatial variations in ACP performance can help policymakers and other stakeholders better target resources to address the digital divide. • We provide background on the ACP, a recent program that subsidizes broadband service for low-income U.S. households. • We estimate ACP enrollment using both ordinary least squares (OLS) and geographically weighted regression (GWR). • OLS results show that economic distress and local library presence are strongly positively associated with ACP enrollment. • Higher rates of foreign-born or elderly residents are negatively associated with ACP enrollment. • GWR results show that relationships between ACP enrollment and local characteristics vary widely and are context-specific. [ABSTRACT FROM AUTHOR]

Abstract: Background: Many osteopathic medical schools across the United States seek to produce physicians who will ultimately locate in rural areas. Most of these schools offer courses and programs designed to promote rural location. Objective: To explore how mandatory and optional programs offered by Oklahoma State University (OSU) College of Osteopathic Medicine (and student perception of them) impacted the location decisions of students graduating from the osteopathic program between 1997 and 2002. Methods: Data on student hometowns, medical school schedules and course evaluations, and current practice location were obtained for 190 OSU osteopathic graduates. Logistic regression was used to find factors that were significant in predicting the location decision. Results: Of the 190 students in the dataset who are now practicing physicians, approximately 30% chose to practice in a rural location. The regression results suggest that an optional summer program during a student’s first year in which they spend 4 weeks in a rural primary care setting increases their probability of locating in a rural area by roughly 24%. There is also some evidence that a more positive overall experience in the first one-month rotation in a rural community hospital improves the likelihood of rural location. Conclusions: The findings show that programs implemented by an osteopathic school can influence where graduates choose to practice. In particular, programs taking place in both the 1st and 4th year of training have an impact on rural practice location, implying that students can be influenced both early and late in their medical school careers. [Copyright &y& Elsevier]

We examine the effects of broadband speed on county unemployment rates in the U.S. state of Tennessee. We merge the older National Broadband Map dataset and the newer FCC dataset in lengthening our broadband access data over the period 2011–2015. Extending the dataset improves the precision of the estimates. Our panel regressions control for potential selection bias and reverse causality and show that broadband speed matters: unemployment rates are about 0.26 percentage points lower in counties with high speeds compared to counties with low speeds. Ultra-high speed broadband also appears to reduce unemployment rates; however, we are unable to distinguish between the effects of high and ultra-high speed broadband. We document beneficial effects of the early adoption of high speed broadband on unemployment rates. Better quality broadband appears to have a disproportionately greater effect in rural areas. • We examine the effects of broadband speed on county unemployment rates in the U.S. state of Tennessee. • We merge the National Broadband Map and the FCC datasets to get broadband access data over the period 2011-2015. • We find that unemployment rates are about 0.26 percentage points lower in high speed counties. • We document beneficial effects of the early adoption of high speed broadband on unemployment rates. • Better quality broadband appears to have a disproportionately greater effect in rural areas. [ABSTRACT FROM AUTHOR]