Abstract
In order to better understand the association between broadband and jobs/income in non-metropolitan counties, this study conducts spatial and first-differenced regressions using recent data from the Federal Communications Commission and the National Broadband Map. The relationships between broadband adoption/availability and jobs/income in rural areas are analyzed after controlling for a host of potentially influential variables such as age, race, educational attainment, transportation infrastructure, and the presence of natural amenities. Results from spatial error models using 2011 data provide evidence that high levels of broadband adoption in non-metro counties are positively related to the number of firms and total employees in those counties. The first-differenced regressions use data from 2008 and 2011 to suggest that increases in broadband adoption levels are associated with increases in median household income and the percentage of non-farm proprietors in non-metro counties. Interestingly, simply obtaining increases in broadband availability (not adoption) over this time has no statistical impact on either jobs or income.
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Notes
The FCC’s definition of broadband has changed over time. Historically, the definition has been 200 kilobits of data transfer per second (kbps) in at least 1 direction. The most recent (2010) definition is 4 megabits (mbps) download and 1 mbps upload. This report incorporates various thresholds, depending on the data used for analysis.
We recognize the difference between rural/urban (defined at the community level) and metro/non-metro (defined at the county level). Our data are county oriented, so we generally speak in terms of metro/micro/non-core; however, we still use the term “rural” to connote a lack of population density.
Initially, 3,109 counties were included, but Virginia independent cities were meshed with the counties where they reside to ultimately come up with 3,073.
The FCC used this speed (768 kbps down, 200 kbps up) at one point as a definition for broadband, and it is also used by the Broadband Technology Opportunities Program (BTOP) for reporting purposes. The current broadband definition (enacted in 2010) from the FCC is 4 mbps download and 1 mbps upload.
The FCC report notes that “...we have concerns that providers are reporting services as meeting the broadband speed benchmark when they likely do not. ... although mobile networks deployed as of June 30, 2010, may be capable of delivering peak speeds of 3 Mbps/768 kpbs or more in some circumstances, the conditions under which these peak speeds could actually occur are rare.” (FCC 2012, pp. 25–26).
The highest level of broadband availability is where \({<}2\,\%\) of the county’s population lacks access to wired broadband.
Multiple spatial weights matrices were tested before settling for the queen first order (including queen second order as well as rook first and second order).
We note that the aggregate SARMA model can test for the inclusion of both spatial autocorrelation and spatial heterogeneity. However, the spatial structure of these models is complex and is more difficult to interpret than individual spatial error or spatial lag parameters. The focus of this paper is on the broadband component, and as such the easier-to-interpret SEM specification was chosen.
The Local Moran’s I maps displayed in “Appendix” are compiled from a spatial weight matrix using 5 nearest neighbors, mitigating any potential geometric errors that may exist in the continental shapefile. Maps using a queen’s contiguity matrix had similar quantitative and qualitative results.
Kolko (2012) contains a useful discussion of how the relationship between broadband and employment could vary by location—and how it could hypothetically have a negative impact as technology is used as a substitute for labor.
Recall that a 1-unit increase in broadband adoption corresponds to a roughly 20 % increase in county household adoption rates due to the categorical nature of the FCC data.
The FCC data do contain information on the number of residential providers in a county, though it is not as detailed as the NBM data. For this analysis, we use FCC provider data, since the NBM data were not available until 2010.
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Acknowledgments
This study was supported by the National Agricultural and Rural Development Policy (NARDeP) Center under USDA/NIFA Grant no. 2012-70002-19385.
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Appendix
Appendix
See Fig. 6.
Local Moran’s I maps for dependent variables. a Median household income (MHHI), b establishments with paid employees (ESTPE), c % of non-farm proprietors (NFP), d total number of employees (TOTEMP)
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Whitacre, B., Gallardo, R. & Strover, S. Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions. Ann Reg Sci 53, 649–670 (2014). https://doi.org/10.1007/s00168-014-0637-x
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DOI: https://doi.org/10.1007/s00168-014-0637-x