Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    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).

  6. 6.

    The highest level of broadband availability is where \({<}2\,\%\) of the county’s population lacks access to wired broadband.

  7. 7.

    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).

  8. 8.

    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.

  9. 9.

    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.

  10. 10.

    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.

  11. 11.

    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.

  12. 12.

    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.


  1. Allison P (1990) Change scores as dependent variables in regression analysis. Sociol Methodol 20:93–114

    Article  Google Scholar 

  2. Anselin L (1988) Spatial econometrics: methods and models. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  3. Anselin L (2003) Spatial externalities, spatial multipliers, and spatial econometrics. Int Region Sci Rev 26(2):153–166

    Article  Google Scholar 

  4. Anselin L, Bera AK, Raymond F, Yoon MJ (1996) Simple diagnostic tests for spatial dependence. Reg Sci Urb Econ 26:77–104

    Article  Google Scholar 

  5. Atkinson R (2009) Policies to increase broadband adoption at home. The Information Technology and Innovation Foundation.

  6. Chi G (2010) The impacts of highway expansion on population change: an integrated spatial approach. Rural Sociol 75(1):58–89

    Article  Google Scholar 

  7. Czernich N, Falck O, Kretschmer T, Woessmann L (2011) Broadband infrastructure and economic growth. Econ J 121:505–532

    Article  Google Scholar 

  8. Dickes L, Lamie D, Whitacre B (2010) The struggle for broadband in rural America. Choices 25(4).

  9. Federal Communications Commission (FCC) (2012) Eighth broadband progress report.

  10. Ferguson M, Ali K, Olfert M, Partridge M (2007) Voting with their feet: jobs versus amenities. Growth Change 38(1):77–110

    Article  Google Scholar 

  11. Ford G (2011) Challenges in using the National Broadband Map’s data. Phoenix Center Policy Bull 27:1–21

    Google Scholar 

  12. Galloway L (2007) Can broadband access rescue the rural economy? J Small Bus Enterp Dev 14(4):641–653

    Article  Google Scholar 

  13. Gillett S, Lehr W, Osorio C, Sirbu M (2006) Measuring the economic impact of broadband deployment. Economic Development Administration, Dept. of Commerce.

  14. Goetz S, Fleming D, Rupasingha A (2012) The economic impacts of self-employment. J Agric Appl Econ 44(3):315–321

    Google Scholar 

  15. Goldfarb A, Prince J (2008) Internet adoption and usage patterns are different: implications for the digital divide. Inf Econ Policy 20(1):2–15

    Article  Google Scholar 

  16. Government Accountability Office (GAO) (2012) Broadband programs are ongoing, and agencies’ efforts would benefit from improved data quality. Report GAO-12-937.

  17. Grubesic T (2012) The U.S. National Broadband Map: data limitations and implications. Telecommun Policy 36:113–126

    Article  Google Scholar 

  18. Hauge J, Prieger J (2009) Demand-side programs to stimulate adoption of broadband: what works? Social Sciences Research Network.

  19. Holt L, Jamison M (2009) Broadband and contributions to economic growth: lessons from the US experience. Telecommun Policy 33:575–581

    Article  Google Scholar 

  20. Kandilov I, Renkow M (2010) Infrastructure investment and rural economic development: an evaluation of USDA’s broadband loan program. Growth Change 41(2):165–191

    Article  Google Scholar 

  21. Kolko J (2012) Broadband and local growth. J Urb Econ 71(1):100–113

    Article  Google Scholar 

  22. Kuttner H (2012) Broadband for rural America: economic impacts and economic opportunities. Report prepared for the Hudson Institute.

  23. LaRose R, Strover S, Gregg J, Straubhaar J (2011) The impact of rural broadband development: lessons from a natural field experiment. Gov Inf Q 28:91–100

    Article  Google Scholar 

  24. Lehr W, Osorio C, Gillett S (2005) Measuring broadband’s economic impact. Presented at the 33rd research conference on communication, information, and internet policy (TPRC), Arlington, VA.

  25. Mack E, Anselin L, Grubesic T (2011) The importance of broadband provision to knowledge intensive firm location. Reg Sci Policy Pract 3(1):17–35

    Article  Google Scholar 

  26. Mack E, Grubesic T (2009) Broadband provision and firm location: an exploratory data analysis. Tijdschrift voor Economische en Sociale Geografie 100(3):298–315

    Article  Google Scholar 

  27. Mack E, Faggian A (2013) Productivity and broadband: the human factor. Int Reg Sci Rev. doi:10.1177/0160017612471191

  28. Malecki EJ (2003) Digital development in rural areas: potentials and pitfalls. J Rural Stud 19(2):201–214

    Article  Google Scholar 

  29. Mayo J, Wallsten S (2011) From network externalities to broadband growth externalities: a bridge not yet built. Rev Ind Organ 38(2):173–190

    Article  Google Scholar 

  30. McGranahan D, Wojan T, Lambert D (2011) The rural growth trifecta: outdoor amenities, creative class and entrepreneurial context. J Econ Geogr 11:529–557

    Article  Google Scholar 

  31. Mills B, Whitacre B (2003) Understanding the metro-non-metro digital divide. Growth Change 34(2): 219–243

  32. National Telecommunications and Information Administration (NTIA) (1995) Falling through the net: a survey of have-nots in rural and urban America.

  33. NTIA (1999) Falling through the Net: defining the digital divide.

  34. NTIA (2000) Falling through the Net: toward digital inclusion.

  35. Parker EB (2000) Closing the digital divide in rural America. Telecommun Policy 24:281–290

    Article  Google Scholar 

  36. Sobel R, King K (2008) Does school choice increase the rate of youth entrepreneurship? Econ Educ Rev 27(4):429–438

    Article  Google Scholar 

  37. Stenberg P, Morehart M, Vogel S, Cromartie J, Breneman V, Brown D (2009) Broadband Internet’s value for rural America. Economic Research Report No. 70 (ERR-78).

  38. Stephens H, Partridge M (2011) Do entrepreneurs enhance economic growth in lagging regions? Growth Change 42:431–465

    Article  Google Scholar 

  39. Strover S (2001) Rural Internet connectivity. Telecommun Policy 25:331–347

    Article  Google Scholar 

  40. Strover S (ed) (2011) Scholars’ roundtable: the effects of expanding broadband to rural areas. Center for Rural Strategies, USA.

  41. Voss PR, Long DD, Hammer RB, Friedman S (2006) County child poverty rates in the US: a spatial regression approach. Popul Res Policy Rev 25(4):369–391

    Article  Google Scholar 

  42. Whitacre B, Mills B (2007) Infrastructure and the rural–urban divide in high-speed residential internet access. Int Reg Sci Rev 30(3):249–273

    Article  Google Scholar 

  43. Whitacre B, Gallardo R, Strover S (2014) Broadband’s contribution to economic growth in rural areas: moving towards a causal relationship. Telecommun Policy. doi:10.1016/j.telpol.2014.05.005

  44. Winship C, Morgan S (1999) The estimation of causal effects from observational data. Annu Rev Sociol 25:659–707

    Article  Google Scholar 

  45. Wu J, Gopinath M (2008) What causes spatial variations in economic development in the United States? Am J Agric Econ 90(2):392–408

    Article  Google Scholar 

Download references


This study was supported by the National Agricultural and Rural Development Policy (NARDeP) Center under USDA/NIFA Grant no. 2012-70002-19385.

Author information



Corresponding author

Correspondence to Brian Whitacre.



See Fig. 6.

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

JEL Classification

  • R11
  • O18