Abstract
We use proprietary real estate sales data and a variety of empirical methods to account for selection to study the economic impacts of the Broadband Initiatives Program. Broadband Initiatives Program is the largest grant and loan high-speed infrastructure program implemented by USDA and targeted to rural areas. The empirical results suggest that new broadband infrastructure did not have measurable impacts on residential house sale prices.
The authors thank Stephanie Shipp, Aaron Schroeder, Neil Kattampallil, Anil Rupasingha for their research support and valuable comments and discussions. The authors are also grateful to the USDA Rural Utility Service for their impeccable data work. This project was funded by the USDA Economic Research Service under contract #58-6000-8-00-39.
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Notes
- 1.
The percentages are obtained using American Community Survey five-year estimates of 2016–2020.
- 2.
Under USDA methodology rural areas are areas not located within a city, town or incorporated area having a population of more than 20,000 and not in an urbanized area that is contiguous and adjacent to a city or town with more than 50,000 population.
- 3.
Arms-length sales transaction (primary sale code “A”) is what we might call a “typical” transaction between two parties, not a special transaction between parties, such as a sale to a relative for a reduced amount.
- 4.
Cook’s Distance is an estimate of the influence of a data point. It takes into account both the leverage and residual of each observation. Cook’s Distance is a summary of how much a regression model changes when the i-th observation is removed.
References
Molnar G, Savage SJ, Sicker DC (2019) High-speed Internet access and housing values. Appl Econ 51(55):5923–5936
Klein GJ (2022) Fiber-broadband-internet and its regional impact—an empirical investigation. Telecommun Policy 46(5):102–331
Conley KL, Whitacre BE (2020) Home is where the internet is? High-speed internet’s impact on rural housing values. Int Reg Sci Rev 43(5):501–530
Deller S, Whitacre BE (2019) Broadband’s relationship to rural housing values. Pap Reg Sci 98(5):2135–2156
Gu XS, Rosenbaum PR (1993) Comparison of multivariate matching methods: structures, distances, and algorithms. J Comput Graph Stat 2(4):405–420
Abadie A, Imbens GW (2011) Bias-corrected matching estimators for average treatment effects. J Bus Econ Stat 29(1):1–11
Stuart EA, Huskamp HA, Duckworth K, Simmons J, Song Z, Chernew ME, Barry CL (2014) Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health Serv Outcomes Res Method 14(4):166–218
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Charankevich, H., Goldstein, J., Halder, A., Pender, J. (2023). Measuring Efficacy of the Rural Broadband Initiatives: Evidence from the Housing Market. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_30
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DOI: https://doi.org/10.1007/978-981-99-3243-6_30
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