Authors:
(1) Matthew Sprintson
(2) Edward Oughton Table of Links Abstract and Introduction 2. Literature Review 2.1 Reviewing Broadband Infrastructure’s Impact on the Economy 2.2 Previous Research into IO Modeling of Broadband Investment 2.3 Context of the Bipartisan Infrastructure Act through Previous Research 3. Methods and 3.1 Leontief Input-Output (IO) Modeling 3.2 Ghosh Supply-Side Assessment Methods for Infrastructure 3.3 Data and Application 4. Results and 4.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 4.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 4.3 How are the supply chain linkages affected by allocations from the Bipartisan Infrastructure Law? 5. Discussion 5.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 5.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 5.3 How are supply chain linkages affected by allocations from the Bipartisan Infrastructure Law?" Conclusion Acknowledgements and References 5 Discussion In this section, a discussion will be undertaken where the previously articulated results are evaluated within the broader context of the research questions. 5.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? Generally, most states are organized inversely between BEAD Allocation and the supply of existing broadband infrastructure, indicating that the broadband investment programs in the Bipartisan Infrastructure Law allocate funding effectively, targeting communities without broadband supply or those with a significant price barrier. Indeed, our analysis finds that regions with poorer broadband infrastructure receive commensurate broadband investment via the programs available, generally suggesting the allocated capital is likely to go where there is a clear need (as opposed to politically motivated capital allocation). For example, states with lower-than-average ACP enrollment see a greater BEAD allocation, and this may be explained by the fact that households in poorly connected areas have less reason to enroll in a substandard service. Equally, areas with higher-than-average ACP enrollment see fewer BEAD resources allocated. While this pattern is consistent, there is one extreme outlier whereby the BEAD program intends to spend almost US $40k to connect each Alaskan household to broadband. Alaska adheres to the general trend we established in section 4.1, whereby only about 7.2% of Alaskan homes have enrolled in the ACP, which could suggest fewer households have broadband access. Due to the low population density in Alaska, deploying broadband infrastructure will take significantly more time and investment (Espín & Rojas, 2024). Instead of connecting to existing broadband networks within the continental United States, Alaskan providers often need to lay large quantities of greenfield infrastructure over long distances within the Alaskan wilderness. To put this investment into perspective, US $38,802 is more than half of the median household income (56%) in the United States (US $69,600) (Guzman, 2022). Connecting Alaskan households to fixed broadband rather than exploring satellite broadband access could prove costly. An Alaskan community with ten unconnected homes could receive an average of roughly US $390,000 to connect to wireline broadband, broadly equating with hiring a teacher for five years (US Bureau of Labor Statistics, 2022) or establishing scholarships for about 50 students to attend the University of Alaska (University of Alaska, 2024). Another outlying case is Texas, where a significant rural population lowers its per-capita allocation relative to other rural states. Even though Texas will receive the largest grant by far, each unconnected household will only see about US $2-3k to connect to broadband. This per-capita allocation is lower than average, and the ACP connectivity (13.8% enrollment) is approximately average. The discrepancy could result from the high number of unconnected households in Texas - which means the high allocation disperses over a greater number of houses than in other states. Additionally, the spatial distribution of homes in Texas is likely to be denser than in Alaska, and most homes will be closer to an urban area or major highway with substantial local or long-distance fiber infrastructure to connect to. These conditions make it less costly to connect Texan households to the state’s broadband network. Some states, like Ohio and New York, have high ACP enrollment and low BEAD allocations, indicating that a significant barrier to broadband adoption is potentially from lower-income households being unable to afford the service (rather than a lack of existing infrastructure). ACP enrollment in Louisiana is encouraging and shows considerably higher registration than in other states. For example, over 26% of households are enrolled in the program, compared to the nationwide average of 13.3%. Similarly, Ohio (20.7%), Kentucky (22.6%), and New Mexico (20.2%) stand out as states that have succeeded in efforts to ensure that their communities connect to the ACP. States with very high enrollment levels should be concerned as to whether this program may secure longer-term funding into the future, beyond the current administration. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Matthew Sprintson (2) Edward Oughton Authors: Authors: (1) Matthew Sprintson (2) Edward Oughton Table of Links Abstract and Introduction Abstract and Introduction 2. Literature Review 2.1 Reviewing Broadband Infrastructure’s Impact on the Economy 2.1 Reviewing Broadband Infrastructure’s Impact on the Economy 2.2 Previous Research into IO Modeling of Broadband Investment 2.2 Previous Research into IO Modeling of Broadband Investment 2.3 Context of the Bipartisan Infrastructure Act through Previous Research 2.3 Context of the Bipartisan Infrastructure Act through Previous Research 3. Methods and 3.1 Leontief Input-Output (IO) Modeling 3. Methods and 3.1 Leontief Input-Output (IO) Modeling 3.2 Ghosh Supply-Side Assessment Methods for Infrastructure 3.2 Ghosh Supply-Side Assessment Methods for Infrastructure 3.3 Data and Application 3.3 Data and Application 4. Results and 4.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 4. Results and 4.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 4.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 4.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 4.3 How are the supply chain linkages affected by allocations from the Bipartisan Infrastructure Law? 4.3 How are the supply chain linkages affected by allocations from the Bipartisan Infrastructure Law? 5. Discussion 5.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 5.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? 5.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 5.2 What are the GDP impacts of the three funding programs within the Bipartisan Infrastructure Law? 5.3 How are supply chain linkages affected by allocations from the Bipartisan Infrastructure Law?" 5.3 How are supply chain linkages affected by allocations from the Bipartisan Infrastructure Law?" Conclusion Conclusion Acknowledgements and References Acknowledgements and References 5 Discussion In this section, a discussion will be undertaken where the previously articulated results are evaluated within the broader context of the research questions. 5.1 To what extent does the Bipartisan Infrastructure Law allocate funding to unconnected communities in need? Generally, most states are organized inversely between BEAD Allocation and the supply of existing broadband infrastructure, indicating that the broadband investment programs in the Bipartisan Infrastructure Law allocate funding effectively, targeting communities without broadband supply or those with a significant price barrier. Indeed, our analysis finds that regions with poorer broadband infrastructure receive commensurate broadband investment via the programs available, generally suggesting the allocated capital is likely to go where there is a clear need (as opposed to politically motivated capital allocation). For example, states with lower-than-average ACP enrollment see a greater BEAD allocation, and this may be explained by the fact that households in poorly connected areas have less reason to enroll in a substandard service. Equally, areas with higher-than-average ACP enrollment see fewer BEAD resources allocated. While this pattern is consistent, there is one extreme outlier whereby the BEAD program intends to spend almost US $40k to connect each Alaskan household to broadband. Alaska adheres to the general trend we established in section 4.1, whereby only about 7.2% of Alaskan homes have enrolled in the ACP, which could suggest fewer households have broadband access. Due to the low population density in Alaska, deploying broadband infrastructure will take significantly more time and investment (Espín & Rojas, 2024). Instead of connecting to existing broadband networks within the continental United States, Alaskan providers often need to lay large quantities of greenfield infrastructure over long distances within the Alaskan wilderness. To put this investment into perspective, US $38,802 is more than half of the median household income (56%) in the United States (US $69,600) (Guzman, 2022). Connecting Alaskan households to fixed broadband rather than exploring satellite broadband access could prove costly. An Alaskan community with ten unconnected homes could receive an average of roughly US $390,000 to connect to wireline broadband, broadly equating with hiring a teacher for five years (US Bureau of Labor Statistics, 2022) or establishing scholarships for about 50 students to attend the University of Alaska (University of Alaska, 2024). Another outlying case is Texas, where a significant rural population lowers its per-capita allocation relative to other rural states. Even though Texas will receive the largest grant by far, each unconnected household will only see about US $2-3k to connect to broadband. This per-capita allocation is lower than average, and the ACP connectivity (13.8% enrollment) is approximately average. The discrepancy could result from the high number of unconnected households in Texas - which means the high allocation disperses over a greater number of houses than in other states. Additionally, the spatial distribution of homes in Texas is likely to be denser than in Alaska, and most homes will be closer to an urban area or major highway with substantial local or long-distance fiber infrastructure to connect to. These conditions make it less costly to connect Texan households to the state’s broadband network. Some states, like Ohio and New York, have high ACP enrollment and low BEAD allocations, indicating that a significant barrier to broadband adoption is potentially from lower-income households being unable to afford the service (rather than a lack of existing infrastructure). ACP enrollment in Louisiana is encouraging and shows considerably higher registration than in other states. For example, over 26% of households are enrolled in the program, compared to the nationwide average of 13.3%. Similarly, Ohio (20.7%), Kentucky (22.6%), and New Mexico (20.2%) stand out as states that have succeeded in efforts to ensure that their communities connect to the ACP. States with very high enrollment levels should be concerned as to whether this program may secure longer-term funding into the future, beyond the current administration. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

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The Economic Ripple Effect of Broadband Investments

How Are Supply Chain Linkages Affected by Allocations From the Bipartisan Infrastructure Law?

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Dissecting the Allocation Patterns of the Bipartisan Infrastructure Law

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