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

2.2 Previous Research into IO Modeling of Broadband Investment

Input-Output (IO) Modeling is a macroeconomic method developed by Wassily Leontief and awarded the Nobel Prize in Economic Sciences in 1973. The approach defines economies through a matrix where the rows represent sectoral outputs and the columns sectoral inputs. Assuming constant economic returns, one splits an economy into n sectors to analyze intersectional demand. The final economic output is represented in the dollar value of the goods and services that a sector produces. The value is calculated by summing the amount purchased as an input by other sectors of the economy and the amount purchased as a final good by consumers. When incorporating imports, government spending, value-added analysis, and other factors into separate rows and columns, it is possible to use this matrix to analyze the total output of an economy. When adjusting the inputs for each sector, it is possible to quantify the marginal effects of policy and investment either for a specific industry or the wider macroeconomy (Leontief, 1986).

Infrastructure investment impacts the economy through discrete sectoral spillovers (Schreiner & Madlener, 2022; Välilä, 2020). A quantifiable metric for identifying these spillovers is the analysis of the sectoral impacts on each other (Jimmy & Falianty, 2021). For example, the economic consequences of infrastructure development can be considered through the lens of Macroeconomic Growth Theory (Carlsson et al., 2013). Investment is represented within these models as structural changes to those sectoral effects by amplifying some sectors and damping others (Nieto et al., 2020; Sievers et al., 2019).

This approach has been used to quantitatively analyze broadband investment appropriations in the American Recovery and Reinvestment Act 2009. A measure of proportional growth can be captured for the interactions between industrial sectors resulting from additional investment (R. Katz & Suter, 2009). This enables direct, indirect, and induced effects to be quantified for the total output of each sector. One study finds the spillover effects of infrastructure projects and uses them to explore future economic outputs (Dimitriou et al., 2015). For example, business services were found to have a growth multiplier of 2.2, while post and telecommunications would potentially see a growth multiplier of 1.50. These figures represent how much these sectors’ output would benefit from the project construction. Additionally, one assessment uses an IO model to evaluate German broadband investment, finding that an investment of 36 billion (euros) could increase GDP by approximately 171 billion (euros) (R. L. Katz et al., 2010).

Government investment in broadband, Internet, and mobile networking infrastructure has been shown to have a range of positive economic growth impacts through GDP, labor, and other heuristics (Abrardi & Cambini, 2019; Kim et al., 2021). Contemporary studies have used IO models for similar investment analysis for different infrastructure systems (Appiah-Otoo & Song, 2021; Vu & Nguyen, 2024; Zhang et al., 2022). For example, investment impacts on regional economic sectors are estimated using an IO model for US energy infrastructure investment via supply and use tables from the Bureau of Economic Analysis and the IELab US Multi-Regional IO database (Faturay et al., 2020). Another study uses IO modeling to assess port infrastructure on other sectors of the Chinese economy. The methodology allowed the researchers to separate economic effects between industries, showing that the construction, chemical, and transportation sectors would be affected the most by port closures, with overall GDP reducing as much as US $90 billion (Wang & Wang, 2019).

These sources illustrate that IO modeling is an established approach for evaluating the macroeconomic impacts of infrastructure investment, especially when considering inter-industry linkages.