Equitable Funding Pre- and Post-IIJA in the RAISE Program: A Cognitive Time Series Analysis

Equitable Funding Pre- and Post-IIJA in the RAISE Program: A Cognitive Time Series Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Equitable Funding Pre- and Post-IIJA in the RAISE Program: A Cognitive Time Series Analysis Qadri H Shaheen, Qingbin Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5337986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Since the enactment of the Infrastructure Investment and Jobs Act (IIJA), also known as the Bipartisan Infrastructure Law (BIL), in 2021, there has been a notable shift in the distribution of federal funding between rural and urban areas through the RAISE grants (formerly known as TIGER grants). An analysis of grant allocations from 2009 to 2024 reveals an evolving trend in equity considerations within funding decisions. Until 2017, urban areas consistently received the majority of funding; however, there was a significant increase in rural allocations thereafter. This shift reflects a growing emphasis on addressing the infrastructure needs of historically underserved rural communities, a critical equity concerns in national transportation policy. The IIJA authorizes $1.2 trillion for transportation and infrastructure spending, with $550 billion allocated to “new” investments and programs. Notably, rural and urban areas achieved equal shares of funding allocations (50%) for the first time, marking a pivotal moment in federal infrastructure funding equity. This research employs a financial and equity-oriented framework to evaluate the longitudinal trends of RAISE funding allocations. With rural areas receiving over 53% of allocations annually since the IIJA's implementation, the study explores the financial implications of this shift including the distribution of benefits, burdens, and opportunities for rural communities. The findings demonstrate a significant transformation in funding distribution following the IIJA and a marked departure from pre-IIJA patterns. The paper offers actionable recommendations for future infrastructure investments aimed at enhancing both financial sustainability and equitable outcomes. Policymakers must balance infrastructure development with equity considerations, ensuring that disadvantaged communities receive their fair share of benefits. Scientific community and society/Scientific community/Policy Scientific community and society/Business and industry/Engineering Policy integration Longitudinal analysis Equitable funding Figures Figure 1 Figure 2 Introduction Transportation infrastructure is fundamental to the economic and social well-being of regions, providing access to critical services such as healthcare, education, and employment. Historically, urban centers in the United States have disproportionately benefited from federal infrastructure funding, leaving rural areas underfunded and marginalized. This disparity has contributed to significant gaps in transportation access, which in turn have exacerbated existing economic and social inequalities. Policy interventions like the Infrastructure Investment and Jobs Act (IIJA) aim to address these imbalances, seeking to distribute transportation resources more equitably between urban and rural areas (Committee for a Responsible Federal Budget, 2021). The grant program “Rebuilding American Infrastructure with Sustainability and Equity (RAISE)”, plays a central role in this effort. Initially established in response to the 2008 global financial crisis, the TIGER (Transportation Investment Generating Economic Recovery) program was designed to revitalize transportation infrastructure nationwide by basing funding decisions on merit rather than geographic location. Despite these efforts, disparities between rural and urban areas persisted, particularly in terms of both funding allocation and infrastructure quality. Recognizing this, the IIJA introduced new equity mandates aimed at ensuring that historically disadvantaged communities—whether urban or rural—receive a fair share of federal infrastructure funding (Suárez-Cuesta et al., 2024) This paper investigates how the IIJA has impacted the allocation of RAISE funding between rural and urban areas from Fiscal Year (FY) 2009 to FY 2024. Difference-in-Differences (DiD) has become one of the most popular research designs used to evaluate causal effects of policy interventions (Callaway and Sant’Anna, 2021 ). Employing a DiD, this analysis assesses whether the IIJA has shifted funding patterns in favor of rural areas, which have historically been underfunded. Findings reveal a significant change in funding distribution post-FY 2021, with rural areas receiving over 50% of RAISE funding—a notable departure from earlier years when urban areas consistently dominated allocations. The Act’s focus on equitable distribution of federal resources across rural and urban areas has contributed to rethinking the approach towards infrastructure investments, emphasizing both efficiency and fairness. Rudik et al. ( 2024 ) further explore the IIJA’s adaptation investments, discussing how the Act balances equity and efficiency in delivering its goals. Giang et al. ( 2024 ) highlight opportunities for integrating equity in sustainability science, demonstrating that modeling can be used to improve decision-making processes that prioritize equitable outcomes, thus contributing to more effective infrastructure adaptation strategies. The issue of transportation equity has garnered increasing attention among both scholars and policymakers. This research contributes to the growing body of literature on transportation equity by providing a comprehensive, longitudinal analysis of federal infrastructure funding trends. It offers valuable policy insights, particularly regarding the equitable distribution of future infrastructure investments to meet the needs of both urban and rural communities. Transportation equity refers to the fair allocation of resources to ensure all population groups, especially those historically underserved, benefit equitably from access to essential services like healthcare, education, and employment. Pereira and Karner ( 2021 ), argue that transportation equity encompasses more than just an even distribution of funds. It requires that investments directly address the unique needs of disadvantaged communities, whether urban or rural. Historically, federal transportation funding in the United States has favored urban centers, perceived as the nation’s economic engines. Rural areas, by contrast, have often been left with insufficient infrastructure development, stifling both economic growth and social mobility. Thomopoulos et al. ( 2009 ) emphasizes the critical role of equitable transportation policies, noting that infrastructure inequalities can amplify broader socio-economic disparities, particularly in rural regions. These disparities have prompted policymakers to reconsider how federal resources are allocated, especially in response to increasing demands for social justice and sustainability. The programs were originally designed to allocate funds based on merit, but urban areas still received the majority of resources until FY 2017. Bills and Walker ( 2017 ) point out that focusing on averages can obscure the underlying disparities faced by disadvantaged communities. Their analysis of the 2000 Bay Area Travel Survey illustrates that distributional comparisons, rather than average measures, are crucial in revealing the true "winners and losers" from infrastructure investments. The Justice-40 Initiative, which mandates that 40% of the benefits from federal investments flow to underserved communities (The White House, 2021 ), is closely aligned with the goals of the RAISE program. Pereira et al. ( 2016 ) further elaborate on the complexities of distributive justice in transportation equity, emphasizing that policies should not only focus on the efficient allocation of resources but also consider the social and environmental impacts of infrastructure investments. Their work underscores the importance of transportation infrastructure in enhancing social and economic mobility, particularly in disadvantaged communities, and stresses the need to involve these communities in the decision-making process. Karner, et al. ( 2020 ) argue for a shift from the concept of transportation equity to a broader framework of transportation justice. This perspective advocates for aligning transportation planning with social change models, drawing from environmental justice literature and movements that challenge state-sponsored approaches to addressing inequities. Their work emphasizes the importance of reevaluating current methods and suggests that transportation justice offers a more comprehensive lens for addressing systemic issues within infrastructure and transportation planning. To pave the pathway in this equity direction, Litman ( 2022 ) provided essential guidance on evaluating transportation equity, recommending that distributional impacts be incorporated into policy planning. This involves assessing not only how resources are distributed geographically but also how different population groups are affected, especially those historically marginalized. While the IIJA represents a significant step toward integrating equity into federal transportation policy, its success depends on consistent monitoring and evaluation to ensure that benefits reach the communities most in need. By using the DiD methodology, to investigate what hidden information and patterns are inside the data, this research offers a robust framework for evaluating the IIJA's impact on funding disparities. DiD is particularly useful for comparing treated and control groups before and after an intervention, isolating the policy’s effects from other factors. The standard difference-in-differences research design examines the differences in outcomes between a treated group and an untreated group (first difference), both before and after the treatment is introduced (second difference) (Callaway, 2024). Cognitive analysis, considering time series, is an advanced methodology to analyze data, other than traditional quantitive and qualitive analysis (Udin, 2022). With DiD being a longitudinal time series analysis. This study analyzes funding allocations from FY 2009 to FY 2024, collected from federal data sources from the U.S. Department of Transportation (USDOT, 2024), offering a cognitive detailed examination of whether the IIJA has shifted funding patterns in favor of underserved rural areas. The IIJA and related initiatives, such as Justice-40, mark a significant departure from traditional transportation funding practices by prioritizing equity in infrastructure planning. This literature review highlights the importance of addressing geographic and socio-economic disparities in transportation investments, especially in historically underserved rural areas. This research adds to the growing body of work on transportation equity by examining the longitudinal impact of the IIJA on rural and urban funding disparities, offering critical insights for policymakers focused on creating a more equitable transportation system. Results The study utilizes the DiD model that is tested and applied in economic and policy integration (Uddin, 2022) to examine the effects of the post-2021 intervention on the distribution of federal funding between rural and urban areas. The model's coefficients and variables (Callaway and Sant’Anna, 2021 ) and statistical significance provide valuable insights into funding trends over the specified period. The intercept (β0) estimates the baseline funding for urban areas before the intervention at $ 462,941,943, which is statistically significant (p < 0.001). This suggests that, on average, urban areas received approximately $ 463 million in federal funding annually prior to the intervention. The time variable (β1 = 450,662,865) captures the post-intervention effect on urban funding. The positive coefficient signifies a statistically significant increase of $ 450,662,865 in urban funding allocations (p = 0.0121), indicating that the intervention had a substantial positive impact on urban areas. In contrast, the GroupBinary coefficient (β2 = − 183,131,712) represents the difference in pre-intervention funding between rural and urban areas, showing that rural areas received $ 183,131,712 less in funding before the intervention. However, this difference was not statistically significant (p = 0.1341), indicating no meaningful disparity in funding between rural and urban areas at that time. The interaction term (β3 = 179,506,483) reveals the DiD effect, suggesting an additional increase of $ 179,506,483 in rural funding compared to urban funding post-intervention. However, the p-value of 0.4559 indicates that this increase is not statistically significant, implying that the intervention DiD not create a significant differential effect favoring rural areas. Model Significance The model demonstrates moderate explanatory power, with an R-squared value of 0.4521, indicating that 45.21% of the variation in funding can be explained by the model. The overall model is statistically significant, as the F-statistic (7.702, p < 0.001) suggests that the predictors—Time, GroupBinary, and Interaction—account for a notable portion of the variation in funding. The residual standard error of $ 290,800,000 implies that the model's predictions deviate from actual funding amounts by approximately $ 290 million, indicating some unexplained variation that may be addressed with additional covariates or alternative models. Assessment of Pre-IIJA Funding Correlation The relationship between funding and three distinct indicators is examined. These are the total number of applications received, the expansion of NHS lane mileage, and the proportion of Historically Disadvantaged Communities (HDCs) relative to the total population. The data was analyzed using scatter plots to explore correlations with linear trend lines included where and as appropriate to highlight patterns in the relationships. Each graph aims to uncover insights into the effectiveness of funding in stimulating participation, improving infrastructure and promoting equity. The graphs collectively reveal different dynamics of how funding allocation impacts participation, infrastructure growth, and equitable benefits distribution. The strong positive correlation between funding levels and the number of applications, suggesting that higher financial availability encourages increased participation. This indicates that funding acts as a significant motivator for applicants, providing insights for policymakers on how financial incentives can drive engagement. The strong positive relationship between funding and the expansion of NHS lane mileage suggests that increased funding is directly tied to enhanced infrastructure development, emphasizing its critical role in improving highway connectivity and transportation efficiency. The graphs highlight that funding is a powerful tool for stimulating engagement and driving tangible infrastructure improvements. The relationship between funding and the proportion of HDCs served, presents a more dispersed pattern with no clear trend. This lack of consistent correlation suggests that, despite increased funding; the benefits do not necessarily reach HDCs equitably indicating that current funding strategies may fall short in promoting equitable outcomes. The above insight points to the need for targeted approaches to ensure that financial resources effectively address equity goals, emphasizing that merely increasing funding is insufficient for achieving fair distribution. Overall, increased funding correlates with higher participation and improved infrastructure, ensuring equitable access to these benefits requires deliberate and focused strategies that specifically address the unique needs of disadvantaged communities. Discussions The analysis of pre-IIJA funding trends, combined with the DiD model results, provide a holistic understanding of the impact of federal funding on infrastructure development, participation, and equity, both before and after the 2021 intervention. Participation and Application Trends The pre-IIJA scatter plot demonstrates a strong positive correlation between total funding amounts and the number of applications, suggesting that higher funding availability encourages greater participation. The trend highlights that financial incentives play a significant role in motivating applicants, as seen by the near-perfect correlation (R² = 0.9857). This finding is echoed in the DiD results, which show a significant increase in funding for urban areas post-intervention, with a positive effect of $450,662,865 (p = 0.0121). The increase in urban funding post-2021 implies that the intervention further stimulated participation in these areas, supporting the broader finding that adequate financial resources are a key driver of engagement. However, the rural funding increase, though nominal ($179,506,483), was not statistically significant, indicating that rural participation may have been less responsive to the post-2021 intervention. Infrastructure Growth Both the pre-IIJA and DiD analyses highlight the centrality of federal funding in driving infrastructure development. The pre-IIJA correlation between funding and NHS lane mileage shows a strong positive relationship (R² = 0.8738), meaning that greater funding directly correlates with the expansion of the National Highway System. This supports the notion that federal investments are vital for increasing transportation connectivity and enhancing highway infrastructure. The DiD results reinforce this conclusion by indicating a substantial increase in urban infrastructure funding following the 2021 intervention, as evidenced by the significant rise in overall allocations. However, the statistically insignificant rural increase suggests that, despite funding availability, rural infrastructure growth may not have benefited equally from the intervention, aligning with the need for targeted investments in underdeveloped regions. Equity and Disadvantaged Communities The lack of a clear correlation between federal funding and the proportion of Historically Disadvantaged Communities served in the pre-IIJA period reflects persistent equity challenges. The scattered pattern of data indicates that, despite increased funding, the benefits DiD not consistently reach disadvantaged communities. This outcome highlights that existing funding mechanisms may not adequately prioritize equity objectives. In the DiD results, the insignificant difference in rural funding further underscores the gap in equitable distribution, as rural areas (which often encompass many disadvantaged communities) DiD not receive a statistically meaningful boost in funding compared to urban areas. This finding points to the continued need for deliberate policy interventions that focus on equity in infrastructure development. Without specific measures aimed at HDCs and rural communities, federal funding risks perpetuating existing disparities. Overall Impact and Future Recommendations The combined insights from the pre-IIJA and DiD analyses illustrate the significant role of federal funding in stimulating participation and enhancing infrastructure growth but also reveal that equity challenges remain. Post-2021 intervention efforts have successfully increased overall funding, especially in urban areas, driving meaningful engagement and infrastructure expansion. However, the lack of significant rural funding increases and the dispersed impact on HDCs suggest that more focused strategies are necessary to ensure that funding reaches disadvantaged populations. To advance the Justice-40 merits and equity objectives, federal funding policies shall continue to provide robust financial resources to encourage participation and infrastructure development. Additionally, implement targeted funding mechanisms aimed at rural regions and HDCs to address the inequities highlighted in both the pre- and post-2021 data. Lastly, strengthen monitoring and evaluation frameworks to ensure that equity goals are being met in future infrastructure programs. In conclusion, while increased funding has had a positive impact on participation and infrastructure growth, the analysis shows that equitable distribution of resources remains an area for improvement. Policymakers must adopt more nuanced approaches that balance infrastructure development with equity considerations, ensuring that disadvantaged communities receive their fair share of benefits from federal funding initiatives. Methods and Materials Data Collection The data used in this study spans from Fiscal Year (FY) 2009 to FY 2024 and includes federal funding distribution for both rural and urban regions. The data was sourced from federal published records (U.S. Department of Transportation, 2024 ), with a focus on transportation and infrastructure. The funding amounts for each region were extracted, categorized, and verified for accuracy. For consistency, regions were classified based on the U.S. Census Bureau’s designations of "rural" and "urban." The intervention being studied was implemented in FY 2021, and the purpose of this analysis is to assess its impact on funding allocation between rural and urban regions. This intervention aimed to address equity in federal funding, with a particular focus on increasing support for rural areas. The pre-intervention period is defined as FY 2009 to FY 2020, and the post-intervention period as FY 2021 to FY 2024. Difference-in-Differences (DiD) To quantify the effects of the intervention, we applied a DiD analysis. This method allows us to compare the changes in funding allocations between rural and urban areas before and after the intervention, controlling for time trends and pre-existing differences between the two groups. The DiD model is particularly effective for isolating the impact of the intervention as it accounts for external factors that may influence funding trends over time. The model is structured as follows: Y = β0 + β1[Time]+β2[GroupBinary]+β3[Time∗GroupBinary]+β4[Covariates]+ϵ …………………………………… (1) Where: YY represents the federal funding allocation; β0​ is the intercept, representing the baseline funding for urban areas pre-intervention; β1​ measures the effect of time, capturing the funding changes for urban areas post-intervention; β2​ measures the difference in pre-intervention funding between rural and urban areas; β3​ is the interaction term that captures the DiD effect, representing the additional change in rural funding relative to urban funding post-intervention; ϵ represents the error term. (Callaway and Sant’Anna, 2021 ) The key outcome of interest is the interaction term, β3​, which indicates whether the post-2021 intervention led to a differential increase in funding for rural areas compared to urban areas. Model Specifications and Covariates The covariates included in the model control for various factors that might influence funding distributions, such as the size of the population, economic indicators, and the proportion of National Highway System (NHS) lane mileage within each region. Additionally, the analysis controls for the number of HDCs in each area, reflecting the federal commitment to equity. Statistical Analysis All data processing and statistical analyses were conducted in R. The primary focus was on the interaction term, β3​, which indicates the differential impact of the intervention on rural funding compared to urban funding. Model’s fit was assessed using R-squared values and residual standard errors, while the significance of each coefficient was determined using p-values. Visual Analysis In addition to the DiD analysis, scatter plots were created to examine the relationship between funding and three key indicators: the total number of applications, the expansion of NHS lane mileage, and the proportion of HDCs relative to the total population. These scatter plots include linear trend lines to highlight patterns in the relationships. The graphs were analyzed to assess whether increased funding correlates with higher participation, improved infrastructure, and equitable distribution of resources. The trends observed in these graphs were cross-referenced with the results of the DiD analysis to draw broader conclusions about the impact of the intervention. Assumptions and Limitations Several assumptions are inherent in the DiD model used in this study. The first assumption is that the time trends for rural and urban regions would have remained parallel in the absence of the intervention. This means that the change in rural funding would have mirrored the change in urban funding. Additionally, it is assumed that no other significant policy changes or external factors influenced the funding distribution during the study period. Limitations of the study include potential unobserved variables that could affect funding allocations but were not captured in the model, as well as the relatively short post-intervention period (FY 2021 to FY 2024). This may limit the ability to detect longer-term trends. Future research could address these limitations by incorporating additional covariates and extending the study period as more data becomes available. Declarations Author Contribution Q.C. designed research; Q.H.S. collected and analyzed data. Q.H.S wrote the main manuscript text. All authors reviewed the manuscript. Data Availability This study obtained research data from publicly available online repositories. Here is the link to the data: https://www.transportation.gov/policy-initiatives/build/awards-2009-2024 References Bills, T. S., & Walker, J. L. (2017). Looking beyond the mean for equity analysis: Examining distributional impacts of transportation improvements. Transport Policy, 54, 61-69. https://doi.org/10.1016/j.tranpol.2016.08.003 Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230. Callaway, B., Goodman-Bacon, A., & Sant'Anna, P. H. (2024). Difference-in-differences with a continuous treatment (No. w32117). National Bureau of Economic Research. https://doi.org/10.3386/w32117 Federal Highway Administration (FHWA). (2021). Is federal infrastructure investment advancing equity goals? FHWA. https://www.urban.org Giang, A., Edwards, M. R., Fletcher, S. M., Gardner-Frolick, R., Gryba, R., Mathias, J. D., ... & Tessum, C. W. (2024). Equity and modeling in sustainability science: Examples and opportunities throughout the process. Proceedings of the National Academy of Sciences, 121(13), e2215688121. https://doi.org/10.1073/pnas.2215688121 Karner, A., London, J., Rowangould, D., & Manaugh, K. (2020). From Transportation Equity to Transportation Justice: Within, Through, and Beyond the State. Journal of Planning Literature, 35(4), 440-459. https://doi.org/10.1177/0885412220927691 Litman, T. M. (2022). Evaluating transportation equity: Guidance for incorporating distributional impacts in transport planning. Institute of Transportation Engineers. ITE Journal, 92(4), 43-49. https://www.vtpi.org/equity.pdf Pereira, R. H., & Karner, A. (2021). Transportation equity (pp. 271-277). Elsevier. http://dx.doi.org/10.1016/B978-0-08-102671-7.10053-3 Pereira, R. H. M., Schwanen, T., & Banister, D. (2016). Distributive justice and equity in transportation. Transport Reviews, 37(2), 170–191. https://doi.org/10.1080/01441647.2016.1257660 Public Spending on Transportation and Water Infrastructure Committee For A Responsible Federal Budget, U. S. (2021) What's in the Bipartisan Infrastructure Investment and Jobs Act? United States Posted: 1956 Rudik, I., Lemoine, D., & Marcheva, A. (2024). Equity and efficiency in the Bipartisan Infrastructure Law’s adaptation investments. In Environmental and Energy Policy and the Economy (Vol. 6). University of Chicago Press. Suárez-Cuesta, D., & Latorre, M. C. (2024). Macro and Microeconomic Effects of the Infrastructure Investment and Jobs Act (Iija) and How Financing Strategies Matter. http://dx.doi.org/10.2139/ssrn.4821146 The White House. (2021). The Biden administration releases the Bipartisan Infrastructure Law guidebook for state, local, tribal, and territorial governments. https://www.whitehouse.gov/wp-content/uploads/2022/05/BUILDING-A-BETTER-AMERICA-V2.pdf Thomopoulos, N., Grant-Muller, S., & Tight, M. R. (2009). Incorporating equity considerations in transport infrastructure evaluation: Current practice and a proposed methodology. Evaluation and program planning, 32(4), 351-359. https://doi.org/10.1016/j.evalprogplan.2009.06.013 Uddin, S., Ong, S., & Lu, H. (2022). Machine learning in project analytics: a data-driven framework and case study. Scientific Reports, 12(1), 15252. https://doi.org/10.1038/s41598-022-19728-x U.S. Department of Transportation. (2024). BUILD discretionary grant awards 2009-2024. U.S. Department of Transportation. Retrieved [Sept 2024], from https://www.transportation.gov/policy-initiatives/build/awards-2009-2024 Additional Declarations No competing interests reported. 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Historically, urban centers in the United States have disproportionately benefited from federal infrastructure funding, leaving rural areas underfunded and marginalized. This disparity has contributed to significant gaps in transportation access, which in turn have exacerbated existing economic and social inequalities. Policy interventions like the Infrastructure Investment and Jobs Act (IIJA) aim to address these imbalances, seeking to distribute transportation resources more equitably between urban and rural areas (Committee for a Responsible Federal Budget, 2021).\u003c/p\u003e \u003cp\u003eThe grant program \u0026ldquo;Rebuilding American Infrastructure with Sustainability and Equity (RAISE)\u0026rdquo;, plays a central role in this effort. Initially established in response to the 2008 global financial crisis, the TIGER (Transportation Investment Generating Economic Recovery) program was designed to revitalize transportation infrastructure nationwide by basing funding decisions on merit rather than geographic location. Despite these efforts, disparities between rural and urban areas persisted, particularly in terms of both funding allocation and infrastructure quality. Recognizing this, the IIJA introduced new equity mandates aimed at ensuring that historically disadvantaged communities\u0026mdash;whether urban or rural\u0026mdash;receive a fair share of federal infrastructure funding (Su\u0026aacute;rez-Cuesta et al., 2024)\u003c/p\u003e \u003cp\u003eThis paper investigates how the IIJA has impacted the allocation of RAISE funding between rural and urban areas from Fiscal Year (FY) 2009 to FY 2024. Difference-in-Differences (DiD) has become one of the most popular research designs used to evaluate causal effects of policy interventions (Callaway and Sant\u0026rsquo;Anna, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Employing a DiD, this analysis assesses whether the IIJA has shifted funding patterns in favor of rural areas, which have historically been underfunded. Findings reveal a significant change in funding distribution post-FY 2021, with rural areas receiving over 50% of RAISE funding\u0026mdash;a notable departure from earlier years when urban areas consistently dominated allocations. The Act\u0026rsquo;s focus on equitable distribution of federal resources across rural and urban areas has contributed to rethinking the approach towards infrastructure investments, emphasizing both efficiency and fairness. Rudik et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further explore the IIJA\u0026rsquo;s adaptation investments, discussing how the Act balances equity and efficiency in delivering its goals. Giang et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight opportunities for integrating equity in sustainability science, demonstrating that modeling can be used to improve decision-making processes that prioritize equitable outcomes, thus contributing to more effective infrastructure adaptation strategies.\u003c/p\u003e \u003cp\u003eThe issue of transportation equity has garnered increasing attention among both scholars and policymakers. This research contributes to the growing body of literature on transportation equity by providing a comprehensive, longitudinal analysis of federal infrastructure funding trends. It offers valuable policy insights, particularly regarding the equitable distribution of future infrastructure investments to meet the needs of both urban and rural communities. Transportation equity refers to the fair allocation of resources to ensure all population groups, especially those historically underserved, benefit equitably from access to essential services like healthcare, education, and employment. Pereira and Karner (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), argue that transportation equity encompasses more than just an even distribution of funds. It requires that investments directly address the unique needs of disadvantaged communities, whether urban or rural.\u003c/p\u003e \u003cp\u003eHistorically, federal transportation funding in the United States has favored urban centers, perceived as the nation\u0026rsquo;s economic engines. Rural areas, by contrast, have often been left with insufficient infrastructure development, stifling both economic growth and social mobility. Thomopoulos et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) emphasizes the critical role of equitable transportation policies, noting that infrastructure inequalities can amplify broader socio-economic disparities, particularly in rural regions. These disparities have prompted policymakers to reconsider how federal resources are allocated, especially in response to increasing demands for social justice and sustainability. The programs were originally designed to allocate funds based on merit, but urban areas still received the majority of resources until FY 2017. Bills and Walker (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) point out that focusing on averages can obscure the underlying disparities faced by disadvantaged communities. Their analysis of the 2000 Bay Area Travel Survey illustrates that distributional comparisons, rather than average measures, are crucial in revealing the true \"winners and losers\" from infrastructure investments.\u003c/p\u003e \u003cp\u003eThe Justice-40 Initiative, which mandates that 40% of the benefits from federal investments flow to underserved communities (The White House, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), is closely aligned with the goals of the RAISE program. Pereira et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) further elaborate on the complexities of distributive justice in transportation equity, emphasizing that policies should not only focus on the efficient allocation of resources but also consider the social and environmental impacts of infrastructure investments. Their work underscores the importance of transportation infrastructure in enhancing social and economic mobility, particularly in disadvantaged communities, and stresses the need to involve these communities in the decision-making process. Karner, et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argue for a shift from the concept of transportation equity to a broader framework of transportation justice.\u003c/p\u003e \u003cp\u003eThis perspective advocates for aligning transportation planning with social change models, drawing from environmental justice literature and movements that challenge state-sponsored approaches to addressing inequities. Their work emphasizes the importance of reevaluating current methods and suggests that transportation justice offers a more comprehensive lens for addressing systemic issues within infrastructure and transportation planning. To pave the pathway in this equity direction, Litman (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provided essential guidance on evaluating transportation equity, recommending that distributional impacts be incorporated into policy planning. This involves assessing not only how resources are distributed geographically but also how different population groups are affected, especially those historically marginalized. While the IIJA represents a significant step toward integrating equity into federal transportation policy, its success depends on consistent monitoring and evaluation to ensure that benefits reach the communities most in need.\u003c/p\u003e \u003cp\u003eBy using the DiD methodology, to investigate what hidden information and patterns are inside the data, this research offers a robust framework for evaluating the IIJA's impact on funding disparities. DiD is particularly useful for comparing treated and control groups before and after an intervention, isolating the policy\u0026rsquo;s effects from other factors. The standard difference-in-differences research design examines the differences in outcomes between a treated group and an untreated group (first difference), both before and after the treatment is introduced (second difference) (Callaway, 2024).\u003c/p\u003e \u003cp\u003eCognitive analysis, considering time series, is an advanced methodology to analyze data, other than traditional quantitive and qualitive analysis (Udin, 2022). With DiD being a longitudinal time series analysis. This study analyzes funding allocations from FY 2009 to FY 2024, collected from federal data sources from the U.S. Department of Transportation (USDOT, 2024), offering a cognitive detailed examination of whether the IIJA has shifted funding patterns in favor of underserved rural areas.\u003c/p\u003e \u003cp\u003eThe IIJA and related initiatives, such as Justice-40, mark a significant departure from traditional transportation funding practices by prioritizing equity in infrastructure planning. This literature review highlights the importance of addressing geographic and socio-economic disparities in transportation investments, especially in historically underserved rural areas. This research adds to the growing body of work on transportation equity by examining the longitudinal impact of the IIJA on rural and urban funding disparities, offering critical insights for policymakers focused on creating a more equitable transportation system.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study utilizes the DiD model that is tested and applied in economic and policy integration (Uddin, 2022) to examine the effects of the post-2021 intervention on the distribution of federal funding between rural and urban areas. The model's coefficients and variables (Callaway and Sant\u0026rsquo;Anna, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and statistical significance provide valuable insights into funding trends over the specified period. The intercept (β0) estimates the baseline funding for urban areas before the intervention at \u003cspan\u003e$\u003c/span\u003e462,941,943, which is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that, on average, urban areas received approximately \u003cspan\u003e$\u003c/span\u003e463\u0026nbsp;million in federal funding annually prior to the intervention. The time variable (β1\u0026thinsp;=\u0026thinsp;450,662,865) captures the post-intervention effect on urban funding. The positive coefficient signifies a statistically significant increase of \u003cspan\u003e$\u003c/span\u003e450,662,865 in urban funding allocations (p\u0026thinsp;=\u0026thinsp;0.0121), indicating that the intervention had a substantial positive impact on urban areas. In contrast, the GroupBinary coefficient (β2\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;183,131,712) represents the difference in pre-intervention funding between rural and urban areas, showing that rural areas received \u003cspan\u003e$\u003c/span\u003e183,131,712 less in funding before the intervention. However, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.1341), indicating no meaningful disparity in funding between rural and urban areas at that time. The interaction term (β3\u0026thinsp;=\u0026thinsp;179,506,483) reveals the DiD effect, suggesting an additional increase of \u003cspan\u003e$\u003c/span\u003e179,506,483 in rural funding compared to urban funding post-intervention. However, the p-value of 0.4559 indicates that this increase is not statistically significant, implying that the intervention DiD not create a significant differential effect favoring rural areas.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModel Significance\u003c/h2\u003e \u003cp\u003eThe model demonstrates moderate explanatory power, with an R-squared value of 0.4521, indicating that 45.21% of the variation in funding can be explained by the model. The overall model is statistically significant, as the F-statistic (7.702, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggests that the predictors\u0026mdash;Time, GroupBinary, and Interaction\u0026mdash;account for a notable portion of the variation in funding. The residual standard error of \u003cspan\u003e$\u003c/span\u003e290,800,000 implies that the model's predictions deviate from actual funding amounts by approximately \u003cspan\u003e$\u003c/span\u003e290\u0026nbsp;million, indicating some unexplained variation that may be addressed with additional covariates or alternative models.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of Pre-IIJA Funding Correlation\u003c/h3\u003e\n\u003cp\u003eThe relationship between funding and three distinct indicators is examined. These are the total number of applications received, the expansion of NHS lane mileage, and the proportion of Historically Disadvantaged Communities (HDCs) relative to the total population. The data was analyzed using scatter plots to explore correlations with linear trend lines included where and as appropriate to highlight patterns in the relationships. Each graph aims to uncover insights into the effectiveness of funding in stimulating participation, improving infrastructure and promoting equity.\u003c/p\u003e \u003cp\u003eThe graphs collectively reveal different dynamics of how funding allocation impacts participation, infrastructure growth, and equitable benefits distribution. The strong positive correlation between funding levels and the number of applications, suggesting that higher financial availability encourages increased participation. This indicates that funding acts as a significant motivator for applicants, providing insights for policymakers on how financial incentives can drive engagement. The strong positive relationship between funding and the expansion of NHS lane mileage suggests that increased funding is directly tied to enhanced infrastructure development, emphasizing its critical role in improving highway connectivity and transportation efficiency. The graphs highlight that funding is a powerful tool for stimulating engagement and driving tangible infrastructure improvements. The relationship between funding and the proportion of HDCs served, presents a more dispersed pattern with no clear trend. This lack of consistent correlation suggests that, despite increased funding; the benefits do not necessarily reach HDCs equitably indicating that current funding strategies may fall short in promoting equitable outcomes.\u003c/p\u003e \u003cp\u003eThe above insight points to the need for targeted approaches to ensure that financial resources effectively address equity goals, emphasizing that merely increasing funding is insufficient for achieving fair distribution. Overall, increased funding correlates with higher participation and improved infrastructure, ensuring equitable access to these benefits requires deliberate and focused strategies that specifically address the unique needs of disadvantaged communities.\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe analysis of pre-IIJA funding trends, combined with the DiD model results, provide a holistic understanding of the impact of federal funding on infrastructure development, participation, and equity, both before and after the 2021 intervention.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eParticipation and Application Trends\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe pre-IIJA scatter plot demonstrates a strong positive correlation between total funding amounts and the number of applications, suggesting that higher funding availability encourages greater participation. The trend highlights that financial incentives play a significant role in motivating applicants, as seen by the near-perfect correlation (R\u0026sup2; = 0.9857). This finding is echoed in the DiD results, which show a significant increase in funding for urban areas post-intervention, with a positive effect of $450,662,865 (p = 0.0121).\u003c/p\u003e\n\u003cp\u003eThe increase in urban funding post-2021 implies that the intervention further stimulated participation in these areas, supporting the broader finding that adequate financial resources are a key driver of engagement. However, the rural funding increase, though nominal ($179,506,483), was not statistically significant, indicating that rural participation may have been less responsive to the post-2021 intervention.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u0026nbsp;Infrastructure Growth\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBoth the pre-IIJA and DiD analyses highlight the centrality of federal funding in driving infrastructure development. The pre-IIJA correlation between funding and NHS lane mileage shows a strong positive relationship (R\u0026sup2; = 0.8738), meaning that greater funding directly correlates with the expansion of the National Highway System. This supports the notion that federal investments are vital for increasing transportation connectivity and enhancing highway infrastructure.\u003c/p\u003e\n\u003cp\u003eThe DiD results reinforce this conclusion by indicating a substantial increase in urban infrastructure funding following the 2021 intervention, as evidenced by the significant rise in overall allocations. However, the statistically insignificant rural increase suggests that, despite funding availability, rural infrastructure growth may not have benefited equally from the intervention, aligning with the need for targeted investments in underdeveloped regions.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eEquity and Disadvantaged Communities\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe lack of a clear correlation between federal funding and the proportion of Historically Disadvantaged Communities served in the pre-IIJA period reflects persistent equity challenges. The scattered pattern of data indicates that, despite increased funding, the benefits DiD not consistently reach disadvantaged communities. This outcome highlights that existing funding mechanisms may not adequately prioritize equity objectives.\u003c/p\u003e\n\u003cp\u003eIn the DiD results, the insignificant difference in rural funding further underscores the gap in equitable distribution, as rural areas (which often encompass many disadvantaged communities) DiD not receive a statistically meaningful boost in funding compared to urban areas. This finding points to the continued need for deliberate policy interventions that focus on equity in infrastructure development. Without specific measures aimed at HDCs and rural communities, federal funding risks perpetuating existing disparities.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003eOverall Impact and Future Recommendations\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe combined insights from the pre-IIJA and DiD analyses illustrate the significant role of federal funding in stimulating participation and enhancing infrastructure growth but also reveal that equity challenges remain. Post-2021 intervention efforts have successfully increased overall funding, especially in urban areas, driving meaningful engagement and infrastructure expansion. However, the lack of significant rural funding increases and the dispersed impact on HDCs suggest that more focused strategies are necessary to ensure that funding reaches disadvantaged populations.\u003c/p\u003e\n\u003cp\u003eTo advance the Justice-40 merits and equity objectives, federal funding policies shall continue to provide robust financial resources to encourage participation and infrastructure development. Additionally, implement targeted funding mechanisms aimed at rural regions and HDCs to address the inequities highlighted in both the pre- and post-2021 data. Lastly, strengthen monitoring and evaluation frameworks to ensure that equity goals are being met in future infrastructure programs.\u003c/p\u003e\n\u003cp\u003eIn conclusion, while increased funding has had a positive impact on participation and infrastructure growth, the analysis shows that equitable distribution of resources remains an area for improvement. Policymakers must adopt more nuanced approaches that balance infrastructure development with equity considerations, ensuring that disadvantaged communities receive their fair share of benefits from federal funding initiatives.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003eThe data used in this study spans from Fiscal Year (FY) 2009 to FY 2024 and includes federal funding distribution for both rural and urban regions. The data was sourced from federal published records (U.S. Department of Transportation, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with a focus on transportation and infrastructure. The funding amounts for each region were extracted, categorized, and verified for accuracy. For consistency, regions were classified based on the U.S. Census Bureau\u0026rsquo;s designations of \"rural\" and \"urban.\"\u003c/p\u003e \u003cp\u003eThe intervention being studied was implemented in FY 2021, and the purpose of this analysis is to assess its impact on funding allocation between rural and urban regions. This intervention aimed to address equity in federal funding, with a particular focus on increasing support for rural areas. The pre-intervention period is defined as FY 2009 to FY 2020, and the post-intervention period as FY 2021 to FY 2024.\u003c/p\u003e \u003cp\u003eDifference-in-Differences (DiD)\u003c/p\u003e \u003cp\u003eTo quantify the effects of the intervention, we applied a DiD analysis. This method allows us to compare the changes in funding allocations between rural and urban areas before and after the intervention, controlling for time trends and pre-existing differences between the two groups. The DiD model is particularly effective for isolating the impact of the intervention as it accounts for external factors that may influence funding trends over time.\u003c/p\u003e \u003cp\u003eThe model is structured as follows:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1[Time]+β2[GroupBinary]+β3[Time\u0026lowast;GroupBinary]+β4[Covariates]+ϵ \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (1)\u003c/p\u003e \u003cp\u003eWhere: YY represents the federal funding allocation; β0​ is the intercept, representing the baseline funding for urban areas pre-intervention; β1​ measures the effect of time, capturing the funding changes for urban areas post-intervention; β2​ measures the difference in pre-intervention funding between rural and urban areas; β3​ is the interaction term that captures the DiD effect, representing the additional change in rural funding relative to urban funding post-intervention; ϵ represents the error term. (Callaway and Sant\u0026rsquo;Anna, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe key outcome of interest is the interaction term, β3​, which indicates whether the post-2021 intervention led to a differential increase in funding for rural areas compared to urban areas.\u003c/p\u003e \u003cp\u003eModel Specifications and Covariates\u003c/p\u003e \u003cp\u003eThe covariates included in the model control for various factors that might influence funding distributions, such as the size of the population, economic indicators, and the proportion of National Highway System (NHS) lane mileage within each region. Additionally, the analysis controls for the number of HDCs in each area, reflecting the federal commitment to equity.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data processing and statistical analyses were conducted in R. The primary focus was on the interaction term, β3​, which indicates the differential impact of the intervention on rural funding compared to urban funding. Model\u0026rsquo;s fit was assessed using R-squared values and residual standard errors, while the significance of each coefficient was determined using p-values.\u003c/p\u003e \u003cp\u003eVisual Analysis\u003c/p\u003e \u003cp\u003eIn addition to the DiD analysis, scatter plots were created to examine the relationship between funding and three key indicators: the total number of applications, the expansion of NHS lane mileage, and the proportion of HDCs relative to the total population. These scatter plots include linear trend lines to highlight patterns in the relationships. The graphs were analyzed to assess whether increased funding correlates with higher participation, improved infrastructure, and equitable distribution of resources. The trends observed in these graphs were cross-referenced with the results of the DiD analysis to draw broader conclusions about the impact of the intervention.\u003c/p\u003e \u003cp\u003eAssumptions and Limitations\u003c/p\u003e \u003cp\u003eSeveral assumptions are inherent in the DiD model used in this study. The first assumption is that the time trends for rural and urban regions would have remained parallel in the absence of the intervention. This means that the change in rural funding would have mirrored the change in urban funding. Additionally, it is assumed that no other significant policy changes or external factors influenced the funding distribution during the study period.\u003c/p\u003e \u003cp\u003eLimitations of the study include potential unobserved variables that could affect funding allocations but were not captured in the model, as well as the relatively short post-intervention period (FY 2021 to FY 2024). This may limit the ability to detect longer-term trends. Future research could address these limitations by incorporating additional covariates and extending the study period as more data becomes available.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.C. designed research; Q.H.S. collected and analyzed data. Q.H.S wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study obtained research data from publicly available online repositories. Here is the link to the data: https://www.transportation.gov/policy-initiatives/build/awards-2009-2024\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBills, T. S., \u0026amp; Walker, J. L. (2017). Looking beyond the mean for equity analysis: Examining distributional impacts of transportation improvements. Transport Policy, 54, 61-69. https://doi.org/10.1016/j.tranpol.2016.08.003\u003c/li\u003e\n\u003cli\u003eCallaway, B., \u0026amp; Sant\u0026rsquo;Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230.\u003c/li\u003e\n\u003cli\u003eCallaway, B., Goodman-Bacon, A., \u0026amp; Sant\u0026apos;Anna, P. H. (2024). Difference-in-differences with a continuous treatment (No. w32117). National Bureau of Economic Research. https://doi.org/10.3386/w32117\u003c/li\u003e\n\u003cli\u003eFederal Highway Administration (FHWA). (2021). Is federal infrastructure investment advancing equity goals? FHWA. https://www.urban.org\u003c/li\u003e\n\u003cli\u003eGiang, A., Edwards, M. R., Fletcher, S. M., Gardner-Frolick, R., Gryba, R., Mathias, J. D., ... \u0026amp; Tessum, C. W. (2024). Equity and modeling in sustainability science: Examples and opportunities throughout the process. Proceedings of the National Academy of Sciences, 121(13), e2215688121. https://doi.org/10.1073/pnas.2215688121\u003c/li\u003e\n\u003cli\u003eKarner, A., London, J., Rowangould, D., \u0026amp; Manaugh, K. (2020). From Transportation Equity to Transportation Justice: Within, Through, and Beyond the State. Journal of Planning Literature, 35(4), 440-459. https://doi.org/10.1177/0885412220927691\u003c/li\u003e\n\u003cli\u003eLitman, T. M. (2022). Evaluating transportation equity: Guidance for incorporating distributional impacts in transport planning. Institute of Transportation Engineers. ITE Journal, 92(4), 43-49. https://www.vtpi.org/equity.pdf\u003c/li\u003e\n\u003cli\u003ePereira, R. H., \u0026amp; Karner, A. (2021). Transportation equity (pp. 271-277). Elsevier. http://dx.doi.org/10.1016/B978-0-08-102671-7.10053-3\u003c/li\u003e\n\u003cli\u003ePereira, R. H. M., Schwanen, T., \u0026amp; Banister, D. (2016). Distributive justice and equity in transportation. Transport Reviews, 37(2), 170\u0026ndash;191. https://doi.org/10.1080/01441647.2016.1257660\u003c/li\u003e\n\u003cli\u003ePublic Spending on Transportation and Water Infrastructure Committee For A Responsible Federal Budget, U. S. (2021) What\u0026apos;s in the Bipartisan Infrastructure Investment and Jobs Act? United States Posted: 1956\u003c/li\u003e\n\u003cli\u003eRudik, I., Lemoine, D., \u0026amp; Marcheva, A. (2024). Equity and efficiency in the Bipartisan Infrastructure Law\u0026rsquo;s adaptation investments. In Environmental and Energy Policy and the Economy (Vol. 6). University of Chicago Press.\u003c/li\u003e\n\u003cli\u003eSu\u0026aacute;rez-Cuesta, D., \u0026amp; Latorre, M. C. (2024). Macro and Microeconomic Effects of the Infrastructure Investment and Jobs Act (Iija) and How Financing Strategies Matter. http://dx.doi.org/10.2139/ssrn.4821146\u003c/li\u003e\n\u003cli\u003eThe White House. (2021). The Biden administration releases the Bipartisan Infrastructure Law guidebook for state, local, tribal, and territorial governments. https://www.whitehouse.gov/wp-content/uploads/2022/05/BUILDING-A-BETTER-AMERICA-V2.pdf\u003c/li\u003e\n\u003cli\u003eThomopoulos, N., Grant-Muller, S., \u0026amp; Tight, M. R. (2009). Incorporating equity considerations in transport infrastructure evaluation: Current practice and a proposed methodology. Evaluation and program planning, 32(4), 351-359. https://doi.org/10.1016/j.evalprogplan.2009.06.013\u003c/li\u003e\n\u003cli\u003eUddin, S., Ong, S., \u0026amp; Lu, H. (2022). Machine learning in project analytics: a data-driven framework and case study. Scientific Reports, 12(1), 15252. https://doi.org/10.1038/s41598-022-19728-x\u003c/li\u003e\n\u003cli\u003eU.S. Department of Transportation. (2024). BUILD discretionary grant awards 2009-2024. U.S. Department of Transportation. Retrieved [Sept 2024], from https://www.transportation.gov/policy-initiatives/build/awards-2009-2024\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Policy integration, Longitudinal analysis, Equitable funding","lastPublishedDoi":"10.21203/rs.3.rs-5337986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5337986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSince the enactment of the Infrastructure Investment and Jobs Act (IIJA), also known as the Bipartisan Infrastructure Law (BIL), in 2021, there has been a notable shift in the distribution of federal funding between rural and urban areas through the RAISE grants (formerly known as TIGER grants). An analysis of grant allocations from 2009 to 2024 reveals an evolving trend in equity considerations within funding decisions. Until 2017, urban areas consistently received the majority of funding; however, there was a significant increase in rural allocations thereafter. This shift reflects a growing emphasis on addressing the infrastructure needs of historically underserved rural communities, a critical equity concerns in national transportation policy. The IIJA authorizes $1.2 trillion for transportation and infrastructure spending, with $550 billion allocated to “new” investments and programs. Notably, rural and urban areas achieved equal shares of funding allocations (50%) for the first time, marking a pivotal moment in federal infrastructure funding equity.\u003c/p\u003e\n\u003cp\u003eThis research employs a financial and equity-oriented framework to evaluate the longitudinal trends of RAISE funding allocations. With rural areas receiving over 53% of allocations annually since the IIJA's implementation, the study explores the financial implications of this shift including the distribution of benefits, burdens, and opportunities for rural communities.\u003c/p\u003e\n\u003cp\u003eThe findings demonstrate a significant transformation in funding distribution following the IIJA and a marked departure from pre-IIJA patterns. The paper offers actionable recommendations for future infrastructure investments aimed at enhancing both financial sustainability and equitable outcomes. Policymakers must balance infrastructure development with equity considerations, ensuring that disadvantaged communities receive their fair share of benefits.\u003c/p\u003e","manuscriptTitle":"Equitable Funding Pre- and Post-IIJA in the RAISE Program: A Cognitive Time Series Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 12:43:29","doi":"10.21203/rs.3.rs-5337986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9938e7e-ec10-4ae1-87a3-8b97b6911f40","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39846882,"name":"Scientific community and society/Scientific community/Policy"},{"id":39846883,"name":"Scientific community and society/Business and industry/Engineering"}],"tags":[],"updatedAt":"2024-12-08T16:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 12:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5337986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5337986","identity":"rs-5337986","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}