It is estimated that over one-fourth of US households experienced a power outage in 2023, costing on average US $150 Bn annually, with 87% of outages caused by natural hazards. Indeed, numerous studies have examined the macroeconomic impact of power network interruptions, employing a wide variety of modeling methods and data parameterization techniques, which warrants further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard-induced US power outages: Hurricane Ian (2022), the 2021 Texas Blackouts, and Tropical Storm Isaias (2020). Our analysis evaluates the sensitivity of three commonly used data parameterization techniques (household interruptions, kWh lost, and satellite luminosity), along with three static models (Leontief and Ghosh, critical input, and inoperability Input-Output). We find the mean domestic loss estimates to be US $3.13 Bn, US $4.18 Bn, and US $2.93 Bn, respectively. Additionally, data parameterization techniques can alter estimated losses by up to 23.1% and 50.5%. Consistent with the wide range of outputs, we find that the GDP losses are highly sensitive to model architecture, data parameterization, and analyst assumptions. Results sensitivity is not uniform across models and arises from important a priori analyst decisions, demonstrated by data parameterization techniques yielding 11% and 45% differences within a model. We find that the numerical value output is more sensitive than intersectoral linkages and other macroeconomic insights. To our knowledge, we contribute to literature the first systematic comparison of multiple IO models and parameterizations across several natural hazard-induced long-duration power outages, providing guidance and insights for analysts.

More than one-fifth of the US population does not subscribe to a fixed broadband service despite broadband being a recognized merit good. For example, less than 4% of citizens earning more than US $70k annually do not have broadband, compared to 26% of those earning below US $20k annually. To address this, the federal government has undertaken one of the largest broadband investment programs ever via The Bipartisan Infrastructure Law, with the aim of addressing this disparity and expanding broadband connectivity to all citizens. We examine broadband availability, adoption, and need for each US state, and then construct an Input-Output model to explore the potential structural macroeconomic impacts of broadband spending on Gross Domestic Product (GDP) and supply chain linkages. In terms of macroeconomic impact, the total potential indirect contribution to US GDP by the program could be as high as US $84.8 billion, $32.7 billion, and $9.78 billion for the Broadband Equity, Access, and Deployment program, the Affordable Connectivity Program, and additional programs, respectively. Thus, overall, the broadband allocations could expand US GDP by up to US $127.3 billion (0.10% of annual US GDP over the next five years). Moreover, the broadband packages within the Bipartisan Infrastructure Law could create up to 230,000 jobs (0.14% labor market increase). We contribute one of the first economic impact assessments of the US Bipartisan Infrastructure Law to the literature

This project explores the trading behavior of a simple market with both explicit and implicit inflation. In both cases, both cost-pull and demand-push inflation are at play, but in one case, sellers are naturally responding to changes in cost and demand, and in the other, sellers are pushed towards the market equilibrium through a "media" warning them about impending inflation.