Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts

Power outages are a substantial global issue across both advanced and developing countries, affecting economic productivity and growth. Consequently, numerous studies have examined the potential macroeconomic impacts of these disruptions, employing a wide variety of modeling methods and data parameterization techniques. A frequent approach is the use of input-output macroeconomic modeling, yet there is a lack of clarity about how ex ante parameterization and other methodological decisions affect output estimates, warranting further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard 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 for these three blackout events to be $2.42 Bn, $3.24 Bn, and $2.27 Bn, respectively. However, data parameterization techniques can alter estimated losses by up to 52.8% of the mean. Consistent with the wide range of outputs, we find that risk analysis stemming from gross output estimate severity is 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 up to 55.9% differences from empircal results within a model. To our knowledge, we contribute to the literature the first systematic comparison of multiple IO models and parameterizations across several natural hazard long-duration power outages, providing guidance and insights for analysts.