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<p>We examine the linkages between power reliability, economic growth, and income inequality in the United States. Specifically, we use the two-step System Generalized Method of Moments (GMM) estimator to assess the impact of power interruptions on state-level GDP and the Gini Index. Our findings reveal that a 1 percent increase in power interruptions, measured in terms of duration (SAIDI) and frequency (SAIFI), is associated with a 0.07 to 3.7 percent decrease in real GDP and a modest increase in income inequality of approximately 0.17 to 0.20 percent relative to the mean Gini Index. Moreover, the marginal effects of power interruptions are substantial, with frequent outages resulting in GDP losses exceeding $2 trillion in the long run. We also use machine learning models to support the predictive relevance of the power reliability metrics. Overall, the results highlight the significant role that both the frequency and duration of power interruptions play in shaping regional economic performance and the importance of improving power reliability to foster economic stability and equity.</p>

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The distributional effects of power outages on regional economies: evidence from dynamic panel data and machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Sanjay Singh,
  • Zachary Keeler,
  • Bradley Ewing

摘要

We examine the linkages between power reliability, economic growth, and income inequality in the United States. Specifically, we use the two-step System Generalized Method of Moments (GMM) estimator to assess the impact of power interruptions on state-level GDP and the Gini Index. Our findings reveal that a 1 percent increase in power interruptions, measured in terms of duration (SAIDI) and frequency (SAIFI), is associated with a 0.07 to 3.7 percent decrease in real GDP and a modest increase in income inequality of approximately 0.17 to 0.20 percent relative to the mean Gini Index. Moreover, the marginal effects of power interruptions are substantial, with frequent outages resulting in GDP losses exceeding $2 trillion in the long run. We also use machine learning models to support the predictive relevance of the power reliability metrics. Overall, the results highlight the significant role that both the frequency and duration of power interruptions play in shaping regional economic performance and the importance of improving power reliability to foster economic stability and equity.