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Large-Load Electricity Customers and Spillovers on Household Prices: Evidence from U.S. Data Centers

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  • Scalera, Cameron
  • Bosetti, Valentina
  • Pecci, Filippo

Abstract

We study how energy-intensive businesses affect local retail electricity prices for different classes of customers. We analyze the case of data centers in the United States. Data center development accelerated sharply after the arrival of artificial intelligence (AI). We leverage this national surge in data center investment during the post-AI period, map facilities to utility retail territories, and implement a shift-share design based on pre-AI fiber infrastructure. A standard deviation of ten data centers raises electricity prices by 13%, and a single hyperscale facility increases prices by 4%. Households experience a greater price increase compared to business customers. Demand-driven channels seem to be an important mechanism, and we find suggestive evidence that grid spending also plays a role. Nonprofit utilities, energy surplus, and retail competition may mitigate price impacts, while renewable energy does not appear to make inflation worse. Our results provide novel insights about electricity price spillovers from large individual customers and the market features that mediate this relationship.

Suggested Citation

  • Scalera, Cameron & Bosetti, Valentina & Pecci, Filippo, 2026. "Large-Load Electricity Customers and Spillovers on Household Prices: Evidence from U.S. Data Centers," CEPR Discussion Papers 21854, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:21854
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