DP17200 The Distributional Impacts of Real-Time Pricing

Author(s): Michael Cahana, Natalia Fabra, Mar Reguant, Jingyuan Wang
Publication Date: April 2022
Date Revised: April 2022
Keyword(s): clustering, Distributional Effects, Dynamic pricing, electricity, Generalized method of moments
JEL(s): C33, H23, L94
Programme Areas: Industrial Organization
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=17200

We study the distributional impacts of real-time pricing (RTP) in the Spanish electricity market, where RTP was rolled out as the default tariff for a large share of residential customers. We complement aggregate patterns of distributional effects with a novel method for inferring individual households' income using zip code income distributions. We identify three channels for the distributional impacts of RTP: consumption profiles, appliance ownership, and locations. The first channel makes the switch from monthly to hourly prices progressive since high income households consume disproportionately more at peak times when real-time prices are higher. However, in the Spanish context, the other two channels make the switch from annual to monthly prices regressive. In particular, since low income households tend to have more electric heating, they benefit from the price insurance provided by time-invariant prices during winter, when prices are higher and more volatile. Given that price differences are greater across months than within months, the regressive effect dominates in our application. Using counterfactual experiments, we find that RTP makes low income households particularly vulnerable to adverse price shocks during winter. In the future, the wider adoption of enabling technologies (including storage and demand response devices) by the high income groups might worsen the distributional impacts of RTP. Our findings should allow to design an equitable real-time pricing system while retaining its efficiency properties.