Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data
dc.contributor.author | Fezzi, C | |
dc.contributor.author | Fanghella, V | |
dc.date.accessioned | 2020-11-02T12:39:06Z | |
dc.date.issued | 2020-08-04 | |
dc.description.abstract | In response to the COVID-19 emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses’ shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedented disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impacts of COVID-19 on the economy, providing information that is essential for shaping future lockdown policy. Unlike official statistics, which are published with a delay of a few months, our approach permits almost real-time monitoring of the economic impact of the containment policies and the financial stimuli introduced to address the crisis. We illustrate our methodology using daily data for the Italian day-ahead power market. We estimate that the 3 weeks of most severe lockdown reduced the corresponding Italian Gross Domestic Product (GDP) by roughly 30%. Such negative impacts are now progressively declining but, at the end of June 2020, GDP is still about 8.5% lower than it would have been without the outbreak. | en_GB |
dc.description.sponsorship | Università degli Studi di Trento | en_GB |
dc.identifier.citation | Vol. 76, pp. 885 - 900 | en_GB |
dc.identifier.doi | 10.1007/s10640-020-00467-4 | |
dc.identifier.uri | http://hdl.handle.net/10871/123459 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © 2020, The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.subject | COVID-19 | en_GB |
dc.subject | Coronavirus | en_GB |
dc.subject | economic impacts | en_GB |
dc.subject | lockdown | en_GB |
dc.subject | GDP | en_GB |
dc.subject | Pandemic | en_GB |
dc.subject | Real-time monitoring | en_GB |
dc.subject | High-frequency estimates | en_GB |
dc.subject | Wholesale electricity markets | en_GB |
dc.subject | Electricity quantity | en_GB |
dc.subject | Fixed-effect regression | en_GB |
dc.title | Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-11-02T12:39:06Z | |
dc.identifier.issn | 0924-6460 | |
dc.description | This is the final version. Available from Springer via the DOI in this record. | en_GB |
dc.identifier.journal | Environmental and Resource Economics | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-07-09 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-07-09 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-11-02T12:03:32Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-11-02T12:39:12Z | |
refterms.panel | C | en_GB |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © 2020, The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.