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Tracking the economy during the Covid-19 pandemic: the contribution of high-frequency indicators
[Le suivi économique en période de Covid-19 : l’apport d’indicateurs à haute fréquence]

Author

Listed:
  • Bricongne Jean-Charles
  • Coffinet Jérôme
  • Delbos Jean-Brieux
  • Kaiser Vojtech
  • Kien Jean-Noël
  • Kintzler Étienne
  • Lestrade Ariane
  • Meunier Baptiste
  • Mouliom Michel
  • Nicolas Théo

Abstract

The Covid-19 crisis is not only a shock to public health, it has also triggered major and brutal upheavals on an economic and social level. To supplement the standard measures for tracking the economy, based on monthly or quarterly monetary, financial and business-related statistics, economists have turned to alternative indicators derived from so-called “open” data (pollution, electricity consumption, Google Trends, Twitter). This additional information has made it possible to evaluate levels of household confidence, and to measure new behaviours as well as the economic impact of the shock, especially where official data were not available. As part of its tracking of the Covid-19 crisis and notably the lockdown, the Banque de France has designed a series of dashboards incorporating indicators derived from open data. This article describes the main ones used. La crise de la Covid 19 constitue non seulement un choc sanitaire mais aussi une rupture économique et sociale majeure et brutale. Les mesures usuelles de suivi économique, fondées sur les statistiques monétaires, financières et d’entreprises, trimestrielles ou mensuelles, ont pu être complétées par des indicateurs alternatifs issus de données dites « ouvertes » (pollution, consommation d’électricité, Google Trends, Twitter). Cet apport a permis d’évaluer la confiance des ménages, ainsi que de mesurer leurs comportements nouveaux et l’impact économique du choc, notamment lorsque les données officielles n’étaient pas disponibles. Dans le cadre du suivi de la crise de la Covid 19, et notamment du confinement, la Banque de France a conçu des tableaux de bord réguliers qui rassemblent les indicateurs issus des données ouvertes. Cet article propose d’en présenter les principaux.

Suggested Citation

  • Bricongne Jean-Charles & Coffinet Jérôme & Delbos Jean-Brieux & Kaiser Vojtech & Kien Jean-Noël & Kintzler Étienne & Lestrade Ariane & Meunier Baptiste & Mouliom Michel & Nicolas Théo, 2020. "Tracking the economy during the Covid-19 pandemic: the contribution of high-frequency indicators [Le suivi économique en période de Covid-19 : l’apport d’indicateurs à haute fréquence]," Bulletin de la Banque de France, Banque de France, issue 231.
  • Handle: RePEc:bfr:bullbf:2020:231:05
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    Cited by:

    1. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    2. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    3. Alexandre Aspremont & Simon Ben Arous & Jean-Charles Bricongne & Benjamin Lietti & Baptiste Meunier, 2023. "Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks," Working papers 916, Banque de France.

    More about this item

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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