COVID-19, behaviour, policy and economy: Assessment on Trento and Bolzano using real-time high-frequency public data - Wessel Vermeulen

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Presentation by Wessel Vermeulen, Junior Economist, OECD Trento Centre at the 11th Spatial Productivity Lab meeting of the OECD Trento Centre held in virtual format on 10 December 2020. More OECD info: https://oe.cd/SPL

Government & Nonprofit

WESSEL VERMEULEN
Junior Economist, OECD Trento Centre
COVID-19, behaviour, policy and economy:
Assessment on Trento and Bolzano using real-time
high-frequency public data
10 December 2020 | 16.00-17.30 CET | WebinarRecent socioeconomic trends in Trentino and Alto-Adige/South Tyrol, Italy
#spatialproductivity

• Comprehensive
• Local
• High-frequency and
up-to-date
Comprehensive, local, and high-frequency Analysis
COVID-19
Policy
Behaviour
& Economy

• US: most developed. EU: spotty
• This presentation: Bolzano and Trento, already highlights
discrepancies in data release and structure.
Can we do this for sub-national economies?
Indicator Source Scope
COVID-19 case counts DPC provincial daily
Behaviour Google Mobility reports Provincial daily
Labour market BZ/TN provincial admin. Provincial daily/monthly
CIGS (furlough) INPS Provincial monthly
Nitro dioxide emissions EEA Provincial daily

Mobility patterns are equal,
COVID-19 case trends differ between provinces
Between provinces
• Cases: close but not
equal
• Behaviour: practically
identical
Between waves:
• different patterns on
cases - mobility

Bolzano: Daily employment and mobility
Indicators fall, but do not recover together

Sectoral employment for Bolzano indicates strong
variety in sectoral trends in 2020

Trento: Hiring response earlier and stronger.
• Feb – Mar: -50%
by April: -75%.
• Recovery by July.
• Agriculture least
affected
• Public sector biggest
negative and positive
swing.

Extensive use of furlough (CIGS) scheme especially
in sectors characterised by permanent contracts

Emissions follow mobility, but are a noisy measure
-5.4 to -16.6% -4.4 to -14%

• Economic consequences between wave 1 – 2, due to… (?)
– peoples behaviour
– government restrictions
• BZ and TN show similar broad patterns in labour market,
differ in sectoral reliance CIGS
• Real-time local economic analysis? Yes, for TN/BZ.
– Mobility and emissions vs observed employment
• Quick-Gain: harmonise sectoral breakdowns.
• Share widely: BZ experience of real-time web-app
Conclusion

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