meta data for this page
Shocks sheet
Covid mortality impact
Description: Increase in deaths by sex and cohort, compared to 2019.
Unit: rate
Source code: -
Source data link(s) and access dates:
- https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12ag.px/ Date accessed: 2024-12-04
- 12ag – Deaths by age (1-year) and sex, 1980-2023
File: ‘Mortality by sex and cohort 1980-2024 001_12ag_2023_20241204-153452.xlsx’
Note:
- Did not separate the influence of Covid19 from other factors. However, it was the main reason for changing mortalities and continues to affect mortality in Finland: https://stat.fi/julkaisu/cl8mlgiehwn8z0cvzmey6j7sr
- Strictly speaking, this was total changes in mortality rates including the impact of covid.
- The data shows some interesting patterns for different groups (2019 = 1).
- The mortality rate in comparison to 2019 was especially high for males and females over 65 in 2022 and in 2023
| Year | Total | 0-14 | 15-24 | 25-44 | 45-64 | 65- | |
|---|---|---|---|---|---|---|---|
| 2018 | Total | 1.0107 | 1.0242 | 0.8671 | 0.9191 | 1.1026 | 1.0025 |
| Males | 1.0038 | 0.9783 | 0.9343 | 0.9093 | 1.1035 | 0.9907 | |
| Females | 1.0177 | 1.0822 | 0.7282 | 0.9432 | 1.1008 | 1.0132 | |
| 2019 | Total | 1 | 1 | 1 | 1 | 1 | 1 |
| Males | 1 | 1 | 1 | 1 | 1 | 1 | |
| Females | 1 | 1 | 1 | 1 | 1 | 1 | |
| 2020 | Total | 1.0285 | 0.8606 | 0.9620 | 1.0065 | 1.0399 | 1.0287 |
| Males | 1.0326 | 0.9348 | 1.0423 | 1.0161 | 1.0675 | 1.0274 | |
| Females | 1.0244 | 0.7671 | 0.7961 | 0.9830 | 0.9852 | 1.0299 | |
| 2021 | Total | 1.0688 | 0.9697 | 0.8576 | 0.9101 | 1.0365 | 1.0788 |
| Males | 1.0766 | 0.8804 | 0.9249 | 0.8932 | 1.0420 | 1.0923 | |
| Females | 1.0609 | 1.0822 | 0.7184 | 0.9517 | 1.0256 | 1.0666 | |
| 2022 | Total | 1.1718 | 1.0182 | 0.8734 | 0.9485 | 1.0377 | 1.1972 |
| Males | 1.1716 | 1.0326 | 0.9061 | 0.9127 | 1.0335 | 1.2094 | |
| Females | 1.1720 | 1.0000 | 0.8058 | 1.0369 | 1.0460 | 1.1861 | |
| 2023 | Total | 1.1370 | 0.9576 | 1.0728 | 0.9747 | 0.9923 | 1.1605 |
| Males | 1.1496 | 0.9022 | 1.0845 | 0.9610 | 0.9938 | 1.1862 | |
| Females | 1.1243 | 1.0274 | 1.0485 | 1.0085 | 0.9893 | 1.1374 |
- Covid mortality impact data from 2020-2023 added to the model (previous one only had 2020 and 2021 for Italy). The ‘mortality rate cg’ equation was likewise adjusted.
Economic shocks: Imports, Exports, Consumption, Investment
Description: Exogenous negative shocks to consumption, investments, the demand for exports, and imports, caused by the Covid-19 pandemic and war in Ukraine, which led to sanctions against Russia.
Unit: Changes in volume, % turned into decimal fractions
Source code: -
Source data link(s) and access dates:
- https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__vtp/statfin_vtp_pxt_11sf.px/ Date accessed: 2025-05-31
File: ‘Economic shocks 1975-2024 (imports exports consumption GFCF) 001_11sf_2024_20250531-113103.xlsx’
Notes:
- Can use the following variables:
- P7R Imports of goods and services, income
- P6K Exports of goods and services, expenditure
- P3K Consumption expenditure, expenditure
- P51K Gross fixed capital formation, expenditure
- Why is the shock considered without industry disaggregation?
- More info of Covid effects in Finland: https://www.stat.fi/ajk/koronavirus/koronavirus-ajankohtaista-tilastotietoa/miten-vaikutukset-nakyvat-tilastoissa/talouden-tilannekuva#kuluttajat
- Eurogreen assumed the shock for the years 2020 and 2021. The covid shock was based on the variations of consumption, investments, exports and imports during 2020 and 2021.
- Eurogreen: The covid shock is based on the Bank of Italy’s projections for the variations of consumption, investments, exports and imports, available at: https://www.bancaditalia.it/pubblicazioni/proiezioni-macroeconomiche/2020/Proiezioni-Macroeconomiche-Italia-dicembre-2020.pdf.pdf
- Ukraine war economic shock also relates to the energy module à something to consider later.
- For industry disaggregation for trade impacts, see 14mc -- International trade in services, trade value by activity sector, enterprise size class and region, annually, 2019-2023
Economic shocks: LFPR
Description: Change in the Labour Force Participation Rate (15-64y) compared to previous year, during 2020-2021.
Unit: rate
Source code: -
Source data link(s) and access dates:
- 2009-2023: https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__tyti/statfin_tyti_pxt_13aj.px/ Date accessed: 2024-06-09
- 1989-2020: https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin_Passiivi/StatFin_Passiivi__tyti/statfinpas_tyti_pxt_11pl_2020.px/ Date accessed: 2024-06-09
Notes:
- Vensim comment: “DATA FROM ILO, labor force participation rate, 15-64y”, “Assuming complete recover of labor force participation rate to pre-pandemic level 2022, 15-64y: 0.0192; 1,92%”
- According to Finnish data, the labour force was already higher in 2021 than in 2019, with only a minor dip in 2020 (-0.0014) followed by two years of unusually high increases in participation rates, despite stable population.
LFPR gs shock
Description: Change in the Labour Force Participation Rate compared to previous year, during 2019-2022, per gender and skill
Unit: rate
Source code: -
Source data link(s) and access dates:
- https://ec.europa.eu/eurostat/databrowser/view/lfsa_argaedn__custom_19723649/default/table Date accessed: 2026-10-05
Notes:
- Note: Same data source as LFPR gs
Total price shock
Description: How much prices changed in each sector compared to the previous year by comparing value of output with and without the effect of inflation.
Unit: percent
Source code: -
Source data link(s) and access dates:
- https://ec.europa.eu/eurostat/databrowser/view/nama_10_a64__custom_16906482/default/table?lang=en Date accessed: 2025-05-17
File: ‘price-shocks-fi-2010-2023’
Notes:
- Used R code to extract and process the data.
- Also downloaded data with only value added ‘GVA by industry 2010-2023 Eurostat nama_10_a64__custom_16742335_spreadsheet.xlsx’
- Vensim comment: “price shocks computed for the year 2022 (Eurostat data on Value added)”
- The total price shocks for 2022 and 2023 were calculated by deriving implicit price deflators from Eurostat Gross Value Added (GVA) data by NACE industry. Specifically, they likely compared GVA at current prices with GVA at constant prices (previous year price, chain-linked volumes) to isolate the price component for each sector and then expressed the result as a proportional or percentage change. Therefore, Price Shock = (GVAcurrent/GVAconstant)−1
- Explanation is our best guess, waiting confirmation from Guilherme.
- TODO: Need to switch to the new approach of shocking mark-ups instead of prices
- Guilherme: “We realized that a second strategy could be more flexible and useful for the model structure. Rather than giving a shock on prices, we decided to shock the markups. (check the variable target markup i). You can simply compute how much the necessary shock would be, inverting the price-markup formula. (That is, using mark-up as the unknown as the price as the known variable in the price equation, to get the desired mark-up after the shock as the solution). We also included a time of convergence of the targeted mark-up, to avoid a sharp reduction of prices in the year after the shock. You basically just need to replicate the structure in the Sub-module mark-up in the price view of the updated version of the model. We put a 5 year period of convergence, but this can be calibrated in the end.”



