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fingreen:input_data:inputseconomy:shocks [2026/10/30 13:48] – lesifingreen:input_data:inputseconomy:shocks [2026/10/30 14:02] (current) – copied from word documentation, still has to be finalised lesi
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-## Data title+## Economic shocks: LFPR
  
-**Description:** -+**Description:** Change in the Labour Force Participation Rate (15-64y) compared to previous year, during 2020-2021.  
  
-**Unit:** -+**Unit:** rate
  
 **Source code:** - **Source code:** -
  
 **Source data link(s) and access dates:** **Source data link(s) and access dates:**
-  - https://example.com/ Date accessed: 2026/10/30 12:08+  - 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.  
 + 
 +{{:fingreen:input_data:inputseconomy:lfpr_annual_rate_of_change.png?400|}} 
 + 
 +{{:fingreen:input_data:inputseconomy:lfpr_annual_rate_of_change_2.png?400|}} 
 + 
 +## 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.”