📊 insee: Add France data from INSEE for Chartbook of Economic Inequality

[paarriagadap]

Issue https://github.com/owid/owid-issues/issues/1442

[owidbot]

Quick links (staging server):

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Login: ssh owid@staging-site-chartbook-france-insee

chart-diff: ✅

No charts for review.

data-diff: ❌ Found differences

+ Dataset garden/chartbook/2024-04-22/concialdi
+ + Table concialdi
+   + Column gini
+ Dataset garden/chartbook/2024-05-23/wealth_france
+ + Table wealth_france
+   + Column p90p100_share
+   + Column p99p100_share
+   + Column p99_9p100_share
+ Dataset garden/insee/2024-03-21/inequality_france_1999
+ + Table inequality_france_1999
+   + Column gini
+ Dataset garden/insee/2024-04-05/inequality_france
+ + Table inequality_france
+   + Column gini
+ Dataset garden/insee/2024-04-25/insee_premiere_1875
+ + Table insee_premiere_1875
+   + Column p90_p10_ratio
+   + Column s80_s20_ratio
+   + Column gini
+   + Column headcount_50_median
+   + Column headcount_60_median
+   + Column headcount_ratio_50_median
+   + Column headcount_ratio_60_median
+   + Column income_gap_ratio_50_median
+   + Column income_gap_ratio_60_median
+ Dataset garden/insee/2024-04-26/relative_poverty_france
+ + Table relative_poverty_france
+   + Column headcount_ratio_40_median
+   + Column headcount_ratio_50_median
+   + Column headcount_ratio_60_median
+   + Column headcount_ratio_70_median
+ Dataset garden/insee/2024-05-23/interdecile_ratio
+ + Table interdecile_ratio
+   + Column p90_p50_ratio
+ Dataset garden/insee/2024-05-23/interdecile_ratio_2022
+ + Table interdecile_ratio_2022
+   + Column p90_p50_ratio
~ Dataset garden/neglected_tropical_diseases/2024-05-18/funding
-   -   The data collected in this survey has been used to create a unique repository of investment data, providing an unmatched resource for policy-makers, donors, researchers and industry. The database outlines the long-term landscape of funding for R&D for global health priority areas, including where funding gaps exist and how single investments fit into the global picture.
+   +   The data collected in this survey has been used to create a unique repository of investment data, providing an unmatched resource for policy-makers, donors, researchers and industry. The database outlines the long-term landscape of funding for R&D for global health priority areas, including where funding gaps exist and how single investments fit into the global picture. This dataset covers the majority of neglected disease, emerging infectious disease, and sexual & reproductive health R&D funding is captured by G-FINDER, because large funders active in this area and target groups identified by our Advisory Committee are typically responsive and, where they are not, are prioritised during survey follow-up.
+   + 
+   +   The dataset covers  major funding for: Neglected Diseases: Bacterial pneumonia & meningitis, Buruli ulcer, Chagas disease, Cryptococcal meningitis, Dengue, Diarrhoeal diseases, Helminth infections (worms & flukes), Hepatitis B, Hepatitis C, Histoplasmosis, HIV/AIDS, Kinetoplastid diseases, Leprosy, Leptospirosis, Malaria, Mycetoma, Rheumatic fever, Salmonella infections, Scabies, Snakebite envenoming, Trachoma, Tuberculosis, Yaws. Emerging Infectious Diseases: Arenaviral haemorrhagic fevers (including Lassa fever), Bunyaviral diseases (including CCHF, RVF, SFTS), Chikungunya, Coronaviral diseases (including MERS, SARS, COVID-19), Emergent non-polio enteroviruses (including EV71, D68), Filoviral diseases (including Ebola, Marburg), Henipaviral diseases (including Nipah), Mpox (monkeypox), Zika. Sexual & Reproductive Health: Contraception, HIV/AIDS, HPV and HPV-related cervical cancer, Multi-purpose prevention technologies, Post-partum haemorrhage, Pre-eclampsia and eclampsia, Sexually transmitted infections.
  = Table funding_disease_product
  = Table funding_product
+ + Table funding_product_ntd
+   + Column amount__usd
  = Table funding_disease
+ Dataset garden/unu_wider/2024-04-22/world_income_inequality_database
+ + Table world_income_inequality_database
+   + Column gini


Legend: +New  ~Modified  -Removed  =Identical  Details
Hint: Run this locally with etl diff REMOTE data/ --include yourdataset --verbose --snippet

Automatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile are not included

Edited: 2024-06-04 21:46:02 UTC Execution time: 4.11 seconds

[lucasrodes]

Started looking at this.

Datasets to review

• [x] inequality_france_1999
• [x] insee_premiere_1875
• [x] inequality_france
• [x] relative_poverty_france
• [x] interdecile_ratio_2022
• [x] interdecile_ratio
• [x] concialdi
• [x] world_income_inequality_database
• [x] wealth_france

[paarriagadap]

Thank you very much, @lucasrodes!