Name: French Employment, Salaries, Population Per Town
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

French Employment, Salaries, Population Per Town

Context

INSEE is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density...), Economic (Salary, Firms by activity / size...) and more.
It can be a great help to observe and measure inequality in the french population.

Content

Four files are in the dataset :

base_etablissement_par_tranche_effectif : give information on the number of firms in every french town, categorized by size , come from INSEE.
- CODGEO : geographique code for the town (can be joined with code_insee column from "name_geographic_information.csv')
- LIBGEO : name of the town (in french)
- REG : region number
- DEP : depatment number
- E14TST : total number of firms in the town
- E14TS0ND : number of unknown or null size firms in the town
- E14TS1 : number of firms with 1 to 5 employees in the town
- E14TS6 : number of firms with 6 to 9 employees in the town
- E14TS10 : number of firms with 10 to 19 employees in the town
- E14TS20 : number of firms with 20 to 49 employees in the town
- E14TS50 : number of firms with 50 to 99 employees in the town
- E14TS100 : number of firms with 100 to 199 employees in the town
- E14TS200 : number of firms with 200 to 499 employees in the town
- E14TS500 : number of firms with more than 500 employees in the town
name_geographic_information : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )
- EU_circo : name of the European Union Circonscription
- code_région : code of the region attached to the town
- nom_région : name of the region attached to the town
- chef.lieu_région : name the administrative center around the town
- numéro_département : code of the department attached to the town
- nom_département : name of the department attached to the town
- préfecture : name of the local administrative division around the town
- numéro_circonscription : number of the circumpscription
- nom_commune : name of the town
- codes_postaux : post-codes relative to the town
- code_insee : unique code for the town
- latitude : GPS latitude
- longitude : GPS longitude
- éloignement : i couldn't manage to figure out what was the meaning of this number
net_salary_per_town_per_category : salaries around french town per job categories, age and sex
- CODGEO : unique code of the town
- LIBGEO : name of the town
- SNHM14 : mean net salary
- SNHMC14 : mean net salary per hour for executive
- SNHMP14 : mean net salary per hour for middle manager
- SNHME14 : mean net salary per hour for employee
- SNHMO14 : mean net salary per hour for worker
- SNHMF14 : mean net salary for women
- SNHMFC14 : mean net salary per hour for feminin executive
- SNHMFP14 : mean net salary per hour for feminin middle manager
- SNHMFE14 : mean net salary per hour for feminin employee
- SNHMFO14 : mean net salary per hour for feminin worker
- SNHMH14 : mean net salary for man
- SNHMHC14 : mean net salary per hour for masculin executive
- SNHMHP14 : mean net salary per hour for masculin middle manager
- SNHMHE14 : mean net salary per hour for masculin employee
- SNHMHO14 : mean net salary per hour for masculin worker
- SNHM1814 : mean net salary per hour for 18-25 years old
- SNHM2614 : mean net salary per hour for 26-50 years old
- SNHM5014 : mean net salary per hour for >50 years old
- SNHMF1814 : mean net salary per hour for women between 18-25 years old
- SNHMF2614 : mean net salary per hour for women between 26-50 years old
- SNHMF5014 : mean net salary per hour for women >50 years old
- SNHMH1814 : mean net salary per hour for men between 18-25 years old
- SNHMH2614 : mean net salary per hour for men between 26-50 years old
- SNHMH5014 : mean net salary per hour for men >50 years old
population : demographic information in France per town, age, sex and living mode
- NIVGEO : geographic level (arrondissement, communes...)
- CODGEO : unique code for the town
- LIBGEO : name of the town (might contain some utf-8 errors, this information has better quality name_geographic_information)
- MOCO : cohabitation mode : [list and meaning available in Data description]
- AGE80_17 : age category (slice of 5 years) | ex : 0 -> people between 0 and 4 years old
- SEXE : sex, 1 for men | 2 for women
- NB : Number of people in the category
departments.geojson : contains the borders of french departments. From Gregoire David (github)

These datasets can be merged by : CODGEO = code_insee

Acknowledgements

The entire dataset has been created (and actualized) by INSEE, I just uploaded it on Kaggle after doing some jobs and checks on it. I haven't seen INSEE on Kaggle yet but I think it would be great to bring the organization in as a Kaggle actor.

Inspiration

First aim I had creating that dataset was to provide a map of french towns with the number of firm that are settled in by size.
Now my goal is to explore inequality between men and women, youngsters and elders, working / social classes.
Population can also be a great filter to explain some phenomenons on the maps.

Tables

Base Etablissement Par Tranche Effectif

@kaggle.etiennelq_french_employment_by_town.base_etablissement_par_tranche_effectif

888.54 KB
36681 rows
14 columns


CREATE TABLE base_etablissement_par_tranche_effectif (
  "codgeo" VARCHAR,
  "libgeo" VARCHAR,
  "reg" BIGINT,
  "dep" VARCHAR,
  "e14tst" BIGINT,
  "e14ts0nd" BIGINT,
  "e14ts1" BIGINT,
  "e14ts6" BIGINT,
  "e14ts10" BIGINT,
  "e14ts20" BIGINT,
  "e14ts50" BIGINT,
  "e14ts100" BIGINT,
  "e14ts200" BIGINT,
  "e14ts500" BIGINT
);

Name Geographic Information

@kaggle.etiennelq_french_employment_by_town.name_geographic_information

871.29 KB
36840 rows
14 columns


CREATE TABLE name_geographic_information (
  "eu_circo" VARCHAR,
  "code_r_gion" BIGINT,
  "nom_r_gion" VARCHAR,
  "chef_lieu_r_gion" VARCHAR,
  "num_ro_d_partement" VARCHAR,
  "nom_d_partement" VARCHAR,
  "pr_fecture" VARCHAR,
  "num_ro_circonscription" BIGINT,
  "nom_commune" VARCHAR,
  "codes_postaux" VARCHAR,
  "code_insee" BIGINT,
  "latitude" DOUBLE,
  "longitude" VARCHAR,
  "n__loignement" DOUBLE
);

Net Salary Per Town Categories

@kaggle.etiennelq_french_employment_by_town.net_salary_per_town_categories

231.56 KB
5136 rows
26 columns


CREATE TABLE net_salary_per_town_categories (
  "codgeo" VARCHAR,
  "libgeo" VARCHAR,
  "snhm14" DOUBLE,
  "snhmc14" DOUBLE,
  "snhmp14" DOUBLE,
  "snhme14" DOUBLE,
  "snhmo14" DOUBLE,
  "snhmf14" DOUBLE,
  "snhmfc14" DOUBLE,
  "snhmfp14" DOUBLE,
  "snhmfe14" DOUBLE,
  "snhmfo14" DOUBLE,
  "snhmh14" DOUBLE,
  "snhmhc14" DOUBLE,
  "snhmhp14" DOUBLE,
  "snhmhe14" DOUBLE,
  "snhmho14" DOUBLE,
  "snhm1814" DOUBLE,
  "snhm2614" DOUBLE,
  "snhm5014" DOUBLE,
  "snhmf1814" DOUBLE,
  "snhmf2614" DOUBLE,
  "snhmf5014" DOUBLE,
  "snhmh1814" DOUBLE,
  "snhmh2614" DOUBLE,
  "snhmh5014" DOUBLE
);

Population

@kaggle.etiennelq_french_employment_by_town.population

4.92 MB
8536584 rows
7 columns


CREATE TABLE population (
  "nivgeo" VARCHAR,
  "codgeo" VARCHAR,
  "libgeo" VARCHAR,
  "moco" BIGINT,
  "ageq80_17" BIGINT,
  "sexe" BIGINT,
  "nb" BIGINT
);