From name as input (may be a candidate, deputy or supposed lobbysts) , we are trying to create a search engine based on Zefix (commercial registry / registre du commerce). The results will be the company he's part of and the domain of interest.

After that result can be compared with the official interest registry and checked if everything is declared (only for deputies) Also, results will be vizualized via a map or different analysis axis.

Finally, we used as data source because Zefix doesn't retrieve the NOGA code. NOGA code is a company classification. First two digits of this code tells what are the activity of the company. So this 2 digits code can categorize more precisely the domain of interes of wach candidates.

Results of each searches are scraped. Purpose after that is to retrieve NOGA code for each companies.

Another axis of our study is to analyze the data from accreditation for the federal assembly. This is based on the declaration made in 2011.

  • Zefix is not complete for our study
  • doesn't have API (⇒ scraping)
  • Lot of homonyms (real homonyms or someone that move from one canton to another)
  • Some companies have several NOGA codes so you'll have to choose one
  • Some NOGA codes are not precise such as “Association” , “Economic activities”

For data visualisation :

  • Young parties are those starting with J….
  • Accreditated people may have since 4 years.

Public prototype is here :

  • Roland
  • Nicolas
  • Stefano
  • Martin
  • Olivier
  • Hicham ⇒ Search engine

Tableau Public ⇒ Visulisation des datas

MongoLab / python ⇒ BackEnd / moteur de recherche

We assess how the fact of having a mandate in a category is correlated with the type of motion presented by politicians.

By using the dataset provided by the RTS employed in the “Politicians’ extra-parliamentary networks unravelled”, and focusing on the mandate and motion categories with highest frequency by politician, we find that there exists a positive correlation (about 20%) between mandate category and type of motion. When distinguishing between political groups, we find that SPV and BDP show correlations higher than those obtaine using the entire sample, while the opposite is true for the SP group. Correlation around zero refers to the Green party.

Overall: (obs=127)

 bill_mode |   1.0000
  int_mode |   0.1936   1.0000

SVP: (obs=36)

 bill_mode |   1.0000
  int_mode |   0.2538   1.0000

SP: (obs=31)

 bill_mode |   1.0000
  int_mode |   0.1451   1.0000

FDP-Lib: (obs=13)

 bill_mode |   1.0000
  int_mode |   0.4123   1.0000

BDP: (obs=6)

 bill_mode |   1.0000
  int_mode |   0.4747   1.0000

GPS: (obs=10)

 bill_mode |   1.0000
  int_mode |   0.1006   1.0000

CVP: (obs=19)

 bill_mode |   1.0000
  int_mode |   0.0089   1.0000

Martin Péclat and Stefano Puddu, University of Neuchatel

For additional information about our research:!research/c46c

  • project/interest_finder.txt
  • Last modified: 2018/11/13 14:47
  • by alicesmith