TR 24 Resume

Introduction

Nous utilisons le registre de transparence de l’Union Européenne pour observer les caractéristiques des groupes d’intérêts qui interviennent auprès de la Commissions Européennes, et en particulier pour observer les différences entre les groupes qui sont dans des groupes d’intérêts et ceux qui ne le sont pas.

Data

Commençons tout d’abord par détailler les données :

  • Nous nous appuyons sur les données publiées par l’Union Européenne à cette adresse : https://data.europa.eu/data/datasets/transparency-register?locale=en

  • On choisit de travailler sur les données de janvier 2024, car le format des données change à partir de ce point, et que certaines informations sont (encore) moins bien remplies, en particulier ce qui concerne les budgets, qui sont renseignés à la fois comme des chiffre bruts et à la fois commet des intervalles dans la même colonne et sont donc difficilement exploitables à partir de juin 2024.

  • On utilise aussi en parallèle les données nettoyées par LobbyFacts, qui permet de nettoyer des informations sur le nombre de temps pleins et sur les budgets : https://www.lobbyfacts.eu/


Attachement du package : 'dplyr'
Les objets suivants sont masqués depuis 'package:stats':

    filter, lag
Les objets suivants sont masqués depuis 'package:base':

    intersect, setdiff, setequal, union

On créé une variable score basée sur le dernier quantile des variables quantitatives, et la présence de la modalité pour les variables dichtomiques :

  • Nombre d’ETP

  • Nombre de personne accrédité au PE

  • Nombre de RDV avec la CE

  • Nombre d’OPC

  • Nombre de ROADMAPS

  • Bureau à Bruxelles

  • A eu une bourse de l’UE

Tableau Récapitulatif des principales variables

Ressources par catégorie
En 2024 - Pourcentage ligne
Characteristic Academic institutions
N = 3351		 Associations and networks of public authorities
N = 1671		 Companies & groups
N = 3,2921		 Entities, offices or networks established by third countries
N = 21		 Law firms
N = 701		 Non-governmental organisations, platforms and networks and similar
N = 3,5401		 Organisations representing churches and religious communities
N = 461		 Other organisations, public or mixed entities
N = 4641		 Professional consultancies
N = 5201		 Self-employed individuals
N = 1291		 Think tanks and research institutions
N = 5641		 Trade and business associations
N = 2,6001		 Trade unions and professional associations
N = 9601		 Overall
N = 12,6891
Members 3.0 (1.0, 6.0) - max = 50.0) 3.0 (1.0, 5.0) - max = 20.0) 2.0 (1.0, 4.0) - max = 121.0) 7.0 (6.0, 8.0) - max = 8.0) 2.5 (1.0, 5.0) - max = 28.0) 3.0 (2.0, 5.0) - max = 915.0) 2.0 (2.0, 5.0) - max = 12.0) 3.0 (2.0, 5.0) - max = 32.0) 3.0 (1.0, 5.0) - max = 89.0) 1.0 (1.0, 1.0) - max = 10.0) 4.0 (2.0, 7.0) - max = 86.0) 2.0 (1.0, 4.0) - max = 93.0) 2.0 (1.0, 4.0) - max = 25.0) 3.0 (1.0, 5.0) - max = 915.0)
Members.FTE 1.00 (0.25, 2.00) - max = 25.00) 1.00 (0.50, 2.25) - max = 14.00) 0.50 (0.25, 1.50) - max = 36.85) 4.00 (2.00, 6.00) - max = 6.00) 0.50 (0.25, 2.25) - max = 20.00) 1.00 (0.45, 2.50) - max = 91.50) 1.00 (0.60, 2.30) - max = 8.50) 1.00 (0.30, 2.25) - max = 24.00) 1.00 (0.30, 3.10) - max = 71.00) 0.75 (0.25, 1.00) - max = 2.00) 1.25 (0.50, 3.23) - max = 60.00) 1.00 (0.28, 2.00) - max = 46.00) 0.75 (0.25, 1.50) - max = 19.00) 0.95 (0.25, 2.00) - max = 91.50)
Number.of.EP.accredited.Person 0.00 (0.00, 0.00) - max = 2.00) 0.00 (0.00, 0.00) - max = 12.00) 0.00 (0.00, 0.00) - max = 12.00) 0.00 (0.00, 0.00) - max = 0.00) 0.00 (0.00, 0.00) - max = 12.00) 0.00 (0.00, 0.00) - max = 39.00) 0.00 (0.00, 2.00) - max = 10.00) 0.00 (0.00, 0.00) - max = 12.00) 0.00 (0.00, 1.00) - max = 56.00) 0.00 (0.00, 0.00) - max = 2.00) 0.00 (0.00, 0.00) - max = 8.00) 0.00 (0.00, 0.00) - max = 20.00) 0.00 (0.00, 0.00) - max = 18.00) 0.00 (0.00, 0.00) - max = 56.00)
Bruxel













    0 308 (3.2%) 121 (1.3%) 2,858 (30%) 1 (<0.1%) 35 (0.4%) 2,685 (28%) 19 (0.2%) 377 (3.9%) 313 (3.2%) 105 (1.1%) 447 (4.6%) 1,678 (17%) 727 (7.5%) 9,674 (100%)
    1 27 (0.9%) 46 (1.5%) 434 (14%) 1 (<0.1%) 35 (1.2%) 855 (28%) 27 (0.9%) 87 (2.9%) 207 (6.9%) 24 (0.8%) 117 (3.9%) 922 (31%) 233 (7.7%) 3,015 (100%)
In.forum.and.EU.platforms













    0 200 (2.9%) 81 (1.2%) 2,026 (29%) 2 (<0.1%) 46 (0.7%) 1,640 (23%) 24 (0.3%) 249 (3.6%) 385 (5.5%) 92 (1.3%) 278 (4.0%) 1,429 (20%) 561 (8.0%) 7,013 (100%)
    1 135 (2.4%) 86 (1.5%) 1,266 (22%) 0 (0%) 24 (0.4%) 1,900 (33%) 22 (0.4%) 215 (3.8%) 135 (2.4%) 37 (0.7%) 286 (5.0%) 1,171 (21%) 399 (7.0%) 5,676 (100%)
In_intergroup













    0 287 (2.9%) 116 (1.2%) 2,700 (27%) 2 (<0.1%) 64 (0.6%) 2,500 (25%) 20 (0.2%) 350 (3.5%) 433 (4.4%) 109 (1.1%) 451 (4.6%) 2,110 (21%) 718 (7.3%) 9,860 (100%)
    1 48 (1.7%) 51 (1.8%) 592 (21%) 0 (0%) 6 (0.2%) 1,040 (37%) 26 (0.9%) 114 (4.0%) 87 (3.1%) 20 (0.7%) 113 (4.0%) 490 (17%) 242 (8.6%) 2,829 (100%)
Has_EP_access













    0 314 (3.0%) 148 (1.4%) 2,710 (26%) 2 (<0.1%) 55 (0.5%) 2,989 (28%) 24 (0.2%) 424 (4.0%) 341 (3.2%) 102 (1.0%) 491 (4.7%) 2,065 (20%) 837 (8.0%) 10,502 (100%)
    1 21 (1.0%) 19 (0.9%) 582 (27%) 0 (0%) 15 (0.7%) 551 (25%) 22 (1.0%) 40 (1.8%) 179 (8.2%) 27 (1.2%) 73 (3.3%) 535 (24%) 123 (5.6%) 2,187 (100%)
Has_EU_grant













    0 272 (2.4%) 142 (1.3%) 3,056 (27%) 2 (<0.1%) 69 (0.6%) 2,819 (25%) 43 (0.4%) 397 (3.5%) 486 (4.3%) 129 (1.1%) 441 (3.9%) 2,468 (22%) 898 (8.0%) 11,222 (100%)
    1 63 (4.3%) 25 (1.7%) 236 (16%) 0 (0%) 1 (<0.1%) 721 (49%) 3 (0.2%) 67 (4.6%) 34 (2.3%) 0 (0%) 123 (8.4%) 132 (9.0%) 62 (4.2%) 1,467 (100%)
In_EG













    0 255 (2.5%) 131 (1.3%) 2,727 (27%) 1 (<0.1%) 60 (0.6%) 2,862 (28%) 43 (0.4%) 393 (3.9%) 450 (4.5%) 108 (1.1%) 451 (4.5%) 1,874 (19%) 733 (7.3%) 10,088 (100%)
    1 67 (3.2%) 29 (1.4%) 443 (21%) 1 (<0.1%) 6 (0.3%) 549 (26%) 0 (0%) 56 (2.7%) 13 (0.6%) 3 (0.1%) 90 (4.3%) 663 (31%) 189 (9.0%) 2,109 (100%)
    Unknown 13 7 122 0 4 129 3 15 57 18 23 63 38 492
NbExpertGroups 0.00 (0.00, 0.00) - max = 7.00) 0.00 (0.00, 0.00) - max = 38.00) 0.00 (0.00, 0.00) - max = 9.00) 1.00 (0.00, 2.00) - max = 2.00) 0.00 (0.00, 0.00) - max = 1.00) 0.00 (0.00, 0.00) - max = 47.00) 0.00 (0.00, 0.00) - max = 0.00) 0.00 (0.00, 0.00) - max = 12.00) 0.00 (0.00, 0.00) - max = 3.00) 0.00 (0.00, 0.00) - max = 1.00) 0.00 (0.00, 0.00) - max = 11.00) 0.00 (0.00, 1.00) - max = 55.00) 0.00 (0.00, 0.00) - max = 26.00) 0.00 (0.00, 0.00) - max = 55.00)
    Unknown 13 7 122 0 4 129 3 15 57 18 23 63 38 492
LoI_.global 116 (4.4%) 27 (1.0%) 772 (29%) 0 (0%) 20 (0.8%) 743 (28%) 5 (0.2%) 80 (3.0%) 132 (5.0%) 26 (1.0%) 169 (6.4%) 395 (15%) 151 (5.7%) 2,636 (100%)
LoI_.national 162 (4.0%) 53 (1.3%) 987 (24%) 0 (0%) 23 (0.6%) 1,059 (26%) 13 (0.3%) 138 (3.4%) 215 (5.3%) 41 (1.0%) 218 (5.4%) 813 (20%) 350 (8.6%) 4,072 (100%)
LoI_.european 145 (3.7%) 53 (1.3%) 1,020 (26%) 0 (0%) 27 (0.7%) 1,051 (27%) 16 (0.4%) 133 (3.4%) 208 (5.2%) 35 (0.9%) 227 (5.7%) 724 (18%) 327 (8.2%) 3,966 (100%)
LoI_.sub-national 148 (5.1%) 49 (1.7%) 756 (26%) 0 (0%) 12 (0.4%) 803 (27%) 9 (0.3%) 114 (3.9%) 151 (5.2%) 28 (1.0%) 177 (6.1%) 433 (15%) 242 (8.3%) 2,922 (100%)
ROADMAPS 0.00 (0.00, 0.00) - max = 10.00) 0.00 (0.00, 1.00) - max = 13.00) 0.00 (0.00, 0.00) - max = 57.00) 0.00 (0.00, 0.00) - max = 0.00) 0.00 (0.00, 0.00) - max = 2.00) 0.00 (0.00, 1.00) - max = 93.00) 0.00 (0.00, 0.00) - max = 9.00) 0.00 (0.00, 0.00) - max = 111.00) 0.00 (0.00, 0.00) - max = 21.00) 0.00 (0.00, 0.00) - max = 2.00) 0.00 (0.00, 0.00) - max = 24.00) 0.00 (0.00, 2.00) - max = 94.00) 0.00 (0.00, 1.00) - max = 36.00) 0.00 (0.00, 1.00) - max = 111.00)
    Unknown 13 7 122 0 4 129 3 15 57 18 23 63 38 492
OPC 1.00 (0.00, 2.00) - max = 15.00) 0.00 (0.00, 1.00) - max = 16.00) 0.00 (0.00, 1.00) - max = 54.00) 0.00 (0.00, 0.00) - max = 0.00) 0.00 (0.00, 0.00) - max = 8.00) 0.00 (0.00, 1.00) - max = 91.00) 0.00 (0.00, 0.00) - max = 13.00) 0.00 (0.00, 1.00) - max = 138.00) 0.00 (0.00, 0.00) - max = 17.00) 0.00 (0.00, 0.00) - max = 3.00) 0.00 (0.00, 1.00) - max = 25.00) 1.00 (0.00, 4.00) - max = 86.00) 0.00 (0.00, 2.00) - max = 91.00) 0.00 (0.00, 1.00) - max = 138.00)
    Unknown 13 7 122 0 4 129 3 15 57 18 23 63 38 492
1 Median (Q1, Q3) - max = 100% Centile) ; n (%)

I. Description des organisations

Type d'organisation
Dans le registre de transparence de 2024
Characteristic N = 12,6891
Category.of.registration
    Academic institutions 335 (2.6%)
    Associations and networks of public authorities 167 (1.3%)
    Companies & groups 3,292 (26%)
    Entities, offices or networks established by third countries 2 (<0.1%)
    Law firms 70 (0.6%)
    Non-governmental organisations, platforms and networks and similar 3,540 (28%)
    Organisations representing churches and religious communities 46 (0.4%)
    Other organisations, public or mixed entities 464 (3.7%)
    Professional consultancies 520 (4.1%)
    Self-employed individuals 129 (1.0%)
    Think tanks and research institutions 564 (4.4%)
    Trade and business associations 2,600 (20%)
    Trade unions and professional associations 960 (7.6%)
1 n (%)

ETP

   0%   10%   20%   30%   40%   50%   60%   70%   80%   90%  100% 
 0.10  0.10  0.25  0.40  0.50  0.95  1.10  1.60  2.50  4.00 91.50

ETP en fonction des groupes d’expert

Le chargement a nécessité le package : grid
   (0,1]    (1,2]    (2,5]   (5,10] (10,100] 
    7550     2241     2048      617      233 
492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091
FTE_RECO

    (0,1] 6,310 (63%) 878 (42%)
    (1,2] 1,755 (17%) 418 (20%)
    (2,5] 1,502 (15%) 500 (24%)
    (5,10] 379 (3.8%) 225 (11%)
    (10,100] 142 (1.4%) 88 (4.2%)
1 n (%) 
[1] 1.395304e-97

Bruxelles

Characteristic N = 12,6891
Bruxel
    0 9,674 (76%)
    1 3,015 (24%)
1 n (%)

Bruxelles en fonction des groupes d’experts

492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091
Bruxel

    0 8,096 (80%) 1,143 (54%)
    1 1,992 (20%) 966 (46%)
1 n (%) 

[1] 6.370983e-142
              TR24Full$Bruxel
TR24Full$In_EG         0         1
             0  25.39148 -25.39148
             1 -25.39148  25.39148
                  OR  2.5 % 97.5 %         p    
Fisher's test 3.4345 3.1075  3.795 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

L’Odds ratio nous permet de lire que si on ne s’intéresse qu’à la participation à des groupes d’expert et à la présence de bureau à Bruxelles, on a 3,5 plus de chance de participer à un GE si l’on est à Bruxelles.

Resized limits to included dashed line in forest panel
`height` was translated to `width`.

Budget

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       0        0    10000   119511   100000 11385000 
      0%      30%      40%      50%      60%      70%      80%      90% 
       0        0    10000    10000    25000    50000   100000   300000 
    100% 
11385000

Budgets en fonction des groupes d’experts

$`0`
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       0        0    10000    96950    50000 11385000 

$`1`
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       0        0    50000   245652   200000 10000000 

    Welch Two Sample t-test

data:  Lobbying.cost by In_EG
t = -10.44, df = 2347, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -176632.9 -120771.4
sample estimates:
mean in group 0 mean in group 1 
       96949.84       245651.97
Name Lobbying.cost
Fleishman-Hillard 11385000
Veracel Celulose SA 10000000
FTI Consulting Belgium 8180000
Apple Inc. 6500000
Rud Pedersen Public Affairs Brussels 6145000
Burson Cohn & Wolfe SRL 6080000
Kreab Worldwide 6035000
Dentons Global Advisors Europe SA 5575000
EUTOP Europe GmbH 4810000
FGS Global (Europe) GmbH 4750000
Association for Financial Markets in Europe 4500000
Flint Europe 4115000
Penta (formerly Hume Brophy) 4060000
EU Focus Group 3970000
Ordem dos Solicitadores e dos Agentes de Execução 3500000
APCO Worldwide 3185000
FIPRA International SRL 3075000
Teneo Brussels 3060000
BDEW Bundesverband der Energie- und Wasserwirtschaft e. V. 3000000
British American Tobacco 3000000
Bundesverband Öffentlicher Banken Deutschlands eV 3000000
Bundesverband deutscher Banken e.V. 3000000
Servicios para una educacion alternativa A.C. 3000000
Acumen Public Affairs 2875000
Equinor ASA 2500000
Brunswick Group Limited srl 2395000
SEC NEWGATE EU 2330000
Hanover Communications International 2310000
Bundesverband der Deutschen Volksbanken und Raiffeisenbanken 2250000
Philip Morris International Inc. 2250000
Uber 2250000
The Chemours Company 2250000
McDonald’s Global Franchising Limited 2250000
Fédération Française de l'Assurance 2250000
NOVE 2205000
Hill & Knowlton International Belgium 2180000
Siemens Energy AG 2000000
International Swaps and Derivatives Association 2000000
Intel Corporation 2000000
Novartis International AG 2000000
JT International 2000000
ELECTRICITE DE FRANCE 2000000
Forward Global 1995000
Alber & Geiger 1850000
POLITICAL INTELLIGENCE BRUSSELS 1820000
Weber Shandwick 1815000
Grayling 1790000
F. Hoffmann-La Roche Ltd 1750000
Charleroi Entreprendre 1750000
ITTI Sp. z o.o. 1750000
Kontomatik Sp. z o.o. 1750000
Deutscher Sparkassen-und Giroverband 1750000
SANOFI 1750000
Finance Denmark 1750000
Associazione Bancaria Italiana 1750000
TÜV &#124; DEKRA arge tp 21 GbR 1700000
Covington & Burling LLP 1670000
#SustainablePublicAffairs 1655000
LOW Associates Brussels 1515000
European Mortgage Federation - European Covered Bond Council 1500000
Cisco Systems Inc. 1500000
Nederlandse Vereniging van Banken / Dutch Banking Association 1500000
Syngenta Crop Protection AG 1500000
Vereniging van de Nederlandse Pluimveeverwerkende Industrie 1500000
Vodafone Belgium SA 1500000
Direction des Services de la Navigation Aérienne 1500000
European Telecommunications Standards Institute 1500000
Edelman Public Relations Worldwide 1490000
Hague Corporate Affairs 1425000
Portland PR Europe Limited 1360000
Rasmussen Global 1350000
ZN 1345000
Afore Consulting 1310000
TikTok Technology Ltd 1250000
LVMH Publica 1250000
Airbnb Ireland UC 1250000
Ingka Services A.B. 1250000
Merck Sharp & Dohme Europe Belgium SRL 1250000
France Digitale 1250000
Die Deutsche Kreditwirtschaft 1250000
Pfizer Inc. 1250000
Assicurazioni Generali S.p.A 1250000
Swedish Bankers´ Association 1250000
General Electric Company 1250000
H/Advisors Cicero 1250000
European Insurance CFO Forum 1250000
The Walt Disney Company Benelux BVBA 1250000
Daiichi Sankyo Europe GmbH 1250000
CFA Institute 1250000
Fourtold 1180000
Publyon 1120000
Trilligent 1100000
Hanbury Strategy and Communications Limited 1095000
RPP Group 1040000
ACTION EUROPE 1040000 
Warning: Removed 3908 rows containing non-finite outside the scale range (`stat_bin()`).
Removed 3908 rows containing non-finite outside the scale range (`stat_bin()`).

Bourse de l’UE

Characteristic N = 12,6891
Has_EU_grant
    0 11,222 (88%)
    1 1,467 (12%)
1 n (%)

Bourses en fonction des groupes d’experts

492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091		 p-value2
Has_EU_grant

<0.001
    0 9,024 (89%) 1,744 (83%)
    1 1,064 (11%) 365 (17%)
1 n (%)
2 Pearson’s Chi-squared test 
                  OR  2.5 % 97.5 %         p    
Fisher's test 1.7749 1.5549 2.0232 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Le chargement a nécessité le package : carData
lattice theme set by effectsTheme()
See ?effectsTheme for details.
Resized limits to included dashed line in forest panel
Warning in recalculate_width_panels(panel_positions, mapped_text = mapped_text,
: Unable to resize forest panel to be smaller than its heading; consider a
smaller text size
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Meetings

Nombre de rendez-vous avec la Commission
Characteristic N = 12,6891
RECO_Meetings
    0 7,876 (62%)
    1 1,682 (13%)
    2 701 (5.5%)
    3 441 (3.5%)
    4-5 472 (3.7%)
    6-10 527 (4.2%)
    11-20 461 (3.6%)
    20 et + 529 (4.2%)
1 n (%)

Meetings en fonction des groupes d’experts

492 missing rows in the "In_EG" column have been removed.
Proportion d'organisation ayant eu un meeting par rapport à la participation à un groupe d'expert
Characteristic In_EG
N = 2,1091		 Out_EG
N = 10,0881
Has_Meetings

    Meeting 1,176 (56%) 3,531 (35%)
    No_meeting 933 (44%) 6,557 (65%)
1 n (%) 
Warning in recalculate_width_panels(panel_positions, mapped_text = mapped_text,
: Unable to resize forest panel to be smaller than its heading; consider a
smaller text size
`height` was translated to `width`.

EP.passes

OPC

Characteristic N = 12,6891
OPC 0.00 (0.00, 1.00)
    Unknown 492
1 Median (Q1, Q3)

On supprime l’écrasante majorité de 0 pour voir ce que donne l’histogramme du reste des valeurs positives

Characteristic N = 12,6891
Has_OPC
    No_OPC 7,386 (61%)
    OPC 4,811 (39%)
    Unknown 492
1 n (%)

OPC en fonction des groupes d’experts

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   0.000   1.082   1.000  57.000 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   2.000   5.508   7.000 138.000 

    Welch Two Sample t-test

data:  OPC by In_EG
t = -21.016, df = 2184.6, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -4.839049 -4.013037
sample estimates:
mean in group 0 mean in group 1 
       1.081780        5.507824

ROADMAPS

Characteristic N = 12,6891
ROADMAPS 0.00 (0.00, 1.00)
    Unknown 492
1 Median (Q1, Q3)

On supprime l’écrasante majorité de 0 pour voir ce que donne l’histogramme du reste des valeurs positives

Characteristic N = 12,6891
Has_RD
    No_OPC 8,793 (72%)
    OPC 3,404 (28%)
    Unknown 492
1 n (%)

ROADMAPS en fonction des groupes d’experts

492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091
Has_RD

    No_OPC 7,810 (77%) 983 (47%)
    OPC 2,278 (23%) 1,126 (53%)
1 n (%) 
$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   0.000   0.679   0.000  72.000 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   1.000   4.367   5.000 111.000 

    Welch Two Sample t-test

data:  ROADMAPS by In_EG
t = -18.782, df = 2172, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -4.073564 -3.303332
sample estimates:
mean in group 0 mean in group 1 
      0.6790246       4.3674727

In Forum

Characteristic N = 12,6891
In.forum.and.EU.platforms
    0 7,013 (55%)
    1 5,676 (45%)
1 n (%)

Forums et plateformes de l’UE en fonction des groupes experts

492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091
In.forum.and.EU.platforms

    0 5,851 (58%) 844 (40%)
    1 4,237 (42%) 1,265 (60%)
1 n (%) 

[1] 2.6457e-51
              
TR24Full$In_EG         0         1
             0  15.09165 -15.09165
             1 -15.09165  15.09165
                  OR  2.5 % 97.5 %         p    
Fisher's test 2.0697 1.8789 2.2805 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Resized limits to included dashed line in forest panel
Warning in recalculate_width_panels(panel_positions, mapped_text = mapped_text,
: Unable to resize forest panel to be smaller than its heading; consider a
smaller text size
`height` was translated to `width`.

In InterGroup

Characteristic N = 12,6891
In_intergroup
    0 9,860 (78%)
    1 2,829 (22%)
1 n (%)

Intergroupe en fonction des groupes experts

492 missing rows in the "In_EG" column have been removed.
Characteristic 0
N = 10,0881		 1
N = 2,1091
In_intergroup

    0 7,962 (79%) 1,520 (72%)
    1 2,126 (21%) 589 (28%)
1 n (%) 

[1] 7.282532e-12
              TR24Full$In_intergroup
TR24Full$In_EG         0         1
             0  6.880777 -6.880777
             1 -6.880777  6.880777
                  OR  2.5 % 97.5 %         p    
Fisher's test 1.4512 1.3022 1.6159 1.826e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Resized limits to included dashed line in forest panel
`height` was translated to `width`.

2. Détails en fonction du type d’organisations

492 missing rows in the "In_EG" column have been removed.
Type d'organisation
Dans le registre de transparence de 2024
Characteristic 0
N = 10,0881		 1
N = 2,1091
Category.of.registration

    Academic institutions 255 (2.5%) 67 (3.2%)
    Associations and networks of public authorities 131 (1.3%) 29 (1.4%)
    Companies & groups 2,727 (27%) 443 (21%)
    Entities, offices or networks established by third countries 1 (<0.1%) 1 (<0.1%)
    Law firms 60 (0.6%) 6 (0.3%)
    Non-governmental organisations, platforms and networks and similar 2,862 (28%) 549 (26%)
    Organisations representing churches and religious communities 43 (0.4%) 0 (0%)
    Other organisations, public or mixed entities 393 (3.9%) 56 (2.7%)
    Professional consultancies 450 (4.5%) 13 (0.6%)
    Self-employed individuals 108 (1.1%) 3 (0.1%)
    Think tanks and research institutions 451 (4.5%) 90 (4.3%)
    Trade and business associations 1,874 (19%) 663 (31%)
    Trade unions and professional associations 733 (7.3%) 189 (9.0%)
1 n (%)

On ne garde pas des analyses suivantes les organisations suivantes :

  • “Entities, offices or networks established by third countries”

  • “Self-employed individuals”

  • “Organisations representing churches and religious communities”

  • “Law firms”

Parce qu’elles représentent trop peu d’individus, en particulier dans les groupes experts.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

ETP

ETP en fonction de l'organisation
Category.of.registration n_ETP Percent mean_orga nb_orga
Non-governmental organisations, platforms and networks and similar 7317.95 33.15 2.067218 3540
Trade and business associations 4312.90 19.54 1.658808 2600
Companies & groups 4003.95 18.14 1.216267 3292
Professional consultancies 1861.40 8.43 3.579615 520
Think tanks and research institutions 1574.50 7.13 2.791667 564
Trade unions and professional associations 1223.70 5.54 1.274687 960
Other organisations, public or mixed entities 862.20 3.91 1.858190 464
Academic institutions 582.95 2.64 1.740149 335
Associations and networks of public authorities 334.05 1.51 2.000299 167

En fonction de l’appartenance à un groupe expert

`summarise()` has grouped output by 'In_EG'. You can override using the
`.groups` argument.
Nombre d'ETP dans les organisations
En fonction du type d'organisations
In_EG n min q1 med q3 max Mean.FTE sd.FTE
Academic institutions
0 255 0.1 0.25 1.00 2.00 25.00 1.736863 2.605826
1 67 0.1 0.45 1.05 2.35 9.00 1.792537 1.969776
Associations and networks of public authorities
0 131 0.1 0.40 1.00 2.02 13.00 1.871374 2.539443
1 29 0.1 0.90 1.75 2.50 14.00 2.663793 3.244966
Companies & groups
0 2727 0.1 0.25 0.50 1.25 29.75 1.071030 1.557618
1 443 0.1 0.30 1.25 3.00 36.85 2.226185 3.219214
Non-governmental organisations, platforms and networks and similar
0 2862 0.1 0.40 1.00 2.20 91.50 1.833753 3.108770
1 549 0.1 0.75 1.75 4.00 62.25 3.421038 5.056722
Other organisations, public or mixed entities
0 393 0.1 0.30 1.00 2.25 24.00 1.767048 2.502048
1 56 0.1 0.50 1.25 2.81 16.25 2.765179 3.879491
Professional consultancies
0 450 0.1 0.31 1.00 3.50 71.00 3.851556 8.281492
1 13 0.1 0.50 1.00 2.75 26.00 3.530769 6.994419
Think tanks and research institutions
0 451 0.1 0.50 1.25 3.00 60.00 2.692683 5.049851
1 90 0.1 0.55 2.05 4.44 35.00 3.361111 4.668270
Trade and business associations
0 1874 0.1 0.25 0.75 1.50 19.25 1.263687 1.623185
1 663 0.1 0.58 1.70 3.50 46.00 2.860633 4.247329
Trade unions and professional associations
0 733 0.1 0.25 0.50 1.35 14.00 1.066576 1.345904
1 189 0.1 0.50 1.25 2.50 19.00 2.193915 2.731578

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
<0.001
<0.001
    Academic institutions 1.05 (0.40, 2.45)
1.00 (0.25, 2.00)
    Associations and networks of public authorities 1.75 (0.90, 2.50)
1.00 (0.30, 2.05)
    Companies & groups 1.25 (0.30, 3.00)
0.50 (0.25, 1.25)
    Non-governmental organisations, platforms and networks and similar 1.75 (0.75, 4.00)
1.00 (0.40, 2.20)
    Other organisations, public or mixed entities 1.25 (0.50, 2.88)
1.00 (0.30, 2.25)
    Professional consultancies 1.00 (0.50, 2.75)
1.00 (0.30, 3.50)
    Think tanks and research institutions 2.05 (0.50, 4.50)
1.25 (0.50, 3.00)
    Trade and business associations 1.70 (0.55, 3.50)
0.75 (0.25, 1.50)
    Trade unions and professional associations 1.25 (0.50, 2.50)
0.50 (0.25, 1.35)
1 Members.FTE: Median (Q1, Q3)
2 Kruskal-Wallis rank sum test

Bruxelles

Characteristic In_Bruxel
N = 2,9281		 No_Bruxel
N = 9,5141		 p-value2
Category.of.registration

<0.001
    Academic institutions 27 (0.9%) 308 (3.2%)
    Associations and networks of public authorities 46 (1.6%) 121 (1.3%)
    Companies & groups 434 (15%) 2,858 (30%)
    Non-governmental organisations, platforms and networks and similar 855 (29%) 2,685 (28%)
    Other organisations, public or mixed entities 87 (3.0%) 377 (4.0%)
    Professional consultancies 207 (7.1%) 313 (3.3%)
    Think tanks and research institutions 117 (4.0%) 447 (4.7%)
    Trade and business associations 922 (31%) 1,678 (18%)
    Trade unions and professional associations 233 (8.0%) 727 (7.6%)
1 n (%)
2 Pearson’s Chi-squared test

En fonction des groupes experts

Characteristic
In_EG
No_EG
In_Bruxel
N = 9641		 No_Bruxel
N = 1,1351		 p-value2 In_Bruxel
N = 1,9151		 No_Bruxel
N = 7,9611		 p-value2
Category.of.registration

<0.001

<0.001
    Academic institutions 6 (0.6%) 61 (5.4%)
20 (1.0%) 235 (3.0%)
    Associations and networks of public authorities 18 (1.9%) 11 (1.0%)
25 (1.3%) 106 (1.3%)
    Companies & groups 133 (14%) 310 (27%)
298 (16%) 2,429 (31%)
    Non-governmental organisations, platforms and networks and similar 237 (25%) 312 (27%)
600 (31%) 2,262 (28%)
    Other organisations, public or mixed entities 14 (1.5%) 42 (3.7%)
71 (3.7%) 322 (4.0%)
    Professional consultancies 4 (0.4%) 9 (0.8%)
191 (10.0%) 259 (3.3%)
    Think tanks and research institutions 29 (3.0%) 61 (5.4%)
88 (4.6%) 363 (4.6%)
    Trade and business associations 435 (45%) 228 (20%)
479 (25%) 1,395 (18%)
    Trade unions and professional associations 88 (9.1%) 101 (8.9%)
143 (7.5%) 590 (7.4%)
1 n (%)
2 Pearson’s Chi-squared test

Budget

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
<0.001
<0.001
    Academic institutions 0 (0, 0)
0 (0, 0)
    Associations and networks of public authorities 10,000 (0, 50,000)
0 (0, 50,000)
    Companies & groups 100,000 (25,000, 400,000)
50,000 (10,000, 100,000)
    Non-governmental organisations, platforms and networks and similar 0 (0, 25,000)
0 (0, 10,000)
    Other organisations, public or mixed entities 10,000 (0, 100,000)
10,000 (0, 50,000)
    Professional consultancies 85,000 (45,000, 100,000)
50,000 (10,000, 200,000)
    Think tanks and research institutions 0 (0, 0)
0 (0, 0)
    Trade and business associations 100,000 (25,000, 400,000)
50,000 (10,000, 100,000)
    Trade unions and professional associations 50,000 (10,000, 200,000)
25,000 (10,000, 50,000)
1 Lobbying.cost: Median (Q1, Q3)
2 Kruskal-Wallis rank sum test 
`summarise()` has grouped output by 'In_EG'. You can override using the
`.groups` argument.
Nombre d'ETP dans les organisations
En fonction du type d'organisations
In_EG n min q1 med q3 max Mean.FTE sd.FTE
Academic institutions
0 255 0 0 0 0 600000 18509.80 73724.96
1 67 0 0 0 0 400000 12238.81 55310.99
Associations and networks of public authorities
0 131 0 0 0 37500 1000000 59541.98 170097.77
1 29 0 0 10000 50000 500000 50517.24 102993.97
Companies & groups
0 2727 0 10000 50000 100000 10000000 140385.04 340263.16
1 443 0 25000 100000 400000 9000000 434187.36 926419.43
Non-governmental organisations, platforms and networks and similar
0 2862 0 0 0 10000 3000000 28810.27 119176.11
1 549 0 0 0 25000 3000000 82194.90 232317.47
Other organisations, public or mixed entities
0 393 0 0 10000 50000 1500000 74389.31 183822.46
1 56 0 0 10000 100000 2750000 183928.57 504890.37
Professional consultancies
0 450 0 10000 50000 200000 11385000 361822.22 1050366.84
1 13 10000 45000 85000 100000 1930000 245384.62 528888.23
Think tanks and research institutions
0 451 0 0 0 0 600000 23381.37 81660.11
1 90 0 0 0 0 2000000 59944.44 252291.18
Trade and business associations
0 1874 0 10000 50000 100000 4500000 121093.92 253325.15
1 663 0 25000 100000 400000 10000000 338099.55 732566.99
Trade unions and professional associations
0 733 0 10000 25000 50000 3500000 75272.85 193589.00
1 189 0 10000 50000 200000 3000000 183492.06 337146.55

Bourse de l’UE

Characteristic Has_grant
N = 1,4631		 No_grant
N = 10,9791		 p-value2
Category.of.registration

<0.001
    Academic institutions 63 (4.3%) 272 (2.5%)
    Associations and networks of public authorities 25 (1.7%) 142 (1.3%)
    Companies & groups 236 (16%) 3,056 (28%)
    Non-governmental organisations, platforms and networks and similar 721 (49%) 2,819 (26%)
    Other organisations, public or mixed entities 67 (4.6%) 397 (3.6%)
    Professional consultancies 34 (2.3%) 486 (4.4%)
    Think tanks and research institutions 123 (8.4%) 441 (4.0%)
    Trade and business associations 132 (9.0%) 2,468 (22%)
    Trade unions and professional associations 62 (4.2%) 898 (8.2%)
1 n (%)
2 Pearson’s Chi-squared test

En fonction des groupes experts

The following errors were returned during `tbl_strata()`:
✖ For variable `Category.of.registration` (`Has_EU_grant`) and "estimate",
  "p.value", "conf.low", and "conf.high" statistics: FEXACT erreur 7(location).
  LDSTP=18270 est trop petit pour ce problème, (pastp=65.2352,
  ipn_0:=ipoin[itp=548]=8329, stp[ipn_0]=61.0695). Augmentez la taille de
  l’environnement de travail ou considérez l’utilisation de
  ‘simulate.p.value=TRUE’.
Characteristic
In_EG
No_EG
Has_grant
N = 3651		 No_grant
N = 1,7341		 p-value Has_grant
N = 1,0601		 No_grant
N = 8,8161		 p-value2
Category.of.registration




<0.001
    Academic institutions 13 (3.6%) 54 (3.1%)
48 (4.5%) 207 (2.3%)
    Associations and networks of public authorities 9 (2.5%) 20 (1.2%)
15 (1.4%) 116 (1.3%)
    Companies & groups 41 (11%) 402 (23%)
188 (18%) 2,539 (29%)
    Non-governmental organisations, platforms and networks and similar 177 (48%) 372 (21%)
529 (50%) 2,333 (26%)
    Other organisations, public or mixed entities 14 (3.8%) 42 (2.4%)
51 (4.8%) 342 (3.9%)
    Professional consultancies 5 (1.4%) 8 (0.5%)
24 (2.3%) 426 (4.8%)
    Think tanks and research institutions 23 (6.3%) 67 (3.9%)
98 (9.2%) 353 (4.0%)
    Trade and business associations 56 (15%) 607 (35%)
73 (6.9%) 1,801 (20%)
    Trade unions and professional associations 27 (7.4%) 162 (9.3%)
34 (3.2%) 699 (7.9%)
1 n (%)
2 Pearson’s Chi-squared test

Meetings

Characteristic N = 12,4421
Category.of.registration
    Academic institutions 0.0 (0.0 - 1.0) | 3.0 - 23.0
    Associations and networks of public authorities 0.0 (0.0 - 1.0) | 6.0 - 27.0
    Companies & groups 0.0 (0.0 - 2.0) | 11.0 - 372.0
    Non-governmental organisations, platforms and networks and similar 0.0 (0.0 - 1.0) | 4.0 - 291.0
    Other organisations, public or mixed entities 0.0 (0.0 - 1.0) | 4.0 - 81.0
    Professional consultancies 0.0 (0.0 - 1.0) | 7.0 - 94.0
    Think tanks and research institutions 0.0 (0.0 - 1.0) | 8.0 - 95.0
    Trade and business associations 0.0 (0.0 - 2.0) | 11.0 - 443.0
    Trade unions and professional associations 0.0 (0.0 - 1.0) | 5.0 - 252.0
1 Meetings: Median (Q1 - Q3) | 90% Centile - Max

En fonction des groupes experts

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
<0.001
<0.001
    Academic institutions 0 (0 - 3) | 4 - 23
0.0 (0.0 - 1.0) | 2.0 - 17.0
    Associations and networks of public authorities 1 (0 - 9) | 17 - 27
0.0 (0.0 - 0.0) | 2.0 - 22.0
    Companies & groups 2 (0 - 20) | 49 - 372
0.0 (0.0 - 2.0) | 7.0 - 178.0
    Non-governmental organisations, platforms and networks and similar 1 (0 - 5) | 19 - 291
0.0 (0.0 - 1.0) | 3.0 - 136.0
    Other organisations, public or mixed entities 0 (0 - 1) | 13 - 81
0.0 (0.0 - 1.0) | 3.0 - 79.0
    Professional consultancies 0 (0 - 0) | 19 - 22
0.0 (0.0 - 1.0) | 8.0 - 94.0
    Think tanks and research institutions 1 (0 - 5) | 19 - 37
0.0 (0.0 - 1.0) | 6.0 - 95.0
    Trade and business associations 2 (0 - 12) | 35 - 443
0.0 (0.0 - 1.0) | 4.0 - 93.0
    Trade unions and professional associations 1 (0 - 8) | 20 - 252
0.0 (0.0 - 1.0) | 2.0 - 82.0
1 Meetings: Median (Q1 - Q3) | 90% Centile - Max
2 Kruskal-Wallis rank sum test

EP.Passes

Characteristic N = 12,4421
Category.of.registration
    Academic institutions 2 (1 - 8) | 16 - 33 / 49
    Associations and networks of public authorities 7 (2 - 17) | 33 - 79 / 39
    Companies & groups 6 (2 - 16) | 40 - 218 / 1,013
    Non-governmental organisations, platforms and networks and similar 6 (2 - 19) | 46 - 366 / 1,032
    Other organisations, public or mixed entities 6 (2 - 16) | 39 - 238 / 95
    Professional consultancies 11 (3 - 46) | 148 - 725 / 248
    Think tanks and research institutions 7 (2 - 21) | 44 - 85 / 130
    Trade and business associations 10 (3 - 28) | 55 - 318 / 902
    Trade unions and professional associations 6 (2 - 21) | 47 - 168 / 249
1 all.EP.passes: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing

En fonction des groupes experts

Warning: Removed 8685 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
0.043
<0.001
    Academic institutions 3 (2 - 8) | 21 - 33 / 12
2 (1 - 9) | 15 - 31 / 36
    Associations and networks of public authorities 15 (7 - 21) | 71 - 79 / 15
3 (1 - 8) | 26 - 66 / 23
    Companies & groups 20 (7 - 42) | 69 - 175 / 213
5 (2 - 12) | 27 - 218 / 788
    Non-governmental organisations, platforms and networks and similar 18 (4 - 46) | 85 - 366 / 233
5 (2 - 15) | 34 - 156 / 789
    Other organisations, public or mixed entities 7 (2 - 19) | 140 - 238 / 14
7 (2 - 16) | 38 - 90 / 78
    Professional consultancies 80 (2 - 160) | 164 - 164 / 4
13 (4 - 50) | 143 - 725 / 232
    Think tanks and research institutions 14 (8 - 35) | 60 - 67 / 29
4 (2 - 20) | 36 - 85 / 98
    Trade and business associations 19 (7 - 43) | 74 - 318 / 404
6 (2 - 17) | 34 - 125 / 494
    Trade unions and professional associations 16 (5 - 40) | 67 - 168 / 88
4 (1 - 12) | 31 - 77 / 157
1 all.EP.passes: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing
2 Kruskal-Wallis rank sum test

OPC

Characteristic N = 12,4421
Category.of.registration
    Academic institutions 1.00 (0.00 - 2.00) | 4.00 - 15.00 / 322
    Associations and networks of public authorities 0.00 (0.00 - 1.00) | 3.50 - 16.00 / 160
    Companies & groups 0.00 (0.00 - 1.00) | 4.00 - 54.00 / 3,170
    Non-governmental organisations, platforms and networks and similar 0.00 (0.00 - 1.00) | 4.00 - 91.00 / 3,411
    Other organisations, public or mixed entities 0.00 (0.00 - 1.00) | 3.00 - 138.00 / 449
    Professional consultancies 0.00 (0.00 - 0.00) | 1.00 - 17.00 / 463
    Think tanks and research institutions 0.00 (0.00 - 1.00) | 3.00 - 25.00 / 541
    Trade and business associations 1.00 (0.00 - 4.00) | 10.00 - 86.00 / 2,537
    Trade unions and professional associations 0.00 (0.00 - 2.00) | 5.00 - 91.00 / 922
1 OPC: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing

En fonction des groupes experts

Warning: Removed 467 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
<0.001
<0.001
    Academic institutions 2 (0 - 4) | 5 - 15 / 67
0.00 (0.00 - 2.00) | 3.00 - 10.00 / 255
    Associations and networks of public authorities 0 (0 - 7) | 12 - 16 / 29
0.00 (0.00 - 1.00) | 2.00 - 10.00 / 131
    Companies & groups 1 (0 - 6) | 13 - 54 / 443
0.00 (0.00 - 1.00) | 3.00 - 43.00 / 2,727
    Non-governmental organisations, platforms and networks and similar 1 (0 - 6) | 13 - 91 / 549
0.00 (0.00 - 1.00) | 3.00 - 41.00 / 2,862
    Other organisations, public or mixed entities 1 (0 - 4) | 9 - 138 / 56
0.00 (0.00 - 1.00) | 2.00 - 13.00 / 393
    Professional consultancies 0 (0 - 0) | 1 - 1 / 13
0.00 (0.00 - 0.00) | 1.00 - 17.00 / 450
    Think tanks and research institutions 2 (0 - 4) | 11 - 21 / 90
0.00 (0.00 - 1.00) | 2.00 - 25.00 / 451
    Trade and business associations 4 (1 - 11) | 22 - 86 / 663
0.00 (0.00 - 2.00) | 5.00 - 57.00 / 1,874
    Trade unions and professional associations 2 (0 - 6) | 14 - 91 / 189
0.00 (0.00 - 1.00) | 3.00 - 33.00 / 733
1 OPC: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing
2 Kruskal-Wallis rank sum test

ROADMAPS

Characteristic N = 12,4421
Category.of.registration
    Academic institutions 0.00 (0.00 - 0.00) | 1.00 - 10.00 / 322
    Associations and networks of public authorities 0.00 (0.00 - 1.00) | 2.00 - 13.00 / 160
    Companies & groups 0.00 (0.00 - 0.00) | 2.00 - 57.00 / 3,170
    Non-governmental organisations, platforms and networks and similar 0.00 (0.00 - 1.00) | 3.00 - 93.00 / 3,411
    Other organisations, public or mixed entities 0.00 (0.00 - 0.00) | 2.00 - 111.00 / 449
    Professional consultancies 0.00 (0.00 - 0.00) | 0.00 - 21.00 / 463
    Think tanks and research institutions 0.00 (0.00 - 0.00) | 2.00 - 24.00 / 541
    Trade and business associations 0.00 (0.00 - 2.00) | 8.00 - 94.00 / 2,537
    Trade unions and professional associations 0.00 (0.00 - 1.00) | 4.00 - 36.00 / 922
1 ROADMAPS: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing

En fonction des groupes experts

Warning: Removed 467 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Characteristic
In_EG
No_EG
N = 2,0991 p-value2 N = 9,8761 p-value2
Category.of.registration
<0.001
<0.001
    Academic institutions 0 (0 - 1) | 2 - 5 / 67
0.00 (0.00 - 0.00) | 1.00 - 10.00 / 255
    Associations and networks of public authorities 1 (0 - 4) | 9 - 13 / 29
0.00 (0.00 - 0.00) | 1.00 - 5.00 / 131
    Companies & groups 0 (0 - 2) | 7 - 57 / 443
0.00 (0.00 - 0.00) | 1.00 - 30.00 / 2,727
    Non-governmental organisations, platforms and networks and similar 1 (0 - 5) | 13 - 93 / 549
0.00 (0.00 - 0.00) | 2.00 - 72.00 / 2,862
    Other organisations, public or mixed entities 0 (0 - 1) | 5 - 111 / 56
0.00 (0.00 - 0.00) | 2.00 - 72.00 / 393
    Professional consultancies 0 (0 - 0) | 1 - 1 / 13
0.00 (0.00 - 0.00) | 0.00 - 21.00 / 450
    Think tanks and research institutions 0 (0 - 2) | 7 - 24 / 90
0.00 (0.00 - 0.00) | 1.00 - 14.00 / 451
    Trade and business associations 3 (0 - 10) | 19 - 94 / 663
0.00 (0.00 - 1.00) | 3.00 - 44.00 / 1,874
    Trade unions and professional associations 1 (0 - 4) | 11 - 36 / 189
0.00 (0.00 - 1.00) | 2.00 - 19.00 / 733
1 ROADMAPS: Median (Q1 - Q3) | 90% Centile - Max / N Non-missing
2 Kruskal-Wallis rank sum test

In Forum

Characteristic Has_forum
N = 5,5931		 No_forum
N = 6,8491		 p-value2
Category.of.registration

<0.001
    Academic institutions 135 (2.4%) 200 (2.9%)
    Associations and networks of public authorities 86 (1.5%) 81 (1.2%)
    Companies & groups 1,266 (23%) 2,026 (30%)
    Non-governmental organisations, platforms and networks and similar 1,900 (34%) 1,640 (24%)
    Other organisations, public or mixed entities 215 (3.8%) 249 (3.6%)
    Professional consultancies 135 (2.4%) 385 (5.6%)
    Think tanks and research institutions 286 (5.1%) 278 (4.1%)
    Trade and business associations 1,171 (21%) 1,429 (21%)
    Trade unions and professional associations 399 (7.1%) 561 (8.2%)
1 n (%)
2 Pearson’s Chi-squared test

En fonction des groupes experts

Characteristic
In_EG
No_EG
Has_forum
N = 1,2621		 No_forum
N = 8371		 p-value2 Has_forum
N = 4,1631		 No_forum
N = 5,7131		 p-value2
Category.of.registration

<0.001

<0.001
    Academic institutions 23 (1.8%) 44 (5.3%)
106 (2.5%) 149 (2.6%)
    Associations and networks of public authorities 19 (1.5%) 10 (1.2%)
62 (1.5%) 69 (1.2%)
    Companies & groups 225 (18%) 218 (26%)
1,005 (24%) 1,722 (30%)
    Non-governmental organisations, platforms and networks and similar 358 (28%) 191 (23%)
1,489 (36%) 1,373 (24%)
    Other organisations, public or mixed entities 36 (2.9%) 20 (2.4%)
174 (4.2%) 219 (3.8%)
    Professional consultancies 4 (0.3%) 9 (1.1%)
114 (2.7%) 336 (5.9%)
    Think tanks and research institutions 57 (4.5%) 33 (3.9%)
216 (5.2%) 235 (4.1%)
    Trade and business associations 431 (34%) 232 (28%)
715 (17%) 1,159 (20%)
    Trade unions and professional associations 109 (8.6%) 80 (9.6%)
282 (6.8%) 451 (7.9%)
1 n (%)
2 Pearson’s Chi-squared test 

Resized limits to included dashed line in forest panel
`height` was translated to `width`.

Intergroupe

Characteristic Has_intergroup
N = 2,7771		 No_intergroup
N = 9,6651		 p-value2
Category.of.registration

<0.001
    Academic institutions 48 (1.7%) 287 (3.0%)
    Associations and networks of public authorities 51 (1.8%) 116 (1.2%)
    Companies & groups 592 (21%) 2,700 (28%)
    Non-governmental organisations, platforms and networks and similar 1,040 (37%) 2,500 (26%)
    Other organisations, public or mixed entities 114 (4.1%) 350 (3.6%)
    Professional consultancies 87 (3.1%) 433 (4.5%)
    Think tanks and research institutions 113 (4.1%) 451 (4.7%)
    Trade and business associations 490 (18%) 2,110 (22%)
    Trade unions and professional associations 242 (8.7%) 718 (7.4%)
1 n (%)
2 Pearson’s Chi-squared test

En fonction des groupes experts

The following errors were returned during `tbl_strata()`:
✖ For variable `Category.of.registration` (`In_intergroup`) and "estimate",
  "p.value", "conf.low", and "conf.high" statistics: FEXACT erreur 7(location).
  LDSTP=18270 est trop petit pour ce problème, (pastp=87.1375,
  ipn_0:=ipoin[itp=261]=3697, stp[ipn_0]=81.8651). Augmentez la taille de
  l’environnement de travail ou considérez l’utilisation de
  ‘simulate.p.value=TRUE’.
Characteristic
In_EG
No_EG
Has_intergroup
N = 5891		 No_intergroup
N = 1,5101		 p-value Has_intergroup
N = 2,0791		 No_intergroup
N = 7,7971		 p-value2
Category.of.registration




<0.001
    Academic institutions 7 (1.2%) 60 (4.0%)
39 (1.9%) 216 (2.8%)
    Associations and networks of public authorities 10 (1.7%) 19 (1.3%)
39 (1.9%) 92 (1.2%)
    Companies & groups 103 (17%) 340 (23%)
463 (22%) 2,264 (29%)
    Non-governmental organisations, platforms and networks and similar 189 (32%) 360 (24%)
818 (39%) 2,044 (26%)
    Other organisations, public or mixed entities 16 (2.7%) 40 (2.6%)
89 (4.3%) 304 (3.9%)
    Professional consultancies 1 (0.2%) 12 (0.8%)
74 (3.6%) 376 (4.8%)
    Think tanks and research institutions 20 (3.4%) 70 (4.6%)
90 (4.3%) 361 (4.6%)
    Trade and business associations 177 (30%) 486 (32%)
303 (15%) 1,571 (20%)
    Trade unions and professional associations 66 (11%) 123 (8.1%)
164 (7.9%) 569 (7.3%)
1 n (%)
2 Pearson’s Chi-squared test 

Resized limits to included dashed line in forest panel
`height` was translated to `width`.