corruption levels in European countries and Ukraine's ranking co… — Data analysis

corruption levels in European countries and Ukraine's ranking compared to other European countries

Model: DeepSeek V4.1 Flash Checked on: 09/10/2026

Corruption levels in Europe (Corruption Perceptions Index, Eurostat)

Important caveat on Ukraine first: The Eurostat Corruption Perceptions Index dataset¹ — the only corruption‑level dataset available in Eurostat or OECD — does not include Ukraine. Across the whole series (2012–2025, 44 reporting entities) there are zero observations for Ukraine²³. I also checked the OECD alternatives: Ukraine appears in the codelists of the OECD Public Integrity Indicators⁴ and the Government at a Glance integrity tables⁵⁶, but the latter return no data for Ukraine (they cover only ~33 OECD members)⁷⁸, and the Public Integrity Indicators measure institutional/anti‑corruption frameworks rather than a corruption level. So I cannot provide Ukraine's ranking relative to other European countries from the available data.

What the data does show: European countries, CPI 2025

The CPI score runs 0–100, where a higher score = less corrupt. Latest‑year (2025) ranking of the 39 European countries covered¹⁹:

Rank	Country	Score	Rank	Country	Score
1	🇩🇰 Denmark	89	21	🇪🇸 Spain	55
2	🇫🇮 Finland	88	22	🇨🇾 Cyprus	55
3	🇳🇴 Norway	81	23	🇵🇱 Poland	53
4	🇸🇪 Sweden	80	24	🇮🇹 Italy	53
5	🇨🇭 Switzerland	80	25	🇬🇷 Greece	50
6	🇳🇱 Netherlands	78	26	🇲🇹 Malta	49
7	🇱🇺 Luxembourg	78	27	🇸🇰 Slovakia	48
8	🇩🇪 Germany	77	28	🇭🇷 Croatia	47
9	🇮🇸 Iceland	77	29	🇲🇪 Montenegro	46
10	🇪🇪 Estonia	76	30	🇷🇴 Romania	45
11	🇮🇪 Ireland	76	31	🇽🇰 Kosovo	43
12	🇬🇧 United Kingdom	70	32	🇭🇺 Hungary	40
13	🇧🇪 Belgium	69	33	🇧🇬 Bulgaria	40
14	🇦🇹 Austria	69	34	🇲🇰 North Macedonia	40
15	🇫🇷 France	66	35	🇦🇱 Albania	39
16	🇱🇹 Lithuania	65	36	🇧🇦 Bosnia & Herzegovina	34
17	🇱🇻 Latvia	60	37	🇷🇸 Serbia	33
18	🇨🇿 Czechia	59	38	🇹🇷 Türkiye	31
19	🇸🇮 Slovenia	58	39	🇷🇺 Russia	22
20	🇵🇹 Portugal	56

For reference, the aggregates were EU‑27 average = 62 and euro area (EA21) = 63 in 2025¹⁹. The Nordic countries and Switzerland top the list; the Western Balkans, Türkiye and Russia sit at the bottom.

I generated two charts during the analysis²: a horizontal bar chart of the 2025 scores (cpi_2025_europe.svg) and a 2012–2025 trend line for selected countries plus the EU average (cpi_trend.svg).

Bottom line

I can describe corruption levels for European countries (above), where Denmark and Finland are the cleanest and Russia/Türkiye/Serbia the most corrupt in 2025.
I cannot place Ukraine in that ranking: Ukraine is absent from the Eurostat Corruption Perceptions Index dataset and no other Eurostat/OECD dataset provides a comparable corruption level for it.

Eurostat dataset: Corruption Perceptions Index

Code Run 2

Dataset: sdg_16_50

Program output

total rows: 616 n geos: 44
['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EA21', 'EE', 'EL', 'ES', 'EU27_2020', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT', 'JP', 'KR', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'RU', 'SE', 'SI', 'SK', 'TR', 'UK', 'US', 'XK']
U-codes: ['UK', 'US']

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
geos = sorted(df['geo'].unique())
print("total rows:", len(df), "n geos:", len(geos))
print(geos)
# any geo whose code starts with U
print("U-codes:", [g for g in geos if g.startswith('U')])

Code Run 1

Dataset: sdg_16_50

Dataset filters for `sdg_16_50`

Filter	Value(s)
geo	UA

Program output

UA rows: 0
Any geo containing U: []
(0, 5)

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print("UA rows:", (df['geo']=='UA').sum())
print("Any geo containing U:", [g for g in df['geo'].unique() if 'U' in g])
print(df.shape)

OECD dataset: Public Integrity Indicators
OECD dataset: Perception of public sector integrity - Government at a glance indicators, 2025 edition
OECD dataset: Trust, security and dignity - government at a glance indicators, 2025 edition

Code Run 4

Dataset: sdg_16_50

Program output

2025 values:
      geo  value
       DK     89
       FI     88
       NO     81
       CH     80
       SE     80
       LU     78
       NL     78
       DE     77
       IS     77
       IE     76
       EE     76
       JP     71
       UK     70
       AT     69
       BE     69
       FR     66
       LT     65
       US     64
       KR     63
     EA21     63
EU27_2020     62
       LV     60
       CZ     59
       SI     58
       PT     56
       ES     55
       CY     55
       IT     53
       PL     53
       EL     50
       MT     49
       SK     48
       HR     47
       ME     46
       RO     45
       XK     43
       HU     40
       BG     40
       MK     40
       AL     39
       BA     34
       RS     33
       TR     31
       RU     22

EU27_2020 2025: [62]
EA21 2025: [63]

European countries ranking 2025 (higher score = less corrupt):
 rank geo  value
    1  DK     89
    2  FI     88
    3  NO     81
    4  SE     80
    5  CH     80
    6  NL     78
    7  LU     78
    8  DE     77
    9  IS     77
   10  EE     76
   11  IE     76
   12  UK     70
   13  BE     69
   14  AT     69
   15  FR     66
   16  LT     65
   17  LV     60
   18  CZ     59
   19  SI     58
   20  PT     56
   21  ES     55
   22  CY     55
   23  PL     53
   24  IT     53
   25  EL     50
   26  MT     49
   27  SK     48
   28  HR     47
   29  ME     46
   30  RO     45
   31  XK     43
   32  HU     40
   33  BG     40
   34  MK     40
   35  AL     39
   36  BA     34
   37  RS     33
   38  TR     31
   39  RU     22

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
df['time'] = df['time'].astype(int)

# Aggregates vs countries
aggs = ['EA21','EU27_2020']
non_european = ['JP','KR','US']

# Latest year snapshot
latest = df[df['time']==2025].copy()
print("2025 values:")
print(latest.sort_values('value', ascending=False)[['geo','value']].to_string(index=False))
print()
print("EU27_2020 2025:", latest.loc[latest.geo=='EU27_2020','value'].values)
print("EA21 2025:", latest.loc[latest.geo=='EA21','value'].values)

# Full 2025 ranking of European COUNTRIES (exclude aggregates & non-European)
eur = latest[~latest['geo'].isin(aggs+non_european)].copy()
eur = eur.sort_values('value', ascending=False).reset_index(drop=True)
eur['rank'] = range(1, len(eur)+1)
print("\nEuropean countries ranking 2025 (higher score = less corrupt):")
print(eur[['rank','geo','value']].to_string(index=False))

Code Run 3

Dataset: DSD_GOV_INT@DF_GOV_INT_2025

Dataset filters for `DSD_GOV_INT@DF_GOV_INT_2025`

Filter	Value(s)
CATEGORY	INT

Program output

shape: (432, 10)
measures: <ArrowStringArray>
['TRUST_BIR', 'TRUST_FAV']
Length: 2, dtype: str
n areas: 33
['AUS', 'AUT', 'BEL', 'CAN', 'CHE', 'CHL', 'COL', 'CRI', 'CZE', 'DEU', 'DNK', 'ESP', 'EST', 'FIN', 'FRA', 'GBR', 'GRC', 'IRL', 'ISL', 'ITA', 'JPN', 'KOR', 'LUX', 'LVA', 'MEX', 'NLD', 'NOR', 'NZL', 'OECD_REP', 'PRT', 'SVK', 'SVN', 'SWE']
UKR present: False

Code


import pandas as pd
df = pd.read_csv(DATASET_FILE)
print("shape:", df.shape)
print("measures:", df['MEASURE'].unique())
print("n areas:", df['REF_AREA'].nunique())
print(sorted(df['REF_AREA'].unique()))
print("UKR present:", 'UKR' in df['REF_AREA'].values)

Code Run 5

Dataset: sdg_16_50

Images

cpi_2025_europe.svg

cpi_trend.svg

Program output

saved cpi_2025_europe.svg
saved cpi_trend.svg
Ukraine rows in full dataset: 0

Code


import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

df = pd.read_csv(DATASET_FILE)
df['time'] = df['time'].astype(int)

aggs = ['EA21','EU27_2020']
non_european = ['JP','KR','US']

latest = df[df['time']==2025].copy()
eur = latest[~latest['geo'].isin(aggs+non_european)].copy().sort_values('value')

eu_avg = latest.loc[latest.geo=='EU27_2020','value'].iloc[0]

fig, ax = plt.subplots(figsize=(10, 11))
colors = ['#2b8cbe' if v>=eu_avg else '#e34a33' for v in eur['value']]
bars = ax.barh(eur['geo'], eur['value'], color=colors)
ax.axvline(eu_avg, color='black', linestyle='--', linewidth=1.2, label=f'EU27 average ({eu_avg})')
ax.set_xlabel('Corruption Perceptions Index score (2025)  —  higher = less corrupt')
ax.set_title('Corruption Perceptions Index 2025\nEuropean countries covered by Eurostat')
ax.set_xlim(0, 100)
for b, v in zip(bars, eur['value']):
    ax.text(v+0.7, b.get_y()+b.get_height()/2, str(v), va='center', fontsize=8)
ax.legend(loc='lower right')
plt.tight_layout()
plt.savefig('cpi_2025_europe.svg')
plt.close()
print("saved cpi_2025_europe.svg")

# Time series: EU average and a few illustrative countries + note Ukraine absence
fig, ax = plt.subplots(figsize=(11,6))
for geo in ['EU27_2020','DK','DE','FR','PL','HU','BG','RU']:
    sub = df[df.geo==geo].sort_values('time')
    ax.plot(sub['time'], sub['value'], marker='o', ms=3, label=geo)
ax.set_xlabel('Year'); ax.set_ylabel('CPI score (higher = less corrupt)')
ax.set_title('Corruption Perceptions Index over time (2012–2025)')
ax.legend(ncol=2, fontsize=8); ax.grid(alpha=0.3)
plt.tight_layout(); plt.savefig('cpi_trend.svg'); plt.close()
print("saved cpi_trend.svg")

# Confirm no Ukraine in any year
print("Ukraine rows in full dataset:", (df.geo=='UA').sum())

corruption levels in European countries and Ukraine's ranking compared to other European countries

Corruption levels in Europe (Corruption Perceptions Index, Eurostat)

What the data does show: European countries, CPI 2025

Bottom line

Program output

Code

Dataset filters for sdg_16_50

Program output

Code

Program output

Code

Dataset filters for DSD_GOV_INT@DF_GOV_INT_2025

Program output

Code

Images

Program output

Code

Dataset filters for `sdg_16_50`

Dataset filters for `DSD_GOV_INT@DF_GOV_INT_2025`