Country

Indicator

Value type

Values present

Control of Corruption

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	0.709	-6.27%	44
Afghanistan	-1.15	-2.43%	169
Angola	-0.61	-0.401%	136
Albania	-0.332	-18.6%	106
Andorra	1.25	-1.49%	27
United Arab Emirates	1.07	-7.09%	31
Argentina	-0.361	-19.3%	109
Armenia	0.0584	+109%	79
American Samoa	1.25	-1.49%	27
Antigua & Barbuda	0.327	+5.17%	68
Australia	1.78	+0.954%	10
Austria	1.13	-9.93%	29
Azerbaijan	-1.2	+14.8%	172
Burundi	-1.56	+2.49%	191
Belgium	1.34	-10.6%	24
Benin	-0.0496	-44.8%	87
Burkina Faso	-0.17	+51.9%	94
Bangladesh	-1.12	+4.19%	167
Bulgaria	-0.138	-12.9%	93
Bahrain	0.178	+27.4%	74
Bahamas	1.27	+0.833%	26
Bosnia & Herzegovina	-0.582	-15%	133
Belarus	-0.665	+14.7%	141
Belize	-0.226	-4.56%	96
Bermuda	1.25	-1.49%	27
Bolivia	-0.838	-5.78%	152
Brazil	-0.504	-11.1%	125
Barbados	1.34	+4.66%	23
Brunei	1.28	+8.75%	25
Bhutan	1.53	+1.1%	20
Botswana	0.686	+3.61%	48
Central African Republic	-1.31	+5.24%	179
Canada	1.67	+0.779%	12
Switzerland	2.02	+0.629%	7
Chile	0.969	+0.0496%	34
China	-0.00537	-133%	84
Côte d’Ivoire	-0.313	-7.59%	104
Cameroon	-1.16	+2.91%	170
Congo - Kinshasa	-1.48	-4.17%	187
Congo - Brazzaville	-1.35	-1.07%	181
Colombia	-0.309	-14.6%	103
Comoros	-0.99	-20.5%	157
Cape Verde	0.985	+2.6%	33
Costa Rica	0.654	+40.3%	50
Cuba	-0.0551	-20.5%	88
Cayman Islands	0.438	-4.39%	63
Cyprus	0.333	-21.1%	67
Czechia	0.768	+15.8%	41
Germany	1.66	-8.48%	13
Djibouti	-0.781	-1.56%	148
Dominica	0.545	+0.913%	58
Denmark	2.38	-1.11%	1
Dominican Republic	-0.449	+2.2%	118
Algeria	-0.589	-7.64%	134
Ecuador	-0.647	+2.71%	139
Egypt	-0.748	+10.1%	146
Eritrea	-1.48	+15.1%	186
Spain	0.629	-8.18%	52
Estonia	1.54	-0.427%	19
Ethiopia	-0.465	+7.34%	119
Finland	2.22	-1.13%	2
Fiji	0.506	+30.2%	61
France	1.18	-5.92%	28
Micronesia (Federated States of)	0.784	+62.2%	38
Gabon	-1.02	-2.63%	160
United Kingdom	1.48	-8.59%	21
Georgia	0.618	-0.439%	53
Ghana	-0.099	+53.8%	90
Guinea	-0.898	-7.63%	155
Gambia	-0.281	+2.03%	100
Guinea-Bissau	-1.18	-0.598%	171
Equatorial Guinea	-1.57	-1.97%	192
Greece	0.0978	+160%	77
Grenada	0.558	+15.3%	56
Greenland	1.03	-0.0795%	32
Guatemala	-1.12	-7.32%	168
Guam	1.25	-1.49%	27
Guyana	-0.384	+29.9%	112
Hong Kong SAR China	1.63	+0.893%	15
Honduras	-1.11	+10.9%	166
Croatia	0.182	+23.1%	73
Haiti	-1.44	-1.05%	185
Hungary	-0.000498	-99.5%	83
Indonesia	-0.487	+11.8%	121
India	-0.366	+13.6%	110
Ireland	1.58	-5.92%	16
Iran	-1.23	+9.34%	176
Iraq	-1.32	+8.87%	180
Iceland	1.55	-1.03%	18
Israel	0.829	+6.19%	37
Italy	0.55	+4.25%	57
Jamaica	-0.112	+381%	91
Jordan	0.0887	+21.4%	78
Japan	1.4	-9.37%	22
Kazakhstan	-0.267	+43.6%	98
Kenya	-0.771	+1.5%	147
Kyrgyzstan	-1.22	-0.756%	174
Cambodia	-1.3	+4.64%	178
Kiribati	0.412	+74.1%	65
St. Kitts & Nevis	0.413	+6.02%	64
South Korea	0.894	+19.5%	35
Kuwait	0.207	+62.3%	72
Laos	-0.974	+0.834%	156
Lebanon	-1.23	+8.85%	175
Liberia	-0.888	-4.01%	154
Libya	-1.53	-0.0838%	190
St. Lucia	0.544	-6.09%	59
Liechtenstein	1.71	-0.275%	11
Sri Lanka	-0.384	+0.194%	113
Lesotho	-0.509	+38.2%	127
Lithuania	0.782	+4.63%	39
Luxembourg	1.93	+2.29%	8
Latvia	0.698	+0.621%	46
Macao SAR China	0.849	-2.24%	36
Morocco	-0.536	+38.4%	128
Monaco	1.25	-1.49%	27
Moldova	-0.28	-18.1%	99
Madagascar	-1	+3.12%	159
Maldives	-0.397	+19.3%	114
Mexico	-1.02	+0.839%	161
Marshall Islands	0.412	+6.75%	65
North Macedonia	-0.349	+7.6%	108
Mali	-0.862	+2.1%	153
Malta	0.11	-54%	76
Myanmar (Burma)	-1.22	+5.64%	173
Montenegro	-0.0793	-33.2%	89
Mongolia	-0.493	-12.2%	123
Mozambique	-0.828	-0.875%	151
Mauritania	-0.823	+4.04%	150
Mauritius	0.453	+7.81%	62
Malawi	-0.603	-0.992%	135
Malaysia	0.296	+18.8%	69
Namibia	0.11	-48.8%	75
Niger	-0.564	-3.58%	131
Nigeria	-1.04	-7.85%	162
Nicaragua	-1.39	+7.83%	183
Netherlands	1.87	-2.61%	9
Norway	2.11	+2.22%	3
Nepal	-0.508	-4.66%	126
Nauru	0.589	-2.19%	54
New Zealand	2.08	-3.66%	4
Oman	0.215	+309%	71
Pakistan	-0.999	+24.2%	158
Panama	-0.628	-1.07%	138
Peru	-0.721	-10.8%	144
Philippines	-0.538	-0.66%	129
Palau	0.589	-2.19%	54
Papua New Guinea	-0.657	-5.62%	140
Poland	0.565	+11.6%	55
Puerto Rico	0.0196	-82.8%	82
North Korea	-1.58	-1.34%	193
Portugal	0.696	-4.47%	47
Paraguay	-1.06	-3.01%	164
Palestinian Territories	-0.676	-3.78%	142
Qatar	0.702	-12.7%	45
Romania	0.0405	+146%	81
Russia	-1.1	+12.7%	165
Rwanda	0.672	+19.6%	49
Saudi Arabia	0.536	+50%	60
Sudan	-1.5	+6.25%	189
Senegal	0.0578	-539%	80
Singapore	2.04	-2.6%	5
Solomon Islands	-0.135	-27%	92
Sierra Leone	-0.575	+2.62%	132
El Salvador	-0.561	-12.6%	130
San Marino	1.25	-1.49%	27
Somalia	-1.73	-3.33%	196
Serbia	-0.447	-2.05%	117
South Sudan	-1.97	+6.13%	198
São Tomé & Príncipe	0.36	+18.1%	66
Suriname	-0.404	-0.149%	115
Slovakia	0.293	+37.2%	70
Slovenia	0.777	+1.22%	40
Sweden	2.03	-1.61%	6
Eswatini	-0.735	+1.2%	145
Seychelles	1.63	-4.1%	14
Syria	-1.75	-1.46%	197
Chad	-1.48	-0.412%	188
Togo	-0.619	-2.89%	137
Thailand	-0.489	+8.09%	122
Tajikistan	-1.38	-3.87%	182
Turkmenistan	-1.42	-0.94%	184
Timor-Leste	-0.227	-14.6%	97
Tonga	-0.308	-26.7%	102
Trinidad & Tobago	-0.374	-1.39%	111
Tunisia	-0.342	+14%	107
Turkey	-0.502	+7.35%	124
Tuvalu	0.646	-0.942%	51
Tanzania	-0.32	-9.86%	105
Uganda	-1.04	-1.75%	163
Ukraine	-0.686	+8.22%	143
Uruguay	1.57	-2.27%	17
United States	1.12	+1.75%	30
Uzbekistan	-0.808	+6.57%	149
St. Vincent & Grenadines	0.737	-2.54%	42
Venezuela	-1.69	+2.33%	195
U.S. Virgin Islands	-0.0185	+214%	86
Vietnam	-0.416	+44.8%	116
Vanuatu	-0.0149	-48%	85
Samoa	0.727	+31.2%	43
Kosovo	-0.185	-29.8%	95
Yemen	-1.65	-1.84%	194
South Africa	-0.285	-13.8%	101
Zambia	-0.478	-5.39%	120
Zimbabwe	-1.26	+0.0989%	177

The "Control of Corruption" indicator represents an important measure of governance that assesses the extent to which public power is exercised for private gain, including both petty and grand forms of corruption. It derives from expert assessments and surveys that compile perceptions of corruption within various public institutions and mechanisms. In 2023, the median value of this indicator stood at -0.23, reflecting a global challenge where many nations continue to grapple with corruption at varying degrees.

The significance of the Control of Corruption indicator cannot be overstated. Corruption undermines economic development, distorts public spending, erodes trust in institutions, and deepens inequalities. By minimizing corrupt practices, nations can improve their public services, foster a conducive business environment, and ultimately enhance the quality of life for their citizens. Therefore, tracking this indicator is critical for policymakers and stakeholders aiming to promote good governance and transparency.

This indicator is closely related to various other governance indicators such as "Government Effectiveness," "Political Stability," and "Rule of Law." For instance, a country with high levels of corruption is likely to experience governmental inefficiencies, which can negatively impact its political stability. Additionally, improvements in the control of corruption are often accompanied by enhanced rule of law, as both promote accountability and transparency within a government structure.

Several factors influence a country's control of corruption. Political culture plays a significant role; in societies where corruption is normalized or overlooked, it becomes deeply entrenched. Moreover, the strength and independence of judicial and legislative bodies can either mitigate or exacerbate corruption levels. Economic factors also contribute—countries with greater wealth and resources may have more means to combat corruption, while those struggling economically might find it harder to hold corrupt officials accountable.

Strategies to improve control of corruption are multifaceted and require long-term commitment from governments, civil society, and international organizations. Enhancing transparency is a critical step, which includes implementing open government initiatives where citizens have access to information regarding public spending, procurements, and decision-making processes. Empowering judicial systems by providing them with the necessary independence and resources can help in holding individuals accountable. Furthermore, adopting stringent laws to prevent and punish corrupt practices can create a deterrent effect.

Education also plays a fundamental role in fighting corruption. By embedding anti-corruption themes into educational curricula and promoting civic awareness, future generations may possess a strengthened ethical framework to reject corrupt practices altogether. Moreover, the involvement of civil society in monitoring government actions provides an additional layer of scrutiny that can pressure institutions to remain accountable and operate transparently.

Nevertheless, there are notable flaws within the indicator itself. One significant issue is that it relies heavily on perceptions rather than objective measures of corruption, making it susceptible to biases based on individual experiences and societal narratives. In areas where media freedom is restricted, reports of corruption may be less visible or significantly downplayed, leading to an underestimated prevalence of such practices. Furthermore, the indicator’s focus on public corruption may overlook corruption in private sectors or informal economies, which can be rampant in various contexts.

Regarding the recent data from 2023, the top-rated countries for control of corruption were Denmark (2.38), Finland (2.22), Norway (2.11), New Zealand (2.08), and Singapore (2.04). These nations exhibit strong governance frameworks, high levels of public trust, and robust enforcement mechanisms against corruption. For instance, Denmark's long-standing tradition of transparency and accountability, alongside its strong welfare system, minimizes opportunities for corruption. Similarly, Singapore’s effective anti-corruption agencies and strict penalties for corrupt behavior contribute to its low corruption perception.

In stark contrast, countries at the lower end of the scale, including South Sudan (-1.97), Syria (-1.75), Somalia (-1.73), Venezuela (-1.69), and Yemen (-1.65), demonstrate the severity of corruption and its deep ramifications on societal stability and growth. South Sudan, facing ongoing conflict, struggles with governance challenges that further deteriorate its control over corruption. Somalia, historically plagued by instability, deals with systemic corruption at various levels of governance that impede any meaningful progress in public sector reform. Similarly, Venezuela’s economic crisis has been compounded by corruption, eroding public trust and damaging the political landscape.

In conclusion, the Control of Corruption indicator serves as a crucial barometer of governance quality across nations. Its implications extend beyond the mere prevalence of corrupt practices, influencing economic health, political legitimacy, and social equity. By comprehensively understanding its significance, the interrelations with other governance metrics, the influencing factors, and the implementation of effective strategies, stakeholders may work towards promoting environments that curtail corruption. Addressing the flaws inherent in corrupt practices measurement will further refine efforts, enabling a clearer picture of where to direct anti-corruption initiatives while fostering nations that align with the ideals of transparency and accountability.

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'CC.EST'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'CC.EST'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

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Control of Corruption

Year: 2023

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