Country

Indicator

Value type

Values present

Government Effectiveness

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	0.795	-28%	41
Afghanistan	-1.99	+5.69%	197
Angola	-1.01	-1.67%	169
Albania	0.251	+289%	74
Andorra	1.48	-1.33%	19
United Arab Emirates	1.6	+23.5%	11
Argentina	-0.378	+33.2%	123
Armenia	-0.184	-41.5%	107
American Samoa	0.654	-2.02%	52
Antigua & Barbuda	0.38	-338%	68
Australia	1.59	+4.02%	14
Austria	1.33	-9.44%	22
Azerbaijan	-0.0326	-20.8%	94
Burundi	-1.2	-5.02%	176
Belgium	1.04	-15.5%	36
Benin	-0.219	+28.4%	110
Burkina Faso	-0.826	-3.16%	155
Bangladesh	-0.697	-8.65%	143
Bulgaria	0.0471	-117%	88
Bahrain	0.697	+8.98%	49
Bahamas	0.27	-36.2%	71
Bosnia & Herzegovina	-0.968	-9.12%	167
Belarus	-0.991	+16.4%	168
Belize	-0.376	+5.5%	122
Bermuda	1.2	-1.45%	26
Bolivia	-0.639	+13.2%	138
Brazil	-0.547	-7.05%	132
Barbados	0.38	-11.2%	67
Brunei	1.4	-1.44%	21
Bhutan	0.566	+0.893%	56
Botswana	0.431	-1.97%	61
Central African Republic	-1.74	+1.44%	191
Canada	1.52	-3.18%	18
Switzerland	2.13	+4.17%	2
Chile	0.718	+31.4%	46
China	0.679	+37.4%	51
Côte d’Ivoire	-0.36	+7.71%	121
Cameroon	-0.906	+2.81%	163
Congo - Kinshasa	-1.69	-3.3%	190
Congo - Brazzaville	-1.33	-2.61%	180
Colombia	-0.0759	-650%	99
Comoros	-1.54	-8.79%	186
Cape Verde	0.0137	-34.4%	90
Costa Rica	0.26	+395%	73
Cuba	-0.476	+35.5%	129
Cayman Islands	1.24	-1.99%	24
Cyprus	0.738	+1.01%	45
Czechia	1.11	+1.88%	32
Germany	1.19	-8.06%	28
Djibouti	-0.731	-7.03%	146
Dominica	0.261	-368%	72
Denmark	2.02	+1.28%	4
Dominican Republic	0.128	-287%	82
Algeria	-0.67	+30.5%	142
Ecuador	-0.494	+59.8%	131
Egypt	-0.24	-46.6%	111
Eritrea	-1.79	+3.61%	194
Spain	0.752	-18.3%	44
Estonia	1.26	-5.9%	23
Ethiopia	-0.769	-2.93%	149
Finland	1.74	-0.911%	7
Fiji	0.491	-17.4%	58
France	1.14	-1.77%	31
Micronesia (Federated States of)	0.232	-38.8%	76
Gabon	-0.78	+13.8%	151
United Kingdom	1.16	-6.22%	29
Georgia	0.791	+21.7%	42
Ghana	-0.0927	-12.9%	101
Guinea	-0.908	-0.687%	164
Gambia	-0.591	-9.64%	137
Guinea-Bissau	-1.43	-3.66%	183
Equatorial Guinea	-1.32	+1.8%	179
Greece	0.148	-66.9%	80
Grenada	-0.00606	-116%	93
Greenland	0.764	+0.971%	43
Guatemala	-0.897	-1.17%	161
Guam	0.654	-2.02%	52
Guyana	-0.294	+10.6%	116
Hong Kong SAR China	1.55	-2.19%	16
Honduras	-0.795	-8.31%	152
Croatia	0.713	+22.6%	47
Haiti	-2.23	-0.127%	198
Hungary	0.373	-30.2%	69
Indonesia	0.58	+33.1%	55
India	0.475	+28.6%	59
Ireland	1.59	+2.75%	13
Iran	-1.02	+15.8%	170
Iraq	-1.39	+6.67%	182
Iceland	1.56	-0.503%	15
Israel	1.15	-7.34%	30
Italy	0.611	+36.2%	54
Jamaica	0.407	-32.6%	63
Jordan	0.386	+99%	66
Japan	1.63	+0.732%	8
Kazakhstan	0.147	+2.84%	81
Kenya	-0.304	-7.87%	118
Kyrgyzstan	-0.891	+0.0244%	160
Cambodia	-0.306	-13%	119
Kiribati	-0.0966	-158%	103
St. Kitts & Nevis	0.38	-2.89%	68
South Korea	1.4	+4.13%	20
Kuwait	0.00754	-93.2%	92
Laos	-0.643	+7.73%	140
Lebanon	-1.58	+8.3%	187
Liberia	-1.36	-5.29%	181
Libya	-1.64	-6.83%	189
St. Lucia	0.0738	-176%	84
Liechtenstein	1.63	+9.41%	9
Sri Lanka	-0.252	-35.6%	113
Lesotho	-0.954	+7.39%	165
Lithuania	1.05	+5.65%	34
Luxembourg	1.91	+8.01%	5
Latvia	0.697	+0.726%	50
Macao SAR China	1.09	-1.38%	33
Morocco	-0.0359	-56.1%	95
Monaco	2.02	-1.18%	3
Moldova	-0.165	-46.4%	106
Madagascar	-1.02	+5.22%	171
Maldives	-0.128	+19%	105
Mexico	-0.198	-29.6%	108
Marshall Islands	0.07	-69.2%	85
North Macedonia	-0.0513	-36.9%	97
Mali	-1.14	-0.901%	173
Malta	0.397	-50.6%	64
Myanmar (Burma)	-1.75	+4.02%	192
Montenegro	0.247	-999%	75
Mongolia	-0.472	+11%	128
Mozambique	-0.717	-2.77%	145
Mauritania	-0.764	+5.31%	148
Mauritius	0.71	-7.31%	48
Malawi	-0.86	+0.736%	158
Malaysia	0.875	-11.8%	38
Namibia	0.0309	-55.4%	89
Niger	-0.641	+2.12%	139
Nigeria	-0.848	-18.6%	157
Nicaragua	-1.08	+4.64%	172
Netherlands	1.63	+2.95%	10
Norway	1.8	-7.3%	6
Nepal	-0.808	-12%	154
Nauru	-0.0678	-134%	98
New Zealand	1.53	+14.2%	17
Oman	0.275	+1,177%	70
Pakistan	-0.578	-7.51%	135
Panama	-0.21	+63.7%	109
Peru	-0.492	+23.5%	130
Philippines	0.154	+141%	79
Palau	0.442	-1.64%	60
Papua New Guinea	-0.802	-1%	153
Poland	0.421	+62.8%	62
Puerto Rico	-0.389	-19.4%	124
North Korea	-1.48	+1.39%	185
Portugal	0.988	-1.36%	37
Paraguay	-0.462	-24.7%	127
Palestinian Territories	-1.21	+34.1%	178
Qatar	1.2	+5.35%	27
Romania	-0.0927	+19,942%	102
Russia	-0.712	+2.33%	144
Rwanda	0.388	+67.3%	65
Saudi Arabia	0.796	+36.8%	40
Sudan	-1.98	+17.2%	195
Senegal	0.0697	+75.1%	86
Singapore	2.32	+8.08%	1
Solomon Islands	-0.734	+7.96%	147
Sierra Leone	-1.16	-1.24%	174
El Salvador	0.0504	-116%	87
San Marino	1.2	-32.2%	26
Somalia	-1.98	-2.33%	196
Serbia	0.00763	-88.3%	91
South Sudan	-2.33	-2.63%	200
São Tomé & Príncipe	-0.902	+8.06%	162
Suriname	-0.967	+2.38%	166
Slovakia	0.23	-39.3%	77
Slovenia	1.04	-2.74%	35
Sweden	1.6	+1.84%	12
Eswatini	-0.885	+13.3%	159
Seychelles	0.625	-5.61%	53
Syria	-1.79	+2.01%	193
Chad	-1.48	+3.9%	184
Togo	-0.578	-10.5%	136
Thailand	0.172	+34.7%	78
Tajikistan	-0.772	+2.01%	150
Turkmenistan	-1.21	+4.1%	177
Timor-Leste	-0.842	+9.32%	156
Tonga	-0.115	-30.8%	104
Trinidad & Tobago	-0.0436	+70.9%	96
Tunisia	-0.301	+14.4%	117
Turkey	-0.248	+24.2%	112
Tuvalu	-0.45	+43.3%	125
Tanzania	-0.455	-1.15%	126
Uganda	-0.55	-9.18%	133
Ukraine	-0.357	-28.1%	120
Uruguay	0.85	+0.0779%	39
United States	1.22	-3.05%	25
Uzbekistan	-0.284	-14%	115
St. Vincent & Grenadines	0.0738	-76.8%	84
Venezuela	-1.6	-5.32%	188
U.S. Virgin Islands	0.654	-2.02%	52
Vietnam	0.126	-27.6%	83
Vanuatu	-0.569	+5.59%	134
Samoa	0.493	+60%	57
Kosovo	-0.0801	-58.9%	100
Yemen	-2.28	+1.88%	199
South Africa	-0.257	+136%	114
Zambia	-0.659	+3.28%	141
Zimbabwe	-1.17	-6.46%	175

Government Effectiveness is a critical indicator that measures the quality of public services, the capacity of the civil service, and the degree of its independence from political pressures, as well as the quality of policy formulation and implementation. This indicator, part of the broader governance framework, provides an estimate of how effectively the government operates in a given country or territory.

The importance of Government Effectiveness cannot be overstated. A government that functions effectively fosters trust among its citizens, enhancing their engagement and compliance with public policies. It also plays a vital role in promoting economic growth and social stability. When a government is effective, it can efficiently allocate resources, implement laws and regulations, and improve the overall welfare of its population. In contrast, inefficiency and corruption can lead to a loss of faith in public institutions, hampering development efforts and leading to social unrest.

This indicator correlates with various other metrics that assess governance, such as the Rule of Law, Control of Corruption, and Regulatory Quality. Together, these dimensions help provide a comprehensive picture of governance quality in different regions. For instance, a high score in Government Effectiveness often aligns with high scores in Rule of Law and Regulatory Quality, indicating an environment conducive for businesses and civil society to thrive. Conversely, countries that struggle with effectiveness often face challenges in these other areas, as seen in nations with systemic corruption or bureaucratic inefficiency.

Several factors influence a government's effectiveness. Political stability plays a crucial role; countries with uninterrupted governance structures and consistent policies usually achieve better outcomes. Additionally, the education and professionalism of civil service employees significantly impact service delivery and policy implementation. Countries that invest in their human capital tend to yield higher scores on government effectiveness. Furthermore, the level of public engagement and transparency in governance can facilitate a more responsive and accountable administration.

Strategies to improve government effectiveness are multi-faceted. One essential approach is the investment in public sector reform, which can include modernizing administrative processes, adopting e-governance, and enhancing public service delivery mechanisms. For instance, countries can streamline bureaucratic procedures to reduce red tape, making it easier for citizens and businesses to interact with the government. Strengthening the capability of the civil service through training and professional development is another crucial strategy, ensuring that public servants are equipped with the necessary skills to serve the public effectively.

Moreover, establishing mechanisms for citizen participation in decision-making processes can enhance government responsiveness and accountability. Encouraging civic engagement through public consultations and feedback loops helps align government policies with the needs of society. Utilizing technology can also play a critical role in increasing transparency and public trust, through digital platforms that allow for open data and citizen engagement.

Despite its significance, the Government Effectiveness indicator is not without its flaws. The estimation relies on data which are subjective and can be influenced by political bias, particularly if assessments are based on expert surveys or perceptions. For example, certain countries might be rated higher due to international perception rather than actual efficacy in governance. Furthermore, the indicator often reflects a snapshot in time and may not account for sudden political shifts or emergencies that can quickly alter a government's effectiveness.

As of 2023, the median value for Government Effectiveness stands at -0.07, indicating that the average nation is operating at near stasis regarding government functionality. In contrast, the top-performing countries exhibit impressively high scores, with Singapore leading at 2.32. Singapore's effectiveness can be attributed to strong institutions, low corruption levels, and a rigorous focus on public service efficiency. Following Singapore, Switzerland and Monaco illustrate similar governance qualities, emphasizing stability, transparency, and public trust. Denmark and Luxembourg also reflect high effectiveness, showcasing a commitment to excellent civil services.

Conversely, the bottom tier of the effectiveness measure highlights areas with extreme challenges. South Sudan, at -2.33, and Yemen, at -2.28, suffer from ongoing conflicts and humanitarian crises that severely undermine governmental functionality. These nations exemplify how political instability, civil unrest, and lack of infrastructure can decimate government capabilities. Haiti, Afghanistan, and Somalia also reflect similar struggles, characterized by weak institutions, corruption, and societal fragmentation. Their positions on the effectiveness scale highlight urgent needs for international support, policy intervention, and domestic reform to address fundamental governance issues.

In conclusion, the Government Effectiveness indicator serves as a vital tool for assessing the performance of governments globally. It highlights the interconnectedness of governance dimensions and underscores the essential strategies that can lead to improvements. Understanding the nuances of this indicator informs not only policymakers but also civil society leaders aiming to foster better governance across various contexts. Continuous efforts to bolster government effectiveness could lead to profound improvements in public welfare, stability, and economic growth.

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 = 'GE.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 <- 'GE.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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Government Effectiveness

Year: 2023

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