Country

Indicator

Value type

Values present

Regulatory Quality

Source: worldbank.org, 07.12.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	0.947	-2.11%	38
Afghanistan	-1.27	-0.423%	181
Angola	-0.761	+25.5%	153
Albania	0.172	+7.91%	79
Andorra	1.35	-3.42%	22
United Arab Emirates	1.04	+0.735%	35
Argentina	-0.483	-30.8%	126
Armenia	0.0476	-350%	90
American Samoa	0.512	-6.21%	59
Antigua & Barbuda	0.546	+88.9%	55
Australia	1.94	+2.5%	2
Austria	1.36	+6.09%	21
Azerbaijan	-0.109	+5.29%	97
Burundi	-0.986	+4.04%	168
Belgium	1.17	-6.76%	28
Benin	-0.296	-15.1%	115
Burkina Faso	-0.486	+3.7%	127
Bangladesh	-0.914	-1.46%	161
Bulgaria	0.408	+27.2%	64
Bahrain	1.08	+11.1%	32
Bahamas	0.00718	-105%	92
Bosnia & Herzegovina	-0.141	-10.4%	102
Belarus	-1.42	+6.63%	185
Belize	-0.431	-1.87%	125
Bermuda	0.931	-4.2%	40
Bolivia	-1.18	-2.76%	176
Brazil	-0.296	+34.5%	116
Barbados	0.503	+5.58%	60
Brunei	0.977	-8.45%	37
Bhutan	-0.401	+4.99%	123
Botswana	0.497	-19.9%	61
Central African Republic	-1.47	-0.391%	188
Canada	1.64	-1.93%	12
Switzerland	1.73	+6.74%	10
Chile	0.926	-5.38%	41
China	-0.357	-14.8%	119
Côte d’Ivoire	-0.116	-23.6%	98
Cameroon	-0.913	+1.62%	160
Congo - Kinshasa	-1.41	-4.57%	184
Congo - Brazzaville	-1.24	-1.75%	179
Colombia	0.101	-28.1%	87
Comoros	-1.24	+3.16%	178
Cape Verde	0.243	-7.88%	76
Costa Rica	0.544	-4.15%	56
Cuba	-1.5	+8.65%	190
Cayman Islands	1.07	-1.73%	33
Cyprus	0.778	+0.582%	43
Czechia	1.3	-5.87%	24
Germany	1.46	-4.29%	18
Djibouti	-0.933	+3.7%	164
Dominica	0.308	-9.74%	73
Denmark	1.84	-0.196%	5
Dominican Republic	0.171	+146%	80
Algeria	-0.948	-10.9%	167
Ecuador	-0.718	+56.2%	151
Egypt	-0.665	-6.79%	144
Eritrea	-2.36	-0.27%	200
Spain	0.694	-13%	46
Estonia	1.43	-8.3%	19
Ethiopia	-1.02	+7.66%	171
Finland	1.77	-0.787%	8
Fiji	-0.0902	+49.8%	96
France	1.15	-2.98%	29
Micronesia (Federated States of)	-0.58	+3.36%	136
Gabon	-0.705	+0.365%	148
United Kingdom	1.54	-1.91%	16
Georgia	0.947	-8.3%	39
Ghana	-0.181	+1.03%	105
Guinea	-1.08	+2.47%	173
Gambia	-0.64	-10.5%	141
Guinea-Bissau	-1.25	-5.42%	180
Equatorial Guinea	-1.47	-1.26%	189
Greece	0.579	+24.7%	54
Grenada	0.315	-20.1%	72
Greenland	1.21	+0.31%	26
Guatemala	-0.29	-0.499%	113
Guam	0.512	-6.21%	59
Guyana	-0.563	+6.7%	134
Hong Kong SAR China	1.6	+0.914%	14
Honduras	-0.551	+13.5%	133
Croatia	0.644	+28.1%	51
Haiti	-1.39	+5.8%	183
Hungary	0.318	-21.7%	70
Indonesia	0.301	+46.3%	74
India	-0.137	+172%	101
Ireland	1.75	+6.7%	9
Iran	-1.69	+5.95%	192
Iraq	-1.44	+21.8%	187
Iceland	1.28	-1.75%	25
Israel	1.12	-7%	31
Italy	0.644	+26.1%	50
Jamaica	0.098	-48.5%	88
Jordan	0.218	+38.8%	77
Japan	1.47	+2.12%	17
Kazakhstan	0.066	-687%	89
Kenya	-0.391	+2.62%	121
Kyrgyzstan	-0.617	-2.51%	139
Cambodia	-0.685	-3.55%	147
Kiribati	-0.279	+6.51%	111
St. Kitts & Nevis	0.542	-7.27%	57
South Korea	1.12	-1.98%	30
Kuwait	0.321	+52.8%	68
Laos	-0.928	-6.01%	163
Lebanon	-1.01	-10.4%	170
Liberia	-0.924	-5.83%	162
Libya	-1.95	-6.86%	196
St. Lucia	0.397	-9.57%	66
Liechtenstein	1.62	-0.498%	13
Sri Lanka	-0.508	-22.3%	131
Lesotho	-0.59	-4.09%	138
Lithuania	1.34	+3.01%	23
Luxembourg	1.93	+5.19%	3
Latvia	1.17	+0.114%	27
Macao SAR China	1.79	-1.66%	6
Morocco	-0.0642	-26.1%	94
Monaco	1.35	-3.42%	22
Moldova	0.105	+3.64%	86
Madagascar	-0.8	-1.96%	156
Maldives	-0.672	+2.24%	145
Mexico	-0.172	+16%	103
Marshall Islands	-0.423	+7.2%	124
North Macedonia	0.427	-5.66%	63
Mali	-0.68	+7.17%	146
Malta	0.687	+1.97%	47
Myanmar (Burma)	-1.44	+16.3%	186
Montenegro	0.374	-31.1%	67
Mongolia	-0.176	-35.2%	104
Mozambique	-0.709	-2.44%	149
Mauritania	-0.997	-6.13%	169
Mauritius	1.06	-8.72%	34
Malawi	-0.772	+4.4%	154
Malaysia	0.661	+2.62%	49
Namibia	-0.0623	+315%	93
Niger	-0.817	+13.3%	157
Nigeria	-0.937	-19%	165
Nicaragua	-0.878	-4.01%	158
Netherlands	1.79	+4.6%	7
Norway	1.6	+5.37%	15
Nepal	-0.661	+1.72%	143
Nauru	0.00959	-50.2%	91
New Zealand	1.91	+2.41%	4
Oman	0.435	+0.437%	62
Pakistan	-0.901	+1.65%	159
Panama	0.119	-6.57%	85
Peru	0.292	+40.1%	75
Philippines	0.164	+162%	81
Palau	0.401	-9.4%	65
Papua New Guinea	-0.643	-5.79%	142
Poland	0.78	+8.77%	42
Puerto Rico	0.638	-24.8%	52
North Korea	-2.39	-0.279%	201
Portugal	0.755	-0.959%	44
Paraguay	-0.0789	-53.5%	95
Palestinian Territories	-0.279	+657%	112
Qatar	0.977	+12.2%	36
Romania	0.319	-12.5%	69
Russia	-1.12	-1.74%	174
Rwanda	0.125	-23.5%	84
Saudi Arabia	0.517	+23.5%	58
Sudan	-1.6	+1.38%	191
Senegal	-0.345	+16.4%	118
Singapore	2.31	+4.25%	1
Solomon Islands	-0.78	+1.78%	155
Sierra Leone	-1.06	+0.815%	172
El Salvador	-0.308	-33.5%	117
San Marino	0.931	+70.6%	40
Somalia	-1.88	-1.07%	195
Serbia	0.138	+0.601%	83
South Sudan	-2.12	+0.63%	199
São Tomé & Príncipe	-0.94	+5.62%	166
Suriname	-0.728	-7.83%	152
Slovakia	0.602	-29.2%	53
Slovenia	0.731	+5.25%	45
Sweden	1.72	+2.17%	11
Eswatini	-0.717	-0.212%	150
Seychelles	0.211	-35.6%	78
Syria	-1.81	-0.756%	193
Chad	-1.16	+0.615%	175
Togo	-0.493	-12.4%	129
Thailand	0.158	-4.1%	82
Tajikistan	-1.18	-1.65%	177
Turkmenistan	-2.07	+0.0502%	198
Timor-Leste	-0.488	+3.2%	128
Tonga	-0.393	+13%	122
Trinidad & Tobago	-0.131	+76.4%	100
Tunisia	-0.619	+49.5%	140
Turkey	-0.23	-6.18%	108
Tuvalu	-0.191	+31.5%	106
Tanzania	-0.588	+4.41%	137
Uganda	-0.517	+5.51%	132
Ukraine	-0.266	-19.7%	109
Uruguay	0.675	-5.39%	48
United States	1.39	-2.16%	20
Uzbekistan	-0.577	+4.82%	135
St. Vincent & Grenadines	0.318	-8.96%	71
Venezuela	-2.03	-0.958%	197
U.S. Virgin Islands	1.35	-3.42%	22
Vietnam	-0.383	-11%	120
Vanuatu	-0.12	+31.6%	99
Samoa	-0.271	-0.868%	110
Kosovo	-0.295	-24.1%	114
Yemen	-1.84	-3.89%	194
South Africa	-0.224	+20.8%	107
Zambia	-0.498	-5.85%	130
Zimbabwe	-1.34	-6.14%	182

Regulatory Quality is a significant indicator under the broader framework of governance metrics that measures the ability of the government to formulate and implement sound policies and regulations that allow the private sector to flourish. The estimate of Regulatory Quality is an essential aspect of how nations conduct their affairs regarding business, investment, and social welfare. As of the latest year, 2023, the median value of Regulatory Quality stands at -0.12, indicating that many countries are facing challenges in this area, with performance varying widely across the globe.

The importance of Regulatory Quality cannot be overstated. It plays a crucial role in attracting foreign investments, promoting economic growth, and ensuring a conducive environment for entrepreneurship. Nations with high regulatory quality often experience higher levels of innovation, job creation, and overall economic stability. Conversely, countries with lower regulatory quality levels tend to struggle with inefficiency, corruption, and stagnation, which can lead to broader societal issues.

Comparing the top five and bottom five areas in terms of Regulatory Quality provides a clearer picture of the disparities across global governance. The top spots are filled by countries like Singapore (2.31), Australia (1.94), Luxembourg (1.93), New Zealand (1.91), and Denmark (1.84). These nations exemplify effective governance practices characterized by transparency, inclusivity, and efficiency. For instance, Singapore's robust regulatory framework, which promotes entrepreneurship while ensuring compliance, has fostered a vibrant business environment marked by innovation and economic resilience.

In stark contrast, the bottom five areas—North Korea (-2.39), Eritrea (-2.36), South Sudan (-2.12), Turkmenistan (-2.07), and Venezuela (-2.03)—represent the most extreme challenges regarding regulatory quality. These countries are often hampered by autocratic governance, bureaucratic inefficiencies, and a lack of transparency, all of which stifle economic growth and diminish the prospects for private-sector development. For example, North Korea’s tightly controlled economy results in minimal private enterprise and widespread poverty, showcasing how poor regulatory quality can lead to dire social and economic outcomes.

The relationship between Regulatory Quality and other governance indicators, such as Control of Corruption and Political Stability, is fundamental. Improved regulatory frameworks typically correlate with lower levels of corruption, as transparent processes and accountability mechanisms discourage malfeasance. Additionally, political stability fosters a predictable environment, essential for regulatory quality. When governments are unstable, the regulatory environment tends to be volatile, creating uncertainty for businesses. Thus, enhancing Regulatory Quality is often inextricably linked to overall governance improvements.

Several factors affect Regulatory Quality at both national and local levels. Economic factors, such as GDP growth and foreign investment levels, can influence how governments prioritize regulatory improvements. Sociopolitical dynamics, including the level of civic engagement and public trust in institutions, also play crucial roles. Countries with active civil societies often hold their governments accountable, leading to better regulatory outcomes. Furthermore, international pressure and collaboration may also contribute; nations seeking to improve their standing on global indices might undertake reforms to enhance their regulatory frameworks.

To enhance Regulatory Quality, various strategies and solutions can be pursued. One significant approach is the simplification of regulatory processes, making it easier for businesses to comply with necessary rules without excessive bureaucracy. Streamlining regulations can drastically improve efficiency and encourage entrepreneurship. Additionally, fostering transparency through the use of technology, such as e-governance platforms, can enhance public access to regulatory information and promote accountability.

Investing in training and capacity-building for regulatory bodies is also vital. Empowering these institutions with the necessary skills and resources to implement and enforce regulations effectively can lead to significant improvements in regulatory quality. Encouraging stakeholder consultations and public participation in the regulatory process can also enhance outcomes, as this approach allows for diverse perspectives and fosters trust between the government and the populace.

However, it is crucial to recognize that Regulatory Quality is not without its flaws. In some instances, reforms aimed at improving regulatory frameworks can lead to rigid or overly complex regulations that may inadvertently stifle innovation and entrepreneurship. Additionally, the focus on international rankings may lead governments to prioritize superficial compliance over meaningful regulatory improvements. This could result in a scenario where regulatory frameworks appear robust on paper but lack the necessary mechanisms for enforcement and accountability in practice.

In conclusion, Regulatory Quality plays a foundational role in the overall governance landscape and significantly influences economic growth and societal well-being. The stark disparities between the top and bottom areas underscore the need for concerted efforts to enhance regulatory frameworks worldwide. Addressing the challenges associated with Regulatory Quality requires a multifaceted approach, involving governmental processes, stakeholder engagement, and a commitment to transparency and accountability. By focusing on these areas, nations can build stronger, more effective governance structures that foster sustainable growth and development.

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 = 'RQ.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 <- 'RQ.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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Regulatory Quality

Year: 2023

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