Business Environment Map
Top Business Obstacles
Regional Comparison
Quality Indices
Distribution Analysis
Explore distribution of key business metrics
Global Business Environment Overview
Comprehensive benchmarking of business environments across 168 economies using World Bank Enterprise Survey data
Regional Comparison - Key Indicators
Distribution of Key Indicators
Density plots showing the distribution of business environment metrics across countries. Use the arrows, dropdown, or auto-scroll to explore different indicators.
Country Profile
In-depth analysis of business environment indicators for individual countries
Business Environment Radar
Key Indicators
Infrastructure Obstacles
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Bars rank which infrastructure services firms flag as biggest obstacles, indicating where reliability investments are needed.
Water & Transport Constraints
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Shows water and transport infrastructure constraints affecting business operations.
Financial Access Overview
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-fina
Financial product uptake across credit and deposit instruments highlights where inclusion gaps remain.
Bribery by Transaction Type
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-gov_
Bribery prevalence by transaction type surfaces which interactions with government most often trigger informal payments.
Workforce Composition
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-work
Shows workforce composition including female participation and skill levels.
Crime as Business Obstacle
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-crim
Shows crime and security costs as obstacles to business operations.
Capacity Utilization
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-perf
Shows capacity utilization and operational efficiency metrics.
Macro-level indicators from the World Bank Databank provide context for interpreting firm-level WBES data.
Macroeconomic Overview
Macro Environment Radar
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-macr
National-level indicators on governance, infrastructure, and trade complement the firm-level business environment radar.
Governance Indicators (WGI)
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-gove
World Bank Worldwide Governance Indicators ranging from -2.5 (weak) to +2.5 (strong).
Sector Profile
In-depth analysis of business environment indicators for individual sectors
Business Environment Radar
Business Environment Radar
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-rada
Key Indicators
Infrastructure Obstacles
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Power Source Distribution
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Water & Transport Constraints
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Digital Connectivity
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-infr
Financial Access Overview
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-fina
Collateral Requirements
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-fina
Bribery by Transaction Type
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-gov_
Regulatory Burden
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-gov_
Gender Composition
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-work
Training & Skills
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-work
Crime & Security Costs
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-crim
Crime-Related Losses
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-crim
Operational Performance
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-perf
Export Orientation
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-perf
Country Distribution
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065856-coun
Regional Profile
In-depth analysis of business environment indicators across geographic regions
Business Environment Radar
Key Indicators
Infrastructure Obstacles
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-infr
Bars rank which infrastructure services firms in this region flag as biggest obstacles.
Water & Transport Constraints
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-infr
Water and transport constraints for firms in this region.
Financial Access Overview
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-fina
Financial product uptake across credit and deposit instruments for this region.
Bribery by Transaction Type
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-gov_
Bribery prevalence by transaction type for firms in this region.
Gender Composition
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-work
Gender composition in workforce and ownership for firms in this region.
Crime & Security Costs
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-crim
Crime as obstacle and security costs for firms in this region.
Operational Performance
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-perf
Operational performance metrics for firms in this region.
Firm Size Profile
In-depth analysis of business environment indicators by firm size category
Business Environment Radar
Business Environment Radar
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-rada
Key Indicators
Bars rank which infrastructure services firms of this size flag as biggest obstacles.
The pie shows how firms of this size power operations (grid, generator, mixed).
Water and transport constraints for firms of this size.
Digital connectivity metrics for firms of this size.
Financial product uptake across credit and deposit instruments for this firm size.
The gauge reports average collateral required for loans for firms of this size.
Bribery prevalence by transaction type for firms of this size.
Management time spent on regulatory tasks for this firm size category.
Gender composition in workforce and ownership for firms of this size.
Workforce quality and training metrics for this firm size.
Crime as obstacle and security costs for firms of this size.
Crime-related losses as percentage of sales.
Operational performance metrics for firms of this size.
Export orientation of firms of this size.
Shows the geographic distribution of surveyed firms in this size category across countries.
Cross-Country Benchmarking
Compare business environment indicators across multiple countries and regions
Comparison Settings
Cross-Domain Comparison
Summary of key indicators across all domains for selected countries
Infrastructure Insights
Finance Insights
Governance Insights
Workforce Insights
Performance Insights
Crime Insights
World Bank Databank macro indicators provide national-level context for comparing firm-level WBES metrics across countries.
Firm-Level (WBES) vs National (WB) Comparison
WBES Perceptions vs World Bank Data
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065857-wbes
Compares firm-reported metrics (WBES) with national statistics (World Bank). Gaps may indicate differences between firm experience and national averages.
Full Macro Data Comparison
Cross-Sector Benchmarking
Compare business environment indicators across economic sectors by domain
Comparison Settings
Cross-Domain Comparison
Summary of key indicators across all domains for selected sectors
Infrastructure Insights
Finance Insights
Governance Insights
Workforce Insights
Performance Insights
Crime Insights
Cross-Regional Benchmarking
Compare business environment indicators across geographic regions by domain
Comparison Settings
Cross-Domain Comparison
Summary of key indicators across all domains for selected regions
Infrastructure Insights
Finance Insights
Governance Insights
Workforce Insights
Performance Insights
Crime Insights
Cross-Size Benchmarking
Compare business environment indicators across firm size categories by domain
Comparison Settings
Cross-Domain Comparison
Summary of key indicators across all domains for selected firm sizes
Infrastructure Insights
Finance Insights
Governance Insights
Workforce Insights
Performance Insights
Crime Insights
Infrastructure Constraints Analysis
Analyze power, water, and transport infrastructure impacts on business operations
Infrastructure Quality by Country
Access to Finance Analysis
Analyze financial inclusion, credit access, and financing constraints across economies
Reasons for Not Applying for Loans
Corruption & Governance
Key Insights
Workforce & Gender Inclusion
Workforce vs. Productivity
Key Insights
Business Performance & Trade
Key Insights
Crime & Security
Key Insights
Custom Analysis & Report Builder
Analysis Configuration
Select indicators for your analysis:
Infrastructure
Finance
Governance
Workforce & Performance
Analysis Results
Data Quality & Methodology
Complete transparency on data sources, quality issues, and analytical filtering logic
About This Documentation
This section provides comprehensive documentation of data quality considerations following best practices for transparent data science . Understanding data limitations is essential for valid interpretation.
Data Source Transparency
Filter Logic Documentation
Reproducible R Code
Data Completeness by Indicator
Indicator Data Completeness
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065858-comp
Bars show the share of non-missing responses for each key indicator so you can judge reliability before analysis.
Completeness by Region
Regional Data Completeness
Source: World Bank Enterprise Surveys |
Created by: Kwiz Computing Technologies |
ID: WBES-20260627065858-regi
Regional completeness highlights where survey coverage is thinner, guiding cautious interpretation.
Documented Data Quality Issues
Each issue is classified by severity and includes the specific filter/adjustment applied.
Analysis-Specific Filter Logic
Infrastructure Constraints Analysis
Purpose: Analyze power, water, and transport infrastructure impacts
Data Scope: Firms responding to infrastructure module (variables c6-c20)
Filter Steps:
- Exclude firms with missing power outage data ( 12% of records )
- Winsorize extreme outage durations at 48 hours (caps 2.3% outliers )
- Apply sampling weights for national representativeness
- Flag countries with sample size < 200 for wide confidence intervals
R Code:
wbes_data |>
# Step 1: Remove missing values
filter(!is.na(power_outages_per_month)) |>
# Step 2: Winsorize extreme values
mutate(
avg_outage_duration_hrs = pmin(avg_outage_duration_hrs, 48)
) |>
# Step 3: Flag low-sample countries
mutate(
low_sample_flag = sample_size < 200,
reliability = case_when(
sample_size >= 500 ~ "High",
sample_size >= 200 ~ "Medium",
TRUE ~ "Low (Use with caution)"
)
) |>
# Step 4: Apply weights for aggregation
group_by(country, year) |>
summarise(
weighted_outages = weighted.mean(
power_outages_per_month,
w = sample_weight,
na.rm = TRUE
),
n_firms = n(),
.groups = "drop"
)
Methodological Note
Generator ownership is self-reported and may undercount shared/rented generators.
Fuel cost estimates should be validated against local fuel prices.
Financial Access Analysis
Purpose: Analyze credit constraints and financial inclusion
Data Scope: Firms responding to finance module (variables k1-k30)
Filter Steps:
- Exclude logically inconsistent responses (has loan but no bank account)
- Winsorize collateral requirements at 99th percentile (caps values > 500%)
- Separate analysis by firm size (SME definition: < 100 employees)
- Apply sector-specific adjustments for capital intensity differences
R Code:
wbes_data |>
# Step 1: Remove logical inconsistencies
filter(!(has_loan == 1 & has_bank_account == 0)) |>
# Step 2: Winsorize collateral at 99th percentile
mutate(
collateral_required_pct = pmin(
collateral_required_pct,
quantile(collateral_required_pct, 0.99, na.rm = TRUE)
)
) |>
# Step 3: Create firm size categories (World Bank SME definitions)
mutate(
size_category = case_when(
employees < 20 ~ "Small (5-19)",
employees < 100 ~ "Medium (20-99)",
TRUE ~ "Large (100+)"
)
) |>
# Step 4: Calculate credit gap by size
group_by(country, size_category) |>
summarise(
credit_access_pct = weighted.mean(has_credit_line, w = sample_weight, na.rm = TRUE) * 100,
rejection_rate = weighted.mean(loan_rejected, w = sample_weight, na.rm = TRUE) * 100,
avg_collateral = weighted.mean(collateral_required_pct, w = sample_weight, na.rm = TRUE),
n_firms = n(),
.groups = "drop"
)
Known Limitation
Informal credit sources (family, moneylenders) are not fully captured.
Actual financial constraint may be higher than reported.
Corruption & Governance Analysis
Purpose: Analyze bribery incidence and regulatory burden
Data Scope: Firms responding to governance module (variables j1-j15)
Caution: Sensitive topic with known underreporting bias
Filter Steps:
- Apply survey weights to account for stratified sampling
- Flag rapid responses (< 5 seconds) as potential social desirability bias
- Exclude countries with < 50% response rate on sensitive questions
- Apply list experiment corrections where available (subset of surveys)
R Code:
wbes_data |>
# Step 1: Filter on response rate for corruption questions
filter(corruption_response_rate >= 0.50) |>
# Step 2: Flag potential social desirability bias
mutate(
potential_underreport = response_time_seconds < 5,
# Apply conservative adjustment for flagged responses
bribery_adjusted = case_when(
potential_underreport & bribery_incidence == 0 ~ 0.1, # Small upward adjustment
TRUE ~ bribery_incidence
)
) |>
# Step 3: Weighted aggregation
group_by(country, year) |>
summarise(
bribery_incidence_pct = weighted.mean(
bribery_adjusted,
w = sample_weight,
na.rm = TRUE
) * 100,
response_rate = mean(!is.na(bribery_incidence)),
n_firms = n(),
.groups = "drop"
) |>
# Step 4: Add reliability indicators
mutate(
reliability_flag = case_when(
response_rate < 0.6 ~ "Low - High non-response",
n_firms < 200 ~ "Low - Small sample",
TRUE ~ "Adequate"
)
)
Critical Limitation
Corruption indicators are known to be systematically underreported due to fear
of reprisal. Cross-country comparisons should focus on relative rankings rather
than absolute values. Alternative measures (Transparency International CPI,
World Governance Indicators) recommended for triangulation.
Country Profile Deep Dive
Purpose: Build radar charts and summary cards for a single country
Data Scope: Latest survey wave with complete core indicators
Key Output: Radar scores normalized to 0–100 for comparability
Filter Steps:
- Keep only most recent year available per country
- Require non-missing values for all six radar dimensions
- Cap extreme outage values at 30/month to stabilize the scale
- Apply population weights when summarizing national KPIs
R Code:
profile_data <- wbes_data |>
group_by(country) |>
filter(year == max(year, na.rm = TRUE)) |>
ungroup() |>
filter(!if_any(c(power_outages_per_month, firms_with_credit_line_pct,
bribery_incidence_pct, capacity_utilization_pct,
export_firms_pct, female_ownership_pct), is.na)) |>
mutate(power_outages_per_month = pmin(power_outages_per_month, 30))
Cross-Country Benchmarking
Purpose: Enable like-for-like comparisons across selected countries
Data Scope: Countries with Global Methodology surveys (2019+)
Caution: Exclude economies with sample size < 150 to avoid unstable ranks
Filter Steps:
- Limit to specified indicator and comparison set
- Standardize firm size buckets before aggregating
- Use weighted means with sector weights for fairness
- Attach confidence intervals for interpretation of close ranks
R Code:
benchmark_data <- wbes_data |>
filter(year >= 2019, sample_size >= 150) |>
mutate(size_bucket = case_when(
employees < 20 ~ "Small (5-19)",
employees < 100 ~ "Medium (20-99)",
TRUE ~ "Large (100+)"
)) |>
group_by(country) |>
summarise(
indicator_mean = weighted.mean(.data[[indicator_id]], w = sample_weight, na.rm = TRUE),
indicator_se = sd(.data[[indicator_id]], na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
Workforce & Gender Analysis
Purpose: Examine labor constraints and gender inclusion
Data Scope: Workforce and gender variables (b3, b4, b5, c6)
Notes: Gender variables use female share; missing values imputed with sector medians
Filter Steps:
- Exclude firms with <5 employees to align with sampling frame
- Impute female worker share using sector medians when missing
- Flag countries where female ownership response rate <60%
- Calculate gender gap = female_workers_pct - female_ownership_pct
R Code:
workforce_clean <- wbes_data |>
filter(employees >= 5) |>
group_by(sector) |>
mutate(female_workers_pct = if_else(
is.na(female_workers_pct),
median(female_workers_pct, na.rm = TRUE),
female_workers_pct
)) |>
ungroup() |>
mutate(gender_gap_pct = female_workers_pct - female_ownership_pct,
low_response_flag = female_ownership_response_rate < 0.6)
Performance & Trade Analysis
Purpose: Assess capacity utilization, exports, and competitiveness
Data Scope: Capacity (f1), exports (d3), and constraint indicators
Method: Smooth volatile year-to-year changes with 3-year rolling averages where panel data exist
Filter Steps:
- Winsorize export share at 95th percentile
- Remove panel observations with inconsistent firm identifiers
- Compute rolling means for panel countries; fallback to latest cross-section otherwise
- Flag competitiveness scores with n < 100 as low reliability
R Code:
performance <- wbes_panel |>
mutate(export_firms_pct = pmin(export_firms_pct, quantile(export_firms_pct, 0.95, na.rm = TRUE))) |>
group_by(country) |>
arrange(year) |>
mutate(capacity_roll = zoo::rollmean(capacity_utilization_pct, 3, fill = NA, align = "right")) |>
summarise(
capacity_recent = coalesce(last(capacity_roll[!is.na(capacity_roll)]), last(capacity_utilization_pct)),
export_recent = last(export_firms_pct),
n_obs = n(),
reliability = if_else(n_obs < 100, "Low", "Adequate"),
.groups = "drop"
)
Crime & Security Analysis
Purpose: Evaluate crime obstacles and security spending
Data Scope: Crime and security variables (g5, g6, j14)
Bias Check: Exclude firms with zero security spend but positive crime losses as inconsistent
Filter Steps:
- Remove inconsistent security spend responses
- Cap security costs at 50% of sales to limit outliers
- Group by income level to compare relative burden
- Calculate combined risk score = crime_obstacle * 0.6 + security_costs_pct * 0.4
R Code:
security_filters <- wbes_data |>
filter(!(security_costs_pct == 0 & crime_losses_pct > 0)) |>
mutate(security_costs_pct = pmin(security_costs_pct, 50)) |>
group_by(income) |>
summarise(
crime_obstacle = weighted.mean(IC.FRM.CRIM.ZS, w = sample_weight, na.rm = TRUE),
security_costs = weighted.mean(security_costs_pct, w = sample_weight, na.rm = TRUE),
combined_risk = crime_obstacle * 0.6 + security_costs * 0.4,
.groups = "drop"
)
Cross-Country Benchmarking
Purpose: Valid comparison of business environments across countries
Key Challenge: Survey timing, definitions, and sampling vary
Standardization Steps:
- Use only comparable survey years (2019-2023 using Global Methodology)
- Apply purchasing power parity adjustments for monetary values
- Standardize firm size definitions across countries
- Weight by sector composition for like-with-like comparison
- Calculate confidence intervals accounting for complex survey design
R Code:
wbes_data |>
# Step 1: Use only Global Methodology surveys (2019+)
filter(
year >= 2019,
survey_methodology == "Global"
) |>
# Step 2: Standardize firm size (harmonize country definitions)
mutate(
size_standardized = case_when(
employees >= 5 & employees < 20 ~ "Small (5-19)",
employees >= 20 & employees < 100 ~ "Medium (20-99)",
employees >= 100 ~ "Large (100+)",
TRUE ~ NA_character_
)
) |>
filter(!is.na(size_standardized)) |>
# Step 3: Weight by sector to ensure comparability
group_by(country, sector) |>
mutate(
sector_weight = sample_weight * sector_adjustment_factor
) |>
ungroup() |>
# Step 4: Calculate indicators with design-based SEs
group_by(country) |>
summarise(
across(
c(power_outages_per_month, firms_with_credit_line_pct, bribery_incidence_pct),
list(
mean = ~weighted.mean(.x, w = sector_weight, na.rm = TRUE),
se = ~sqrt(sum(sector_weight^2 * (.x - weighted.mean(.x, w = sector_weight, na.rm = TRUE))^2, na.rm = TRUE)) / sum(sector_weight, na.rm = TRUE)
)
),
n_firms = n(),
survey_year = max(year),
.groups = "drop"
) |>
# Step 5: Add confidence intervals
mutate(
across(
ends_with("_mean"),
list(
ci_lower = ~.x - 1.96 * get(gsub("_mean$", "_se", cur_column())),
ci_upper = ~.x + 1.96 * get(gsub("_mean$", "_se", cur_column()))
),
.names = "{.col}_{.fn}"
)
)
Best Practice
When comparing countries, always report: (1) survey years, (2) sample sizes,
(3) confidence intervals. Avoid comparing countries with > 3 year survey gap.
Variable Dictionary & Definitions
Methodology Reference
World Bank Enterprise Surveys Methodology
The Enterprise Surveys use a stratified random sampling methodology with three levels of stratification:
- Sector: Manufacturing, Retail, Other Services
- Firm Size: Small (5-19), Medium (20-99), Large (100+)
- Geographic Region: Main business cities/regions
Key Documentation
Download Documentation
Download complete data quality documentation for your records:
Citation
World Bank. (2024). Enterprise Surveys. Available at: https://www.enterprisesurveys.org Dashboard developed by Kwiz Computing Technologies. https://kwizresearch.com
About This Dashboard
Business Environment Benchmarking using World Bank Enterprise Survey Data
About the Data
World Bank Enterprise Surveys
The Enterprise Surveys are the World Bank's flagship firm-level data collection, providing comparable, standardized data on the business environment across economies.
- Coverage: 168 economies worldwide
- Sample: 253,000+ businesses surveyed since 2002
- Indicators: 150+ standardized indicators
- Methodology: Stratified random sampling with sampling weights
- Frequency: Rolling surveys, typically every 3-5 years per country
Key Topic Areas
- Infrastructure quality
- Access to finance
- Corruption & governance
- Firm performance
- Labor & workforce
- Trade & exports
- Gender & ownership
- Innovation & technology
Official Data Source: enterprisesurveys.org
Built With
R Programming Language
- Rhino Framework (Appsilon)
- Shiny + bslib
- Plotly Interactive Charts
- Leaflet Maps
- DT DataTables
- box Modular Design
- testthat Testing
Developed By
Kwiz Computing Technologies
Boutique Data Science Consultancy
Nairobi, Kenya
License & Usage
Data License
World Bank Enterprise Survey data is publicly available under the World Bank Terms of Use .
Users should cite the source as:
World Bank Group. Enterprise Surveys. Available at: https://www.enterprisesurveys.org
Dashboard License
This dashboard is developed by Kwiz Computing Technologies.
For commercial licensing, customization, or consultancy inquiries:
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