The Country Policy and Institutional Assessment (CPIA) evaluates the quality of policies and institutions in IDA-eligible countries. Official CPIA assessments are conducted annually through expert judgment and inform World Bank resource allocation decisions. However, these assessments are limited to IDA countries and do not publicly release subquestion-level scores.
This methodological note describes a standardized data processing framework that generates proxy CPIA scores using publicly available governance indicators for questions 12–16, which fall under the responsibility of the World Bank’s Governance Global Practice. The framework transforms indicator data from multiple sources into CPIA-comparable scores on a 1–6 scale, extending coverage beyond IDA countries and providing subquestion-level granularity for governance analysis.
Historically, when governance specialists needed CPIA-style scores for non-IDA countries or subquestion-level analysis, they relied on decentralized, manual workflows. Different teams would independently process governance indicators using varied methods, leading to inconsistencies in scoring approaches and challenges in maintaining cross-year comparability. This methodological note establishes a structured Extract-Transform-Load (ETL) architecture that standardizes the data processing workflow.
The standardization of CPIA data processing and scoring addresses several institutional challenges:
Risks of Manual Aggregation: When governance specialists manually aggregate indicator data, the risk of computational errors, formula inconsistencies, and data entry mistakes increases. Different analysts may apply different normalization approaches or handle missing values inconsistently.
Cross-Year Comparability: Without standardized processing rules, methodological adjustments made in one year may not be consistently applied in subsequent years, compromising the ability to track governance trends over time.
Auditability Requirements: Institutional quality assurance requires clear documentation of how scores are derived from source data. Manual workflows often lack systematic audit trails, making it difficult to verify or reproduce published results.
Institutional Memory Challenges: When scoring logic resides in individual analysts’ spreadsheets or undocumented scripts, institutional knowledge is lost during staff transitions. Standardized frameworks preserve methodological consistency across teams and over time.
Reproducibility Constraints: For World Bank outputs to support evidence-based policymaking, the underlying data processing must be reproducible. Manual workflows introduce variability that undermines reproducibility.
The framework described in this note addresses these challenges by establishing a single, documented, version-controlled approach to CPIA data processing.
This note focuses specifically on CPIA questions 12–16, which are under the purview of the World Bank’s Governance Global Practice. These questions assess property rights and rule-based governance (Q12), quality of budgetary and financial management (Q13), quality of public administration (Q15), and transparency, accountability, and corruption in the public sector (Q16).
This note describes:
This note does not describe changes to the conceptual CPIA framework itself. The CPIA criteria definitions, subquestion structures, and scoring scale (1–6) follow official CPIA documentation. The methodology described here addresses data processing implementation using publicly available data, not the substantive governance assessment framework.
The framework integrates governance indicators from six sources:
CLIAR (Country Level Institutional Assessment Review): 40 indicators from the World Bank’s Closeness-to-Frontier governance assessments, covering public financial management, service delivery, and institutional capacity.
African Integrity Indicators (AII): 28 indicators from Global Integrity’s Africa Integrity Indicators project, providing detailed governance metrics for African countries.
Data360: 8 governance indicators from the World Bank’s Data360 platform, including World Governance Indicators (WGI) dimensions.
World Governance Indicators (WGI): 3 indicators measuring rule of law, government effectiveness, and control of corruption.
Heritage Foundation: 2 indicators measuring property rights protection and government integrity.
World Development Indicators (WDI): 1 governance-related development indicator.
All source indicators are normalized to a 0–1 scale prior to transformation.
The framework covers four CPIA criteria managed by the Governance Global Practice, disaggregated into 11 subquestions. Note that subquestions 13a (composition and quality of the budget) and 13c (transparency and comprehensiveness of financial information) are not included because publicly available data sources adequate to proxy these specific dimensions could not be identified. The Governance team is actively working on alternative data sources and methodologies to address these gaps in future iterations.
All subquestion scores use the standard CPIA scale of 1–6, where:
The framework produces three primary datasets:
Additionally, regional and income group aggregations are produced for each primary dataset.
This framework does not:
Raw indicator data undergo the following processing steps:
Range Validation: All indicators are verified to fall within their expected ranges. Indicators normalized on a 0–1 scale are checked for values outside this interval.
Missing Value Identification: Missing data are explicitly coded and tracked. No imputation is performed; missing values are excluded from calculations.
Outlier Capping: To prevent extreme values from distorting score distributions, outliers are capped at three standard deviations from the mean. For indicator \(x\) with mean \(\mu\) and standard deviation \(\sigma\):
\[ x_{\text{capped}} = \max\left(\mu - 3\sigma, \min(x, \mu + 3\sigma)\right) \]
Country Code Validation: Country identifiers are validated against official ISO 3-letter codes and World Bank country classifications.
Time Period Validation: Year variables are validated to ensure consistency with source data vintages.
All indicators are transformed to a common 0–1 scale prior to aggregation. Indicators already on a 0–1 scale retain their original values. Indicators on other scales are linearly transformed:
\[ x_{\text{normalized}} = \frac{x - x_{\min}}{x_{\max} - x_{\min}} \]
Where \(x_{\min}\) and \(x_{\max}\) represent the theoretical minimum and maximum values for the indicator. In some cases, raw indicators are mathematically inverted to maintain consistent intuition with the CPIA sub-question being proxied.
For each subquestion, the score is calculated as the arithmetic mean of all contributing normalized indicators:
\[ \text{Score}_q = 5 \times \bar{x} + 1 \]
Where:
This transformation maps:
All indicators contributing to a subquestion receive equal weight in the aggregation. No differential weighting scheme is applied. This design choice ensures:
If governance specialists determine that certain indicators should receive greater weight, such adjustments must be made outside this framework and documented separately.
When some but not all indicators for a subquestion are missing, the
subquestion score is calculated from available indicators only. When all
indicators for a subquestion are missing, the subquestion receives no
score (coded as NA).
Missing values do not propagate across subquestions. If one subquestion has missing data, other subquestions for the same country-year are calculated normally.
CPIA scores reflect policies and institutions observed in year \(t\) but assessed in year \(t+1\). To align with official CPIA conventions, the framework includes a “CPIA year” variable calculated as:
\[ \text{CPIA Year} = \text{Data Year} + 1 \]
This ensures that proxy scores correspond temporally with official CPIA assessment years.
The following table shows the mapping between each CPIA sub-question and the specific indicators used to construct proxy scores. Each sub-question is represented by one or more indicators drawn from various governance databases.
| Sub-Question | Indicators |
|---|---|
| q12a | wjp_rol_6_6 (Measures whether the government respects the property rights of people and corporations, refrains from the illegal seizure of private property, and provides adequate compensation when property is legally expropriated); property_rights (Measures the protection and enforcement of property rights through an independent and effective judiciary.); property_rights (Measures the protection and enforcement of property rights through an independent and effective judiciary.) |
| q12b | bs_bti_q3_2 (Measures the independence of the judiciary (i.e. the ability and autonomy to interpret and review existing law, pursue its own reasoning free from the influence of political decision-makers or powerful groups and individual, etc)); idea_gsod_v_21_05 (Measures the extent to which citizens have a right to a fair trial in practice, are not subject to arbitrary arrest, the right to reocgnition as a person before the law, and other civil liberties.); vdem_core_v2clacjstm (Measures the extent to which men can bring cases before the courts without risk to their personal safety, trials are fair, and men have effective ability to seek redress if public authorities violate their rights); vdem_core_v2clacjstw (Measures the extent to which women can bring cases before the courts without risk to their personal safety, trials are fair, and women have effective ability to seek redress if public authorities violate their rights, including the rights to counsel, defense, and appeal); vdem_core_v2juaccnt (Measures whether judges are removed from their posts or disciplined when found responsible for serious misconduct); vdem_core_v2juhcind (Measures whether the judicial system’s high court makes decisions that merely reflect government wishes or its sincere view of the legal record); vdem_core_v2x_rule (Captures to some extent whether laws transparently, independently, predictably, impartially, and equally enforced, and to what extent do the actions of government officials comply with the law.); wjp_rol_2_2 (Measures whether judicial officials refrain from soliciting and accepting bribes to perform duties and whether the judiciary is free of improper influence by the government, private interests, or criminal organizations.); wjp_rol_4_3 (Measures whether the basic rights of criminal suspects are respected, including the presumption of innocence and the freedom from arbitrary arrest and unreasonable pre-trial detention); wjp_rol_7_1 (Captures whether people can access and afford civil justice as well as whether it is free of discrimination, improper government influence, and unreasonable delays); wjp_rol_7_5 (Measures whether civil justice proceedings are conducted and judgments are produced in a timely manner without unreasonable delay); wjp_rol_7_6 (Measures the effectiveness and timeliness of the enforcement of civil justice decisions and judgments in practice); wjp_rol_7_7 (Captures the accessibility, impartiality, and effectiveness of alternative dispute resolution mechanisms); wjp_rol_8_1 (Measures whether perpetrators of crimes are effectively apprehended and charged as well as whether police, investigators, and prosecutors have adequate resources); wjp_rol_8_2 (Captures the effectiveness and timeliness of the criminal investigation system is effective); wjp_rol_8_4 (Measures whether the police and criminal judges are impartial and whether they discriminate in practice based on socio-economic status, gender, ethnicity, religion, national origin, sexual orientation, or gender identity); gi_aii_1 (In law, the independence of the judiciary is guaranteed.); gi_aii_2 (In practice, the independence of the judiciary is guaranteed.); gi_aii_3 (In practice, national-level judges appointments (justices or magistrates) support the independence of the judiciary.); gi_aii_4 (In practice, national-level judges give reasons for their decisions/judgments.); wb_wdi_rq_per_rnk (Ability of the government to formulate and implement sound policies and regulations) |
| q12c | wjp_rol_8_1 (Measures whether perpetrators of crimes are effectively apprehended and charged as well as whether police, investigators, and prosecutors have adequate resources); wjp_rol_8_2 (Captures the effectiveness and timeliness of the criminal investigation system is effective); wjp_rol_8_4 (Measures whether the police and criminal judges are impartial and whether they discriminate in practice based on socio-economic status, gender, ethnicity, religion, national origin, sexual orientation, or gender identity) |
| q13b | bs_bti_q8_2 (Measures the extent t which the government’s budgetary policies support fiscal stability); wb_pefa_pi_2016_18 (PI-18. Measures the nature and extent of legislative scrutiny of the annual budget); wb_pefa_pi_2016_19 (PI-19. Measures the quality of revenue administration based on rights and obligations for revenue measures, revenue risk management, revenue audit and investigation, and revenue arrears monitoring.); wb_pefa_pi_2016_20 (PI-20. Measures the quality of procedures for recording and reporting revenue collections, consolidating revenues collected, and reconciling tax revenue accounts); wb_pefa_pi_2016_21 (PI-21. Meaures the extent to which the central ministry of finance is able to forecast cash commitments and requirements and to provide reliable information on the availability of funds to budgetary units for service delivery.); wb_pefa_pi_2016_23 (PI-23. Measures the quality of payroll management, how changes are handled, and how consistency with personnel records management is achieved); wb_pefa_pi_2016_24 (PI-24. Measures the quality of procurement monitoring, procurement methods, public access to procurement information, and procurement complaints management.); wb_pefa_pi_2016_25 (PI-25. Measures the effectiveness of general internal controls for nonsalary expenditures.); q13b (Primary government expenditures as a percentage of original approved budget) |
| q15a | bs_bti_q15_1 (Measures the extent to which the government makes efficient use of available human, financial and organizational resources) |
| q15b | bs_bti_q15_2 (To what extent can the government coordinate conflicting objectives into a coherent policy?); vdem_core_v2clrspct (Measures whether public officials rigorous and impartial in the performance of their duties (0 to 4 scoring)); vdem_core_v2stcritrecadm (Measures the extent to which appointment decisions in the state administration are based on personal and political connections as opposed to skills and merit (0 to 4 scoring)); wb_spi_census_and_survey_index (Measures the availability of recent censuses and surveys covering broad areas); wb_spi_std_and_methods (Captures the country’s use of internationally accepted and recommended methodologies, classifications and standards regarding data integration) |
| q15c | vdem_core_v2peasjpol (Captures whether state jobs are equally open to qualified individuals regardless of their association with a political group (0 to 4 scoring)); vdem_core_v2peasjsoecon (Captures if state jobs are equally open to qualified individuals regardless of socio-economic position) |
| q16a | vdem_core_v2x_execorr (Captures the extent to which members of the executive engage in malfeasance); gi_aii_5 (In law, there is a supreme audit institution.); gi_aii_6 (In law, the independence of the supreme audit institution is guaranteed.); gi_aii_7 (In practice, the independence of the supreme audit institution is guaranteed.); gi_aii_8 (In practice, appointments to the supreme audit institution support the independence of the agency.); gi_aii_9 (In practice, the supreme audit agency releases frequent reports that are accessible to citizens.) |
| q16b | ibp_obs_obi (Captures the extent to which the public has access to timely and comprehensive budget information); wb_gtmi_dcei (Measures aspects of public participation platforms, citizen feedback mechanisms, open data, and open government portals.); wjp_rol_3_1 (Measures whether basic laws and information on legal rights are publicly available, presented in plain language, and made accessible in all languages used in the country or jurisdiction); wjp_rol_3_2 (Measures whether requests for information held by a government agency are granted within a reasonable time period and at a reasonable cost without paying a bribe); wjp_rol_3_4 (Measures whether people are able to bring specific complaints to the government about the provision of public services or the performance of government officers in carrying out their legal duties in practice as well as whether government officials respond to such complaints); gi_aii_26 (In practice, citizens can access the results and documents associated with procurement contracts (full contract, proposals, execution reports, financial audits, etc.).) |
| q16c | vdem_core_v2lgcrrpt (Captures the extent to which members of the legislative engage in malfeasance); wjp_rol_6_2 (Measures whether the enforcement of regulations is subject to bribery or improper influence by private interests and whether public service are provided without bribery or other inducements); gi_aii_10 (In law, corruption is criminalized as a specific offense.); gi_aii_11 (In law, there is an independent body/bodies mandated to receive and investigate cases of alleged public sector corruption.); gi_aii_12 (In practice, allegations of corruption against senior level politicians and/or civil servants of any level are investigated by an independent body.); gi_aii_13 (In practice, the body/bodies that investigate/s allegations of public sector corruption is/are effective.); gi_aii_14 (In practice, appointments to the body/bodies that investigate/s allegations of public sector corruption support/s the independence of the body.); gi_aii_15 (In law, the head of state and government can be investigated and prosecuted while in office if evidence suggests they committed a crime.); gi_aii_16 (In practice, heads of state and government are investigated and prosecuted while in office if evidence suggests they committed a crime.); gi_aii_27 (In law, companies found guilty of violations of procurement regulations are prohibited from participating in future bids.); gi_aii_28 (In practice, companies found guilty of violating procurement regulations are prohibited from participating in future bids.); gi_aii_29 (In practice, citizens can access the financial records of state-owned companies.); gi_aii_30 (In practice, citizens can access the financial records associated with natural resources exploitation (gas, oil and mining), whether they involve the participation of public or private corporations.); gi_aii_31 (In practice, significant public expenditure receives legislative approval on an annual basis.); gi_aii_32 (In law, both the executive’s budget proposal and the approved budget must be published in full every year.); gi_aii_33 (In practice, citizens can provide input for budget decisions.); gi_aii_34 (In practice, a legislative committee exercises oversight of public funds.); gi_aii_35 (In law, civil servants are required to report cases of alleged corruption.); gi_aii_36 (In law, civil servants who report cases of corruption are protected from recrimination or other negative consequences.); gi_aii_37 (In law, there are formal rules to prevent conflicts of interest, nepotism, cronyism and patronage in all branches of government.); wb_wdi_cc_est (Ability of the government to limit corruption); wb_wdi_va_est (Captures perceptions of citizen participation in selecting the government) |
| q16d | vdem_core_v2x_pubcorr (Captures the extent to which public employees engage in malfeasance); wjp_rol_2 (Captures the extent to which state officials in the executive branch, the judicial branch, the legislative branch, and the police/military use public office for private gain) |
This mapping ensures transparency in the selection of proxy indicators for each CPIA dimension. The indicators were selected based on conceptual alignment with official CPIA assessment criteria and data availability across countries and time periods.
The data processing framework follows a four-stage pipeline:
┌─────────────────────────────────────────────────────┐
│ STAGE 1: DATA INGESTION │
│ │
│ - Load raw indicators from source datasets │
│ - Validate country codes and time periods │
│ - Check data structure integrity │
└──────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────┐
│ STAGE 2: INDICATOR NORMALIZATION │
│ │
│ - Apply outlier capping (mean ± 3 SD) │
│ - Transform to 0–1 scale │
│ - Flag and handle missing values │
└──────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────┐
│ STAGE 3: AGGREGATION AND SCORING │
│ │
│ - Group indicators by subquestion │
│ - Calculate mean normalized values │
│ - Apply CPIA transformation (5 × mean + 1) │
└──────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────┐
│ STAGE 4: METADATA ENRICHMENT │
│ │
│ - Merge country classifications │
│ - Add CPIA year (year + 1) │
│ - Generate regional and income group aggregates │
└─────────────────────────────────────────────────────┘The framework maintains strict separation between:
This separation ensures that methodological changes can be implemented without modifying source data, and that data updates do not require changes to processing logic.
All transformations are deterministic: given identical input data, the framework produces identical output scores. No random processes, subjective adjustments, or stochastic elements are involved. This determinism ensures:
The framework incorporates automated validation checks at each processing stage:
These checks generate error messages when data quality issues are detected, preventing invalid scores from being published.
Each processed dataset includes metadata documenting:
This audit trail enables traceability of how published scores were generated.
The framework includes automated tests that validate:
Transformation Correctness: Verifies that the CPIA transformation formula produces expected outputs for known inputs (e.g., normalized value of 0 produces CPIA score of 1).
Outlier Capping: Confirms that extreme values are correctly capped at three standard deviations.
Missing Value Handling: Validates that missing data are excluded from calculations without propagating to other subquestions.
Aggregation Logic: Checks that arithmetic means are calculated correctly across multiple indicators.
Metadata Completeness: Ensures that all country classifications and temporal variables are populated.
These tests run automatically whenever processing logic is modified, ensuring that changes do not introduce errors.
All processing logic is maintained under version control using Git. Each methodological change is:
MAJOR version changes indicate breaking changes to data structures or methodological revisions.
MINOR version changes indicate new features or additional indicators.
PATCH version changes indicate bug fixes or documentation updates.
Automated checks run on every proposed change to processing logic, validating:
Only changes that pass all checks are incorporated into production processing.
A structured change log documents:
This change log supports institutional memory and enables users to understand differences between score versions.
Traditional approaches to processing governance indicators often involve:
Limitations:
Some teams develop custom R or Python scripts to process governance data. While more reproducible than manual Excel workflows, ad hoc scripts face challenges:
When different teams independently process the same source data, methodological inconsistencies emerge:
These inconsistencies undermine cross-team comparability and create confusion when results differ.
The standardized framework addresses these limitations by:
The framework assumes that source indicator data are accurate and up to date. If source databases contain errors or are not regularly maintained, output scores will reflect those limitations. Users should:
This framework implements a data processing methodology for proxy CPIA scores. It does not:
The subquestion definitions and criteria structures follow official CPIA documentation. Any conceptual changes to CPIA itself must be addressed through official CPIA review processes, not through this data processing framework.
Official CPIA assessments incorporate expert judgment, country context, and qualitative information that cannot be fully captured by quantitative indicators. The proxy scores generated by this framework:
However, they do not replace the nuanced, expert-driven assessments conducted for official CPIA. For operational decisions, official CPIA scores should always be prioritized when available.
When source indicators have limited coverage or missing observations, subquestion scores may:
Users should consult the raw data and metadata to understand which indicators contribute to each subquestion score and identify coverage gaps.
Scores generated under different versions of the processing framework may not be directly comparable if methodological changes have occurred. Users should:
CPIA scores measure the quality of policies and institutions but do not establish causal relationships with development outcomes. Correlation between CPIA scores and economic performance does not imply causation. Analysts using these data for research should:
The framework produces several datasets suitable for different analytical purposes:
Standard CPIA Dataset: Use for cross-country comparisons requiring globally consistent indicators. Suitable for analyses covering countries beyond Africa or when AII data availability is limited.
Africa-Enhanced CPIA Dataset: Use for Africa-focused governance analysis when African Integrity Indicators provide higher-resolution data.
Raw Indicator Data: Use for understanding which specific governance indicators drive subquestion scores or for conducting sensitivity analyses.
Regional and Income Group Aggregates: Use for tracking governance trends across regions or income groups over time.
Governance specialists should select the dataset appropriate for their analytical question and document which dataset was used in reporting.
The framework outputs are integrated into governance dashboards that provide interactive visualizations of CPIA proxy scores. Dashboard users can:
Dashboards facilitate exploratory analysis but do not replace careful interpretation of governance data in country-specific context.
When governance specialists prepare country-level governance diagnostics or CPIA-related write-ups, the framework outputs can:
However, write-ups should clearly distinguish between official CPIA assessments and proxy scores generated by this framework.
The standardized processing ensures that methodological changes are documented and consistently applied. When comparing scores across years, users should:
The framework supports institutional reporting needs by:
However, framework outputs are not substitutes for official CPIA assessments in operational contexts requiring formal scoring.
World Bank. (2023). Country Policy and Institutional Assessment: Criteria and Questionnaire. Washington, DC: World Bank.
World Bank. (2024). World Governance Indicators 2024: Methodology and Analytical Issues. Washington, DC: World Bank.
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). “The Worldwide Governance Indicators: Methodology and Analytical Issues.” Hague Journal on the Rule of Law, 3(2), 220–246.
Heritage Foundation. (2024). Index of Economic Freedom: Methodology. Washington, DC: Heritage Foundation.
Global Integrity. (2022). Africa Integrity Indicators: Methodology. Washington, DC: Global Integrity.
For each subquestion \(q\) with normalized indicators \(x_1, x_2, \ldots, x_n\) where \(x_i \in [0,1]\):
\[ \text{Score}_q = 5 \times \left(\frac{1}{n}\sum_{i=1}^{n} x_i\right) + 1 \]
Equivalently:
\[ \text{Score}_q = 5 \times \bar{x} + 1 \]
Where \(\bar{x}\) is the arithmetic mean of non-missing normalized indicators.
Properties:
For indicator \(x\) with sample mean \(\mu\) and sample standard deviation \(\sigma\):
\[ x_{\text{capped}} = \begin{cases} \mu - 3\sigma & \text{if } x < \mu - 3\sigma \\ x & \text{if } \mu - 3\sigma \leq x \leq \mu + 3\sigma \\ \mu + 3\sigma & \text{if } x > \mu + 3\sigma \end{cases} \]
This approach caps extreme values at three standard deviations from the mean while preserving values within the normal range.
| Variable | Type | Description | Range/Values |
|---|---|---|---|
country_code |
character | ISO 3-letter country code | ISO 3166-1 alpha-3 |
year |
integer | Calendar year of data collection | 2005–2024 |
q12a |
numeric | Property rights legal framework | 1–6 or NA |
q12b |
numeric | Legal and judicial system quality | 1–6 or NA |
q12c |
numeric | Crime and violence impediment | 1–6 or NA |
q13b |
numeric | Financial management systems | 1–6 or NA |
q15a |
numeric | Core admin operations | 1–6 or NA |
q15b |
numeric | Policy implementation quality | 1–6 or NA |
q15c |
numeric | HR management coordination | 1–6 or NA |
q16a |
numeric | Executive accountability | 1–6 or NA |
q16b |
numeric | Civil society information access | 1–6 or NA |
q16c |
numeric | State capture | 1–6 or NA |
q16d |
numeric | Public resource integrity | 1–6 or NA |
cpia_year |
integer | Assessment year (year + 1) | 2006–2025 |
economy |
character | Country name | — |
income_group |
character | World Bank income classification | Low, Lower middle, Upper middle, High income |
lending_category |
character | Lending classification | IDA, IBRD, Blend, NA |
region_code |
character | Region abbreviation | AFE, AFW, EAP, ECA, LAC, MENAAP, SAR, NAC |
region |
character | Full region name | — |
Rule 1: All CPIA subquestion scores must fall within [1, 6] or be coded as NA.
Rule 2: Country codes must match ISO 3166-1 alpha-3 standard.
Rule 3: Year variables must be within valid data collection period (2005–2024 for source data; 2006–2025 for CPIA year).
Rule 4: Income group must be one of: “Low income”, “Lower middle income”, “Upper middle income”, “High income”, or NA.
Rule 5: Region code must be one of: AFE, AFW, EAP, ECA, LAC, MENAAP, SAR, NAC, or NA.
Rule 6: Normalized indicators must fall within [0, 1] after outlier capping.
The framework follows semantic versioning (MAJOR.MINOR.PATCH):
MAJOR version increments when:
MINOR version increments when:
PATCH version increments when:
When methodological changes occur:
When source indicator data are revised by their providers:
End of Methodological Note