Libra, Jesse Madden (2023). OLAS/SCL WASH Household Survey Dataset. IDB Open Data. https://doi.org/10.60966/8yvsrxmk

1. Spanish

What is the temporal and geographic coverage?

The dataset spans 2003 to 2022 for 22 countries. (data.iadb.org)
The countries included are:
- Argentina
- Bahamas
- Barbados
- Belize
- Bolivia
- Brazil
- Chile
- Colombia
- Costa Rica
- Dominican Republic
- Ecuador
- El Salvador
- Guatemala
- Guyana
- Haiti
- Honduras
- Jamaica
- Mexico
- Nicaragua
- Panama
- Paraguay
- Peru
- Suriname
- Trinidad and Tobago
- Uruguay
- Venezuela

How are the indicators expressed and disaggregated?

Indicators are given both as percentages of households and as the number of households.
They can be broken down by:
- urban vs rural area
- income quintile
- migratory status
- ethnicity
- disability status

What methodology supports harmonization and comparability?

The dataset uses a harmonization process to align variable definitions, response categories, and breaks across different national surveys.
It addresses data gaps, classification uncertainty (e.g., unknown sources), and survey inconsistencies.

Which key topics do the indicators cover?

Topics include:
- water access (primary source, continuity, treatment)
- sanitation access (improved vs not, exclusive use)
- facility types and unknown/unclassifiable responses

What are the limitations or cautions of the dataset?

• Differences in survey design or question definitions across countries may limit direct comparisons.
• Some indicators represent “unknown” or unclassified responses where survey questions did not map cleanly to harmonized categories.
• Not all disaggregation dimensions (e.g., ethnicity) are available in every year or for every country.

What is the OLAS Simplified GLAAS methodology for water and sanitation monitoring?

The dataset reflects a household-survey–based monitoring approach that compiles standardized indicators on water and sanitation conditions and disaggregates them by socioeconomic and demographic characteristics.
Rather than infrastructure inventories, it measures experienced access and service conditions at the population level, enabling comparison across territories and groups.

How does OLAS Simplified GLAAS measure service access and inequality?

Access is quantified using survey indicators (indicator, value) that are disaggregated by: - Geographic setting (area – e.g., urban/rural classifications) - Income proxy (quintile) - Sex (sex) - Age, education, ethnicity, disability, and migration status This structure allows analysts to identify distributional gaps in WASH services, not just national averages.

How does OLAS Simplified GLAAS bridge data gaps in rural sanitation reporting?

By collecting microdata with rural/urban identifiers and socioeconomic disaggregation, the dataset enables: - Detection of underserved populations within the same country - Comparison of lowest vs. highest wealth quintiles - Evidence-based targeting where administrative systems lack granular coverage This helps shift monitoring from infrastructure presence to actual household-level service access.

How reliable and current are OLAS Simplified GLAAS indicators for tracking SDG 6 progress?

The dataset includes: - Sampling information (sample) - Standard errors (se) - Coefficients of variation (cv) - Quality flags (quality_check) These statistical fields support robust monitoring aligned with SDG 6 requirements, enabling confidence assessment and trend comparison when multiple years are available.

How can policymakers use OLAS Simplified GLAAS outputs to inform national WASH policy?

Policymakers can use the dataset to: 1. Identify inequality patterns across wealth quintiles and demographic groups.
2. Prioritize investments toward populations with systematically lower indicator values.
3. Track whether access improvements are inclusive rather than concentrated among higher-income households.
4. Integrate statistically validated indicators into SDG monitoring dashboards.

Where can researchers use this dataset most effectively?

This dataset is best suited for: - Equity analysis of WASH access
- Disaggregated SDG 6 monitoring
- Socioeconomic gradient studies in service availability
- Evidence generation for geographically targeted interventions
It is not designed for infrastructure engineering, financial analysis, or institutional benchmarking.