Countries by SPI Pillar 4: Data Sources

Ranking 2024

Analysis

The data sources pillar measures whether countries have access to four types of data: (i) censuses and surveys (population census, labor force surveys, household surveys), (ii) administrative data (tax records, social security records, vehicle registrations), (iii) geospatial data (satellite imagery, maps), and (iv) private and citizen-generated data (private sector data, mobile phone records). Scored 0-100, this pillar captures the underlying capacity to collect information about populations and economies. This matters because no statistics can be produced without underlying data sources. A country scoring 95 (Singapore) maintains regular censuses, frequent labor surveys, integrated tax records, and uses satellite data for urban planning. One scoring 10 (South Sudan) has conducted no census since independence (2011), lacks functional tax systems, and has no regular household surveys. Year-over-year volatility averages 6.5%—low because building or losing data collection infrastructure is slow, requiring years to construct survey capacity or years to collapse due to conflict. All 189 countries reported 2024 data with 100% official quality.

The top rankings combine wealthy nations, functional emerging economies, and deliberate institutional investors. Singapore (95.88, rank 1), Poland (94.33, rank 2), Spain (92.1, rank 3), and Canada (91.28, rank 4) lead. Mexico (89.3, rank 6) ranks in top-10 despite being middle-income, reflecting sustained investment in census and survey capacity. The USA (86.93, rank 12) ranks well but not elite, suggesting gaps in integrated administrative data access. Japan (83.38, rank 24) ranks 24th—developed but lower than expected, possibly reflecting privacy restrictions on administrative data. Colombia (82.5, rank 25) ranks higher than Japan, showing that emerging economies can prioritize data sources. By stark contrast, China (40.7, rank 148) ranks dramatically lower than India (74.18, rank 54) despite China's larger economy, reflecting restricted access to China's census and administrative data systems. Conflict-affected and fragile states cluster at the bottom: Syria (11.17, rank 186), Libya (10.67, rank 187), Central African Republic (10.1, rank 188), and South Sudan (9.74, rank 189) lack functional census infrastructure.

Smaller nations and emerging economies with strong institutional commitment rank higher than larger nations with weaker data governance. Poland (94.33, rank 2) outranks the UK (77.63, rank 46) by 17 points, reflecting EU statistical harmonization requirements and Poland's deliberate data infrastructure modernization. Mongolia (80.96, rank 30) ranks higher than the UK, suggesting that Asian developing nations can achieve robust data source access through policy prioritization. Mexico's top-10 ranking (89.3) despite middle-income status reflects continuous census cycles and household survey programs. Conversely, India (74.18) ranks surprisingly low given its population size and statistical office, reflecting challenges in integrating administrative data and conducting frequent surveys. The low volatility (6.5%) reflects that statistical infrastructure changes gradually—countries do not lose census capacity quickly without major institutional collapse.

This pillar measures whether countries report having data sources, not whether those sources are actually used, accurate, or current. A country scoring 80 may conduct censuses infrequently (every 20 years), maintain incomplete administrative records, or hold data without sharing it for statistical use. Additionally, the assessment relies on country self-reporting of whether they conduct surveys, maintain administrative systems, and collect geospatial data—evaluations may be overstated or reflect aspirational capacity rather than operational capacity. The pillar also does not distinguish between public data sources (open censuses, published statistics) and restricted data (inaccessible administrative records, classified geospatial data), so countries may score high for having data sources while sharing them neither with the public nor with statisticians. Finally, "availability" of data sources may reflect international support and donor-funded surveys rather than country-owned capacity—a country may score moderately due to temporary external support that ends when projects conclude.

Methodology

The data sources pillar score measures each country's capacity to access four types of data used for statistics on a 0-100 scale. (i) Censuses and surveys: whether countries conduct decennial population censuses, regular labor force surveys, and household surveys; (ii) Administrative data: whether countries maintain and use tax records, social security systems, vital registration, and business registers for statistical purposes; (iii) Geospatial data: whether countries access satellite imagery, maps, and geospatial tools for urban planning and environmental statistics; (iv) Private and citizen-generated data: whether countries leverage private sector data (e.g., mobile phone records, bank transactions) or citizen-generated data (e.g., crowdsourced information). Data comes from the World Bank's World Development Indicators (indicator: IQ.SPI.PIL4), assessed through country self-evaluation and expert review of statistical capacity assessments. All 189 countries reported 2024 data with 100% official data quality. The mean data sources score is 52.96 with a standard deviation of 20.06, indicating substantial global variation. No extreme outliers were detected (all within 3 standard deviations). Year-over-year volatility averages 6.5%, reflecting gradual institutional changes in data collection capacity. The pillar is part of the broader SPI framework measuring statistical system quality across five dimensions.

Sources

• World Bank