Overall SPI Score

Since 2019, the Philippines’ Overall SPI Score follows the trend below:

Overall SPI Scores for the Philippines
Year Overall SPI Score
2023 85.20
2022 83.95
2021 84.42
2020 79.15
2019 75.39

Pillar Data

In 2023, the pillar scores are as follows:

Over the years, the Philippine’s pillar scores are:

Pillar 2019 2020 2021 2022 2023
Pillar 1: Data Use 100.00 100.00 100.00 100.00 100.00
Pillar 2: Data Services 88.27 93.77 93.77 92.67 92.67
Pillar 3: Data Products 75.17 78.47 89.81 84.82 85.24
Pillar 4: Data Sources 78.53 83.53 83.53 87.28 83.11
Pillar 5: Data Infrastructure 35.00 40.00 55.00 55.00 65.00

Pillar 1: Data Use

The data infrastructure (capability) pillar includes hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors (World Bank, n.d.).

Pillar 2: Data Services

The data services (output) pillar is segmented by four service types: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics (World Bank, n.d.).

Pillar 3: Data Products

The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country (World Bank, n.d.).

Pillar 4: Data Sources

The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met (World Bank, n.d.).

Pillar 5: Data Infrastructure

The data infrastructure (capability) pillar includes hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors (World Bank, n.d.).

Rank

Global

Year Rank Country Count
2023 46 187
2022 50 187
2021 48 181
2020 52 181
2019 55 174

Lower-Middle-Income Countries

The Philippines’ SPI score of 85.2 in 2023 is above the group average of 63.51.

Year Rank Country Count
2023 1 50
2022 2 50
2021 1 50
2020 2 50
2019 2 50

Upper-Middle-Income Countries

The Philippines’ SPI score of 85.2 in 2023 is above the group average of 69.49.

Year Rank Country Count
2023 8 53
2022 10 53
2021 10 51
2020 13 51
2019 14 48

ASEAN

The Philippines’ SPI score of 85.2 in 2023 is above the regional average of 74.68.

Notes

Dimension

Below is a brief description of the information (or lack thereof) we have available for the dimensions in our framework. For dimensions excluded, we either lacked a source with a developed methodology or else the data collection for that measure was incomplete. This is described below:

  • Dimension 1.1: Data use by national legislature: Not included because of lack of established methodology. In principle it may be possible to utilize websites of national legislatures but this will require further work and assessment.

  • Dimension 1.2: Data use by national executive branch: Not included because of lack of established methodology. There are some usable data sources with fairly good coverage (as used by PARIS21) but gaps in data have prevented fuller assessment of suitable methods.

  • Dimension 1.3: Data use by civil society: Not included because of lack of established methodology. There are some usable data sources with good coverage, for example from social media but more data is required to help assess and allow for likely biases between and within countries.

  • Dimension 1.4: Data use by academia: Not included because of lack of established methodology. We have not been able to find usable data sources with global coverage on which a new methodology could be developed.

  • Dimension 1.5: Data use by international organizations: Reliability/Usefulness of Poverty, Child Mortality, Debt Statistics, safely managed drinking water, and labor force statistics data for international agencies using metadata. We recognize that these data sources provide only partial coverage but consider that they do at least provide some indication of the performance of the national statistical system. With more complete data sources it would be possible to assess this further

  • Dimension 2.1: Data Releases: SPI.D2.1.GDDS - SDDS/e-GDDS subscription. This is a good data source but we recognize that it is a proxy for the concept we are seeking to capture rather than a direct measurement.

  • Dimension 2.2: Online access: SPI.D2.2.Openness.subscore ODIN Open Data Openness score. This is a well-established data source with good country coverage. In using this indicator, it is important to describe carefully what is captured since the purpose of ODIN is different to the purpose of the SPI.

  • Dimension 2.3: Advisory/ Analytical Services: Not included because of lack of established methodology. We recognize that this data source provides only limited coverage but consider that it does at least provide some indication of the performance of the national statistical system. With more complete data sources it would be possible to assess this further.

  • Dimension 2.4: Data services: SPI.D2.4.NADA NADA metadata. We have not been able to find usable data sources with global coverage on which a new methodology could be developed.

  • Dimension 3.1: Social Statistics: Average score for Goal 1-6 indicators. The primary data source is the UN SDG database. Whilst this is a database with comprehensive coverage that all countries have signed up to, it is clear that many (particularly developed countries) are not yet submitting their available national data. Scores for these countries are likely to represent an indicator of their willingness to submit national data rather than their performance in calculating the indicators. For OECD countries, we supplement the UN SDG database with comparable data submitted to the OECD following the methodology in Measuring Distance to the SDG Targets 2020: An Assessment of Where OECD Countries Stand (https://www.oecd.org/sdd/measuring-distance-to-the-sdg-targets-2020-a8caf3fa-en.htm).

  • Dimension 3.2: Economic Statistics: Average score for Goal 7-12 indicators. See 3.1.

  • Dimension 3.3: Environmental Statistics: Average score for Goal 13-15 indicators. See 3.1.

  • Dimension 3.4: Institutional Statistics: Average score for Goal 16-17 indicators. See 3.1.

  • Dimension 4.1: Censuses and Surveys: Average score Census and Survey Indicators indicators. In this release of the SPI the data and methods used for this indicator are the same as for the previous SPI. Further work could improve the validity of this indicator and reduce the risk that countries may be incentivized to adopt outdated practices for censuses and surveys.

  • Dimension 4.2: Administrative Data: Average score for CRVS indicator. Social Protection, Education, and Labor admin data indicators not included because of lack of established methodolgy. While our team identified several promising sources for administrative data from the World Bank’s ASPIRE team, UNESCO, and ILO, incomplete coverage across countries made us drop these indicators from our index. A major research and data collection effort is needed from all custodian agencies to fill in this information, so that a more comprehensive picture of administrative data availability can be produced.

  • Dimension 4.3: Geospatial Data: SPI.D4.3.GEO.first.admin.level - Geospatial data available at 1st Admin Level. We recognize that this data source provides only limited coverage but consider that it does at least provide some indication of the ability of the national statistical system to produce geospatial data. A major research and data collection effort is needed via GGIM to fill in this information, so that a more comprehensive picture of geospatial data capability at the national level can be produced. Until this is done, it we cannot even assess the scale of the data gaps in a comparable way.

  • Dimension 4.4: Private/citizen generated data: Not included because of lack of established methodology. Currently no comprehensive source exists to measure the use of private and citizen generated data in national statistical systems, and this should be another area where more data collection is needed by the international community.

  • Dimension 5.1: Legislation and governance: Included in dashboard, but not index because of insufficient country coverage. A global database of statistical and data legislation and governance practice would be a valuable resource for capacity building in general not just for the SPI.

  • Dimension 5.2: Standards and Methods: Average score for Standards and Methods indicators. In this release of the SPI the data and methods used for this indicator are the same as for the previous SPI. Further work could improve the validity of this indicator and reduce the risk that countries may be incentivized to adopt only traditional standards and methods and neglect innovative solutions that may be more valid in the current context.

  • Dimension 5.3: Skills: Not included because of lack of established methodology or suitable data sources

  • Dimension 5.4: Partnerships: Not included because of lack of established methodology or suitable data sources

  • Dimension 5.5: Finance: Included in dashboard, but not index because of insufficient country coverage and concerns that the indicator has biases that would lead to misleading incentives.

Indicators

Reference

World Bank. (n.d.). SPI Index (R script). GitHub. Retrieved 16 March 2025, from https://github.com/worldbank/SPI/blob/master/02_programs/02-SPI_index.Rmd