Statistical Issues Related to Discrepancy Between National and International Data

1. Statistical Issues Related to Discrepancy Between National and International Data Milorad Kovacevic, UNDP/HDRO Workshop on HD Approach and Measurement for the GCC States Doha, 9-11 May, 2011

2. “… the issue of measuring human development is so important that it must be supported by the strongest statistical practice – anything less is to fail the peoples of the world.” “An Assessment of the Statistical Criticisms Made of the HDR 1999”prepared by the Friends of the Chair of the UNSC, November 2000.

3. Recognized problems Discrepancy between national and international data sources • Timelines • Reference (sub)populations, classifications • Source of data • Methods for estimation/approximation • Definitions

4. EXAMPLE: Maternal mortality ratio – recorded vs. adjustedSource: UNICEF (2011) State of World Children Recorded: NSO, Ministry of Health, 2005-2009 Adjusted: Interagency group WHO, UNICEF, UNFPA, World Bank, 2008 “accounts for under-reporting and misclassification of maternal deaths”

5. International vs. national • International data preferred for cross-national comparisons because ‘standardised’: • Agreed definitions • Same coverage (temporal, spatial, sectoral) • Standard reporting frameworks (classifications, questionnaires) • Specific methods for cross-country comparisons Example: International Comparability Programme (World Bank) →PPP conversion • Independent quality assurance

6. International vs. national • National data preferred for in-country analyses (e.g., NHDR) • More detailed • Reflect national specificities • Greater levels of disaggregation • More timely • More pertinent • More flexibility for additional calculations

7. International vs. national Raw aggregatesVarious national bodies report to numerous multilateral and international agencies Adjustments by international agencies: • Correct inconsistencies in nationally reported data • Calculate indicators combining data from different sources • May estimate or impute for entirely missing national data or for partially complete data International Indicators National authorities • Also may make adjustments • Need to transform national data to meet international definitions and to fit international reporting frameworks

8. International vs. national

9. Data needs at the international level Many important indicators are not available internationally • they may exist nationally for some countries • their standardization requires studies, coordination and mandate • high quality may imply high price Example: Mean years of schooling for adult population • Collected in Censuses, Labour Force Surveys • Education attainment tables by level of education • UNESCO agreed to start computing in 2012

10. Data needs at the international level Enhanced need to study distributions at the international level • income distribution, inequality, polarization • Inequalities in education and health outcomes • overlapping deprivations and multidimensional poverty Needs for internationally standardized household surveys • DHS, MICS, LSMS, WHS International databases (standardized survey data): - World Bank – International Income Distribution Database - Luxembourg Income Study Database - EU-SILC

11. Data needs at the international level Some countries are missing important indicators • Imputation exercise? • Estimates comparable across countries and across time • Task team of the CCSA of the UN Statistical Commission: • best practices, recommendations Stipulation: • Transparency of methods • Use of country data • Country consultation

12. Data Sources for the HDI

13. Data Sources for the HDI Complex relationships Example of Life Expectancy: • UN Population Division, WHO, UNICEF, World Bank • agree on estimates of infant and child mortality rates (which feed into UN PopDiv’s models) • UN Population Division/UNAIDS /WHO • agree on HIV/AIDS prevalence (which feed into UN PopDiv’s models for countries affected by HIV/AIDS) • UN Population Division revises its estimates and projections (for 1950-2050) every two years • Released on May 3, 2011 http://www.unpopulation.org

14. Differences with national data Causes • ‘Standardisation’ • Timing • Imputation/estimation (by international agencies) • Coverage • Definitions • Errors (inclusion‘compounded’ errors when 2 or more sources combined)

15. Example “National ‘Expected Years of Schooling’ is greater than the international one” • International EYS is for schools covered by Min of Education only – private schools excluded • National EYS uses same enrolments as international EYS but refer to different population • National EYS is for a more recent year  International GER is incorrect ?

16. What can you do when discrepancy is discovered? • Contact directly the international organisation • Contact HDRO (or UNDP CO) HDRO.Stats@undp.org • Explain the problem and ask for explanation • HDRO can • Ask international data provider for explanation • Put country in direct touch with data provider • Act as go-between • Virtually (by video conference, Email, telephone) • Face-to-face (in New York with video link to Montreal and Washington)

17. What can you do when discrepancy is discovered? • Corrections can be timely • Most of international agencies • Do revisions once a year or even once in two years • Ask for data, meta-data and other information • HDRO follows the international data providers revisions • Important for international comparisons

18. SUMMARY • The main problems at the international level persist: • thelack of appropriate data and • insufficient data quality of international databases • Nonetheless, over the last 20 years there was a considerable improvement in all aspects of relevant measuring of socio-economic and environmental phenomena • Continuous investments in data and their efficient use are needed, • multilateral and bilateral cooperation.