Data gaps in international databases Francesca Coullare United Nations Statistics Division 2007 International Conference on Millennium Development Goals Statistics Manila, 1 – 3 October 2007

 Current mechanisms/initiatives to improve international data series used to monitor progress towards the MDGs  How international agencies “adjust” country data to obtain regional estimates and/or address data gaps issues Overview  Global monitoring and the Inter-agency and Expert Group on MDGs indicators:   Working modalities,   Millennium Development Goals Indicators database mdgs.un.org 1 2 3

Global monitoring of MDGs indicators Global monitoring of MDGs indicators 1

International Monitoring Efforts  The Inter-Agency and Expert Group (IAEG) on MDG Indicators (2 meetings per year)  Coordinated by UN Statistics Division/DESA  Composed of representatives from:  25 specialized agencies,  regional commissions,  NSOs  Thematic sub-groups of the IAEG  Gender  Employment  Health  Poverty and hunger  Environment  Slums 1

International Monitoring Efforts IAEG is responsible for: (a)compiling data and undertaking analysis to monitor progress towards the MDGs at the global and regional levels; (b)reporting on status of annual progress through printed reports, progress charts, CD-roms and internet; (c)reviewing and preparing guidelines on methodologies and technical issues related to the indicators; (d)helping define priorities and strategies to support countries in data collection, analysis and reporting on MDGs.

(a)compiling data for the global/regional monitoring of MDGs Type of indicator/series AgencyMDGsOthersTotal FAO 213 ILO 4913 IPU 134 ITU 336 OECD 8712 UNAIDS 145 UNEP-Ozone 112 UNEP-WCMC 112 UNESCO 9716 UNFCCC (CDIAC) 2 24 UN-HABITAT 112 UNICEF UNPD 223 WB 7411 WHO 819 WTO

(a) Data compilation: data flow International agency country office Agency Headquarters e.g. UNICEF Line Ministry in country National Statistical Office in country Agency Headquarters e.g. UNESCO Agency Headquarters e.g. ILO MDG Indicators database 48+ indicators 192 Member States mdgs.un.org

Adjustment of country data by international agencies to ensure international comparability and address data gaps Adjustment of country data by international agencies to ensure international comparability and address data gaps 2

Data gaps for MDG 3 in international databases Ind. 9 Enrolment Ind. 10 Ind. 11 Ind.12 PrimarySecondaryTertiary Youth Literacy EmploymentParliament Developing Regions Northern Africa Sub-Saharan Africa Latin America & Caribbean Eastern Asia Southern Asia South-eastern Asia Western Asia Oceania Percentage of countries with at least 2 data points since 1990 (excluding modeled data), by indicator and MDG region Source: UNSD-MDGs database, access on June 2007

Indicator 6. Net enrolment ratio in primary education The example of UNESCO UNESCO Steps: (a)An adjustment to account for over- or under- reporting: i.To include enrolments in private schools and/or geographical areas left out ii.To exclude pupils of other programmes than primary (i.e. adult education) (b)An estimate of the number of enrolments in the official age group for primary education (when only total enrolments in primary education is reported, using reliable source for age distribution)

Indicator 6. Net enrolment ratio in primary education The example of UNESCO UNESCO Steps (cont.): (c)A redistribution of enrolments of unknown age (across known ages - only if more than 5% of tot. enrolments) (d)An estimate of the population in the official age group for primary education (if neither UNPD nor the country itself can provide estimates of their own) Treatment of missing values Treatment of missing values : When missing data for a variable, use: (a) previous years submissions, (b) other correlated variable or (c) similar countries (never published-only used in regional aggregates)

Indicator 11. Share of women in wage employment in the non-agricultural sector The example of ILO-Gender ILO-Gender : Estimated values vs. Predicted values a)Estimations based on auxiliary variables i.Total paid employment ii.Total employment in non-agriculture iii.Employees iv.Total employment v.Economically Active Population in non- agriculture Empirical analysis shows that strong correlation exits between the indicator and the auxiliary variable.

Indicator 11. Share of women in wage employment in the non-agricultural sector The example of ILO-Gender ILO-Gender : Estimated values vs. Predicted values b)Predictions based on statistical models i.Only for producing regional and global aggregates ii.Separate two-level models developed for each of the 5 regions, considering: i.between-countries variation over time, ii.within-country variation over time. iii.Based on the assumption that available data are representative of a country’s deviation from the average trend in its region, across time.

Improving international data series used to monitor progress towards the MDGs Improving international data series used to monitor progress towards the MDGs 3

(a) (a)Strengthening country statistical capacity 2006 ECOSOC Resolution 2004 Marrakech Action Plan for Statistics PARIS21 = Partnership in Statistics for Development in the 21 st Century Renewed commitment on the importance of sound statistical systems to produce evidence-based policies Blue print identifying 6 steps for achieving better statistics for better monitoring policies: 1. NSDS = national strategy 2. Increased budget allocated to Statistics Round of Population and Housing Census 4. Better support for Household Surveys - (IHSN) 5. Quick and better data in key areas such as MDGs – (ADP) 6. Increased accountability and better coordination among international statistical partners Promoting a culture of “Evidence-based decision making and implementation”

(b) Improving mechanisms for data transfer and consultation with countries Within countries: among different stakeholders producing data in the national statistical system Between countries and international agencies Role of Regional Commissions Establishing a central repository of data Between international agencies and UNSD SDMX initiative : in pilot in 3 SADC countries Work in progress in IAEG on MDGs indicators

(c) Enhance transparency in MDGs Global database UNSD MDG database to present metadata information at the “cell” level for country-level estimates Showing data source, reference period, …, pointing out possible discrepancies between international and national figures

(c) Enhance transparency in MDGs Global database UNSD MDG database to present more detailed indicator-level metadata Explaining in details methodology used to calculate indicators and presenting contact details for users to contact to obtain additional information Revised metadata for MDG Indicators in the IAEG MDG Database CONTACT POINT in international agency DEFINITION METHODS OF COMPUTATION COMMENTS AND LIMITATIONS SOURCES OF DISCREPANCIES BETWEEN GLOBAL AND NATIONAL FIGURES PROCESS OF OBTAINING DATA TREATMENT OF MISSING VALUES DATA AVAILABILITY REGIONAL AND GLOBAL ESTIMATES EXPECTED TIME OF RELEASE