workshop on mdg monitoring kampala, uganda, 5-8 may 2008 reconciling international and national...

Workshop on MDG Monitoring Kampala, Uganda, 5-8 May 2008

Reconciling international and national sources for effective

global monitoring

Francesca Perucci

United Nations Statistics Division

DESA, New York

Workshop on MDG Monitoring, Kampala, Uganda, 5-8 May 2008

Reconciling international and national sourcesfor effective global monitoring

Outline

1. Global monitoring and the work by the Inter-agency and Expert Group on MDGs indicators (IAEG)

2. Compilation of international sources

3. Improving international sources and resolving data gaps and discrepancies

4. Next steps



Outline

1.1. Global monitoring and the work by the Inter-Global monitoring and the work by the Inter-agency and Expert Group on MDGs indicators agency and Expert Group on MDGs indicators (IAEG)(IAEG)



4. Next steps


IAEG on MDG Indicators

The Inter-Agency and Expert Group (IAEG) on MDG Indicators (2 meetings per year)

Coordinated by UN Statistics Division/DESA

Composed of representatives from:

Over 25 specialized agencies, UN Regional commissions, National Statistical Offices

Thematic sub-groups of the IAEG Gender Employment Health Poverty and hunger Environment Slums


IAEG and MDG monitoring

IAEG is responsible for:

● Compiling data and undertaking analysis to monitor progress towards the MDGs at the global and regional levels;

● Reporting on status of annual progress through printed reports, progress charts, databases, etc.;

● Reviewing and preparing guidelines on methodologies and technical issues related to the indicators;

● Helping define priorities and strategies to support countries in data collection, analysis and reporting on MDGs.



Outline


2.2. Compilation of international sourcesCompilation of international sources


4. Next steps


Revised MDG monitoring framework: proposals by the IAEG

● To ensure comparability across countries and regions, data used for the global monitoring are compiled by international agencies within their area of expertise

● For each indicator, one or more agencies were designated to be the official data providers and to take the lead in developing appropriate methodologies for data collection and analysis.

● Data are typically drawn from official statistics provided by governments to the international agencies responsible for the indicator, through periodic data collection from ministries and NSOs (ex. ILO)….

● or collected through surveys sponsored and carried out by international agencies (ex. MICS, DHS)


Data provided by the international agencies

(a)(a)compiling data for the compiling data for the global/regional monitoring of global/regional monitoring of MDGsMDGs

151727917

505WTO

1129WHO

1147WB

853UNPD

24717UNICEF

211UN-HABITAT

606UNFCCC (CDIAC)

16106UNESCO

752UNEP-WCMC

211UNEP-Ozone

651UNAIDS

1578OECD

853ITU

431IPU

21165ILO

514FAO

TotalOtherMDGsAgency

Indicator/series


Data compilation: from national sources to the MDG database

InternatioInternational agency nal agency

country country officeoffice

Agency Agency HeadquarteHeadquarte

rs eg. rs eg. UNICEFUNICEF

Line Line Ministry in Ministry in

the the countrycountryNational National

Statistical Statistical Office in the Office in the

countrycountry

Agency Agency HeadquarteHeadquarte

rs eg.rs eg. UNESCOUNESCO

Agency Agency HeadquartHeadquart

erserseg. eg. ILOILO

MDG Indicators database

60 MDG indicators + background/additional indicators192 Member States1990-2007

mdgs.un.org


Data availability in international sources

IndicatorCountries with at least

1 data pointCountries with at least 2

data points

Number % Number %

Population below $1 (PPP) per day 70 43 57 35

Children under 5 moderately or severely underweight 113 69 72 44

Total net enrolment ratio in primary education 137 84 123 75

Literacy rates 109 67 63 39

Share of women in wage employment in the non-agricultural sector 149 91 100 61

Children under five mortality rate per 1,000 live births 138 85 138 85

Maternal mortality ratio per 100,000 live births 133 82 131 80

People living with HIV, 15-49 years old 98 60 98 60

Condom use at last high-risk sex, women 48 29 22 13

Contraceptive use 111 68 82 50

Tuberculosis death rate per 100,000 population 155 95 155 95

Proportion of the population using improved sanitation 140 86 104 64

Slum population as percentage of urban 106 65 105 64

Source: MDG Indicators Database, at mdgs.un.org


But where are the data from?

IndicatorCountries with at least

1 data pointCountries with at least 2

data points

Number % Number %

Population below $1 (PPP) per day 70 43 57 35

Children under 5 moderately or severely underweight 113 69 72 44

Total net enrolment ratio in primary education 137 84 123 75

Literacy rates 109 67 63 39

Share of women in wage employment in the non-agricultural sector 149 91 100 61

Children under five mortality rate per 1,000 live births 138 85 138 85

Maternal mortality ratio per 100,000 live births 133 82 131 80

People living with HIV, 15-49 years old 98 60 98 60

Condom use at last high-risk sex, women 48 29 22 13

Contraceptive use 111 68 82 50

Tuberculosis death rate per 100,000 population 155 95 155 95

Proportion of the population using improved sanitation 140 86 104 64

Slum population as percentage of urban 106 65 105 64

Source: MDG Indicators Database, at mdgs.un.org

Modelled for 68 countries

Modelled for 16 countries


Different types of data in international data sources


Indicator 11. Share of women in wage employment in the non-agricultural sector

An example: ILO, Indicator for Goal 3

Estimated values vs. Predicted values

a) Estimations based on auxiliary variablesi. Total paid employmentii. Total employment in non-agricultureiii. Employeesiv. Total employmentv. Economically Active Population in non-

agriculture

Empirical analysis shows that strong correlation exits between the indicator and the auxiliary variables.


Indicator 11. Share of women in wage employment in the non-agricultural sector

An example: ILO, Indicator for Goal 3

Estimated values vs. Predicted values

b) Predictions based on statistical models1. Only in order to produce regional and global

aggregates2. A separate two-level model is developed for

each of the 5 regions, considering: i. between-countries variation over time,ii. within-country variation over time.

3. Based on the assumption that available data are representative of a country’s deviation from the average trend in its region, across time .



Outline



3.3. Improving international sources and resolving Improving international sources and resolving data gaps and discrepanciesdata gaps and discrepancies

4. Next steps


Improving international sources

● Recommendations of the friends of the Chair to improve further the compilation of the indicators used in the monitoring of the MDGs.

● In July 2006, the Economic and Social Council adopted its resolution 2006/6 on strengthening statistical capacity in countries and included a set of recommendations to improve the coverage, transparency and reporting on all indicators.

● Since then, the Inter-Agency and Expert Group has been working to implement the recommendations of the friends of the Chair and those contained in the ECOSOC resolution.


Work of the IAEG

● Improve data quality and transparency of methods

● Work with countries to identify priorities in national statistical capacity building

● Work with countries to identify best practices for coordination within national statistical systems and for reporting mechanisms to the international statistical system

● Address key methodological issues, including the use of population figures, in the computation of the indicators

● Develop improve training tools on the compilation of the indicators


Resolving data discrepancies

● Improve consistency and transparency in reporting and presenting data and metadata for the international monitoring.

– adoption of the common platform for data exchange, the Statistical Data and Metadata Exchange (SDMX)

– continue to improve the content and structure of the metadata and the tools available on the mdgs.un.org website

● The IAEG also recommended:– To investigate the reasons for discrepancies between national and

international data, and for data gaps– To hold expert group meetings to establish strategies for dealing with

data discrepancies and data gaps at the national and international levels.

– To provide technical assistance to countries in the use of international definitions.

– To improve the process of consultation by international agencies with countries before publishing their data.


Improve transparency in the MDG Indicators Database

● UNSD MDG database to present more detailed metadata

– details on methodology used to calculate indicators

– contacts for users to contact to obtain additional information

Revised structure of 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



Outline




4.4. Next stepsNext steps


Work of the IAEG

● The main objectives of the work of the group remain:

a) improving the effectiveness of capacity building activities; and

b) ensuring data quality and transparency, and reduce data gaps.


Work of the IAEG

● Improving the effectiveness of capacity building activities

The group brings together international agencies, regional commissions and representatives of national statistical systems to set priorities and identify ways to improve the delivery of capacity building programmes and promote the scaling-up of resources.


Work of the IAEG

●Ensuring data quality and transparencyPriority areas: Implement the recommendations by the friend of the Chair on MDG

Indicators and those in the ECOSOC resolution Implement the recommendations by IAEG member countries in

2006 on coordination and reporting mechanisms (Survey to countries and agencies on coordination and reporting)

Address some key methodological issues, including the use of population figures in the computation of the indicators and regional aggregates.

Continuing to improve the content and structure of the metadata and the tools available on the website.

•SDMX task team to develop the data structure for MDG Indicators•revision of the handbook for country reporting•revision of the metadata on international series and upgrading

of the database


Work of the IAEG

● Reducing data gaps

The group has developed a plan to involve more extensively regional commissions in data checking purposes (discrepancies between national and international data series) and in channelling and following-up on specific queries on data and metadata between national statistical systems and international agencies

The plan also involves identifying underlying causes for data gaps and make specific recommendations for improvement


Next steps

● Continue to develop the data structure for SDMX – International agencies– Countries

● Develop new methodologies for the assessment of progress

– Global and regional level– Country level

● Continue the work to assist countries develop their system of coordination and reporting to the international statistical system

– Revise questionnaire– Expand the survey– Workshops and EGMs on coordination practices



THANK YOUTHANK YOU

Visit mdgs.un.org

workshop on mdg monitoring kampala, uganda, 5-8 may 2008 reconciling international and national...

Documents

data gaps

mdg monitoring framework

mdg monitoring kampala

periodic data collection

official data providers

mdg indicatorsthe interagency

mdg monitoringiaeg

expert group iaeg