workshop on mdg monitoring - united...

ESA/STAT/AC.207/3

Workshop on MDG Monitoring Beirut, Lebanon, 7-10 December 2009

Final Report

Workshop on MDG Monitoring, Beirut, Lebanon, 7-10 December 2009

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Table of contents

Introduction 3 Opening addresses .........................................................................................................................3

Objectives of the workshop.............................................................................................................3

Improving MDG data reporting ........................................................................................................4 Conclusions and recommendations .................................................................................................5

Resolving discrepancies between national and international data .................................................6 Mechanisms used by international agencies to compile data and produce estimates .........................7

Education.......................................................................................................................................7

Employment...................................................................................................................................7

Water and sanitation......................................................................................................................8

Child mortality................................................................................................................................9

Assessment of data availability and discrepancies ............................................................................9

Conclusions and recommendations ............................................................................................... 10

Coordination of national statistical systems..................................................................................11 Conclusions and recommendations ............................................................................................... 12

The New MDG Monitoring Framework..........................................................................................12 Conclusions and recommendations ............................................................................................... 13

Other recommendations ................................................................................................................13

Annex 1. List of participants ......................................................................................................14

Annex 2. Summary of identified causes of discrepancies........................................................20

Introduction 1. The Workshop on the Millennium Development Goals (MDG) Monitoring was held in Beirut, on 7-10 December 2009. The workshop was organised by the United Nations Statistics Division (UNSD) in collaboration with the United Nations Economic and Social Commission for Western Asia (ESCWA). ESCWA hosted the Workshop.

2. Participants included representatives from 14 National Statistics Offices – namely Egypt, Iraq, Italy, Jordan, Lebanon, Morocco, Occupied Palestinian Territory, Oman, Saudi Arabia, Sudan, Syrian Arab Repub lic, Tunisia, United Arab Emirates and Yemen – and from four international agencies – the International Labour Organization (ILO), United Nations (UN), Unesco Institute for Statistics (UIS) and United Nations Children’s Fund (UNICEF). The list of participants is displayed in Annex 1.

Opening addresses

3. Two opening addresses were given. The first address was given by Ms. Neda Jafar, from the Statistics Division at ESCWA, who welcomed the participants to the Workshop and stressed the importance of disseminating quality statistics with metadata for monitoring and reporting. She also outlined ESCWA’s efforts in building the statistical capacity of national statistical systems in the region.

4. The second address was given by Ms. Maria Martinho on behalf of Ms. Francesca Perucci, Chief of the Statistical Planning and Coordination Section at UNSD, who encouraged countries and the international community to work together to create a stronger basis of reliable MDG data, to reconcile data differences among different sources and to combine efforts to monitor the MDGs.

Objectives of the workshop

5. This Workshop was the third of a series of Workshops to implement recommendations made by international agencies and countries to improve the monitoring of MDG indicators. The objectives of the Workshop were:

• To inform countries on helpful IT tools for improving data reporting from NSOs and line ministries to international agencies and other users;

• To understand existing discrepancies between national and international data, identify data gaps at the international level and develop recommendations to address these gaps and discrepancies;

• To recommend strategies for a better coordination within national statistical systems (NSS) and between the national and international systems;

• To present the metadata of the new MDG indicators recently adopted and to review related national definitions, methods and data sources.

6. The Recommendations of the Workshop will be reported to the Inter-agency and Expert Group on MDG Indicators (IAEG) at its next meeting in February 2010.


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Improving MDG data reporting 7. The session on improving MDG data reporting started with a presentation by Ms. Sara Duerto Valero (UNSD), who presented the IAEG work to build statistical capacity in countries and to improve data reporting on MDG Indicators. The primary aims of the IAEG are to compile and analyze data to monitor the MDGs at the global/regional level, to review and prepare guidelines for countries and to help countries define strategies for MDG monitoring at the country level. The IAEG relies on the participation of NSOs in the annual meetings in order to understand national priorities and coordinate its activities accordingly. NSOs were particularly involved in the recent update of the Handbook on MDG Indicators – a manual to guide countries on the production of MDG indicators. The Handbook is expected to be released in 2010. The IAEG has also adopted SDMX for data exchanges among agencies and is working towards extending the adoption of this standard platform to data exchanges with countries. To address countries’ recommendations, UNSD has launched a series of regional workshops, organized in collaboration with the regional commissions and other partner agencies, focusing on coordination and methodological issues related to MDG data reporting and monitoring.

8. The rest of the session was divided in two parts: the first part focused on the implementation of SDMX in countries and the second part on the implementation and use of MDG national databases.

9. Representatives from Devinfo and the Italian National Statistical Institute (Istat) presented recent developments in the implementation of SDMX as a format to exchange data and metadata:

a) Mr. Sameer Thapar (DevInfo) explained that SDMX stands for Statistical Data and Metadata Exchange, a common format agreed by different statistical agencies to exchange data and metadata. He introduced the online SDMX-MDG registry which contains the data and metadata structures to be used for exchanges of data and metadata on MDG indicators. This registry is being developed by the IAEG members. The RSS feature of the SDMX registry will automatically inform users every time there is a change in the data or metadata. Mr. Thapar provided a demonstration of the SDMX features of DevInfo, which create and read data and metadata files in XML format using SDMX. These tools are currently being developed for a limited set of indicators.

b) Mr. Giuseppe Sindoni (Istat) presented the challenges and relevance of the implementation of SDMX according to his experience in Istat and Eurostat. He described the architecture of the SDMX exchange system, including the database and the database manager, the loader, the RSS and a web service dissemination system that provides information on data and metadata updates to users. Useful SDMX resources include: http://bms.istat.it/sodidownload/download.aspx and http://circa.europa.eu. The SDMX Framework Manager software is the tool Istat uses for SDMX exchanges. The data structure definitions (DSDs) were defined by both statisticians and IT experts, by consensus among all exchanging agencies. Once these are agreed upon, the data exchanges are conducted by the IT department of the organizations involved. According to Mr. Sindoni’s experience, large investments or additional resources are not required for the implementation of SDMX. In fact, the implementation has now become easier as most SDMX


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applications are open source and free of charge. There is no burden on the exchanges as file sizes are usually not bigger than “equivalent” excel files. SDMX is now being extended to microdata exchanges: the European Groups Register Project – a compilation of data on European companies – is the first project to exchange microdata in SDMX format.

10. Three presentations were devoted to MDG national databases:

a) Ms. Sara Duerto Valero (UNSD) briefly explained that, in previous IAEG meetings, countries had requested the IAEG to review country MDG databases. On the basis of this review, country members of the IAEG identified desirable features of a national MDG database: tools for producing charts, maps and cross-tabulate indicators, ability to store and export data together with metadata and to provide an online interface as well as capacity to disseminate data using SDMX. IAEG members also reiterated that unique national MDG databases lead to improved coordination among national data producers and to better dissemination of data.

b) Mr. Juraj Riecan, Chief of the Statistics Division at ESCWA, compared different strategies for data storage and dissemination. He explained the advantages of shifting from static files – like pdf and Word - which have to be updated manually and have limited flexibility in the manipulation of data to databases permitting customized queries, flexible downloads and easy updates. Ideally, multiple databases should also be replaced by a single national database for a more coherent dissemination of all national MDG data. In reality, however, countries may have to keep multiple databases as there are several agencies collecting data. In this case, countries will benefit from a process oriented architecture in which the multiple databases are linked to a common process database compiling all data. This single comprehensive database will then be used for dissemination purposes.

c) Mr. Mostafa Bouabdellah (Haut Commissariat au Plan, Morocco) presented MarocInfo, a national version of DevInfo. This database comprises data and metadata for 243 indicators, including MDG indicators. Still, for some indicators, the metadata require further refinement. The Haut Commissariat au Plan, and particularly its Statistics Directorate, has recently established committees with regional partners to discuss and agree on a list of indicators which will be regularly produced from routine statistics. In order to ensure a common statistical platform at national, regional and provincial levels, the regional partners will receive training on the use of DevInfo for sub-national monitoring.

Conclusions and recommendations

11. Countries acknowledged the distinctive role of the Statistics Division at ESCWA in improving national data quality and building the capacities of national statistical offices for disseminating their national MDGInfos. Central MDG databases lead to improved coordination among national data producers and to more efficient dissemination of data to international agencies.


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12. To expedite data quality checks and comparison with international sources, ESCWA will develop a regional template for countries to compile national MDG data and metadata. Countries were invited to use this template to report data to ESCWA in 2010.

13. Country representatives welcomed the implementation of SDMX. In particular, ESCWA and the countries of the region plan to conduct in 2010 a pilot exchange of MDG data and metadata using SDMX. This pilot will cover selected MDG indicators for which data structures agreed by the international agencies are already available. Both UIS and the World Bank have already exchanged data with UNSD using SDMX formats. UNSD will share with ESCWA the current SDMX registry. To fully participate in the pilot SDMX exchange exercise, countries requested further training of their IT focal points in the implementation and use of SDMX. ESCWA already organized a three-day training for statisticians and IT focal points in November 2009. Future trainings, which could be organized and conducted by DevInfo, ESCWA and UNSD, would focus on the preparation of MDG data files compatible with the UN DSD as presented in the SDMX registry.

14. There was a general concern on the use of English only for the SDMX data exchanges. Although the original SDMX files use English, countries would like to visualize the metadata in Arabic when these files are uploaded on their own databases. ESCWA offered to assist in the Arabic translation of the DSS of the SDMX registry, so that a common Arabic translation can be used in DevInfo and in countries in-house SDMX converters.

Resolving discrepancies between national and international data 15. At the session on discrepancies between national and international data, Ms. Maria Martinho (UNSD) presented the process of compilation of MDG data from international sources. International agencies compile national data through their own regional and/or country offices or through requests sent to line ministries and/or national statistical offices directly. The UN Statistical Commission has recognized the need to improve these reporting mechanisms, as some national data never reach the international community or are provided to the international statistical system with a significant delay. The Commission also recommended more transparency in the methods applied by the international agencies to produce estimates and a better understanding of the reasons behind discrepancies between national and international data. Some discrepancies are the result of missing data at the international level or lack of comprehensive metadata and can be reduced or eliminated when the missing information is provided. Other differences, however, are justified because countries use definitions or methods better suited to their own national circumstances, which might differ from the international definitions and methods. International agencies may also adjust data to ensure international comparability.

16. Presentations were delivered by Mr. Talal El Hourani from UIS-UNESCO, Ms. Valentina Stoevska from ILO, Ms. Danzhen You from UNICEF, and Mr. Rolf Luyendijk from UNICEF –on behalf of the Joint Monitoring Programme (JMP) for Water Supply and Sanitation – on the process used by their respective agencies to compile data from national sources and produce international estimates. Each presentation was followed by a plenary discussion to clarify specific aspects of the production of national and international estimates.


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Mechanisms used by international agencies to compile data and produce estimates

Education

17. In his presentation, Mr. Talal El Hourani (UIS-UNESCO) explained in detail the methods of calculation of indicators 2.1 “Net enrolment ratio in primary education”, 2.3 “Literacy rate of 15-24 year olds, women and men” and 3.1 “Ratios of girls to boys in primary, secondary and tertiary education” and listed the data sources normally used for these indicators.

18. Definitions and age groups used for the compilation of the indicators at the national level do not always match those used by the international agencies. The agency may thus need to conduct adjustments to comply with international definitions. For instance, Yemen’s enrolment ratios in primary are lower than the UIS figures due to the use of different age groups. At the country level, the basic education age group is used instead of the primary education age for the calculation of the indicator. Since basic education is provided to older students, more students are out of school and, as a result, the national estimates are lower.

19. The use of different definitions of “Literate” for indicator 2.3 also leads to data discrepancies. UIS does not consider every person finishing primary education as being literate, while countries like Yemen do.

20. Lack of accurate information on age can compromise the calculation of the MDG indicators. In Sudan, a large portion of the population lacks birth registration documents, making it difficult to determine if the students are within the age group of interest or not. In addition, some censuses and surveys do not include areas affected by the recent conflicts and the resulting estimates are not nationally representative.

21. Regarding indicators 2.1 and 3.1, discrepancies between national and international data may also arise from the use of different population estimates (UIS utilizes UN population estimates while countries use their own estimates). Another reason for discrepancies is the lack of recent data in UIS due to the time lag from the production of the data in the country to its dissemination to the international statistical system. In order to avoid this, data reporting from national to international agencies needs to be more timely and efficient.

22. Mr. Talal El Hourani (UIS) clarified doubts on other education related MDG indicators. At the international level, indicator 3.1 (ratios of girls to boys in primary, secondary and tertiary education) is calculated by dividing the gross enrolment ratio (GER) of girls by the GER of boys. Some countries use the absolute numbers of pupils enrolled rather than the GERs thus obtaining different values for the indicator. It was also explained that religion education is considered as part of general education.

Employment

23. Ms. Valentina Stoevska (ILO) explained in detail the methodology for the calculation of the labour related indicators and the data sources used by ILO for MDG monitoring at the international level. She pointed out that the main reasons for discrepancies between national and international figures for the labour indicators are: different data sources, different series from the same source or different definitions or classifications.


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24. Some data discrepancies regarding indicator 3.2 “Share of women in wage employment in the non-agricultural sector” are due to the fact that some national data do not reach the ILO. This is the case for Sudan, where data from several LFS and one census have not yet been reported to ILO.

25. When national data are not available for a particular year, ILO uses proxy variables like share of women in total employment (rather than paid employment). When there is no auxiliary variable that can be used as a proxy indicator, ILO uses imputations for missing data, but these are only used to calculate regional aggregate figures and are not presented as country estimates in the database.

26. ILO supports data collection in countries, upon request.

Water and sanitation

27. Mr. Rolf Luyendijk (UNICEF) presented indicators 7.8 and 7.9, “proportion of population using an improved drinking water source” and “proportion of population using an improved sanitation facility”, respectively. He explained that linear regressions are applied to all available country data to produce estimates. The estimates are produced separately for the urban and rural populations and these in turn are then averaged - using population weights - to produce the total for the country. The population weights are the population estimates of the UN Population Division. Data sources used by the UNICEF/WHO JMP are mostly household surveys and, in a few cases, population censuses.

28. Due to the nature of the method used by JMP to calculate their estimates, the national and international data will typically be different. Consistency is expected in terms of levels and trends if the same definitions are used. This is however not always the case. Discrepancies often arise due to:

a) Use of different definitions: different definitions of safe water and improved sanitation are used at the international and national levels.

b) Insufficient or vague response categories in the survey: under a response category “other”, there may be improved and/or unimproved water sources/sanitation facilities. A commonly used response category is “traditional latrine”. As the concept of “traditional latrine” varies from country to country, it is not possible, without additional information, to determine whether this type of sanitation facility is to be considered improved or not. JMP applies adjustments for these categories to compensate for the fact that some of these facilities may be improved and others not.

29. A major cause of discrepancies in the Western Asia region is the use of the tanker truck delivered water category, which is not considered an improved source of drinking water at the international level because no information on the actual source of water is provided. Some countries, such as Iraq, consider this source as improved.

30. Some countries expressed their concern with the classifications used by JMP. On the one hand, it is not always clear how to assign a specific country sanitation facility to the international sanitation categories. For example, how to fit the traditional pit latrines in the international standards? On the other hand, the international classification uses terms which are not familiar in some countries. Therefore, interviewers cannot use these terms while collecting


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data. Mr. Luyendijk suggested that each country uses their own wording as long as they report the data in the international classification format. China, for instance, has mapped its own classification to the international one.

Child mortality

31. Ms. Danzhen You (UNICEF) explained that the international estimates of under-five mortality rates are an average figure obtained with LOESS and splines models applied to all reliable data available. Although, ideally, child mortality should be estimated from vital registration data, the majority of child mortality occurs in countries without adequate vital registration systems. In this case, censuses and household surveys have to be used. She indicated that one of the main reasons for differences between national and international estimates of under-five mortality rates is the existence of different data sources.

32. “Child Mortality Estimation (CME) Info” (site: http://www.childmortality.org/) is an online database set up by the UNICEF to show how the international estimates are calculated. Participants were encouraged to use it and provide feedback to UNICEF.

Assessment of data availability and discrepancies

33. Ms. Neda Jafar (Statistics Division, ESCWA) presented the results of a study1 conducted by ESCWA on the discrepancies between the data and metadata submitted by countries to ESCWA and those displayed in the global MDG website. The study compared the levels of existing data and found that 57 per cent of the data points were exact matches or presented consistent values (difference less that 10 per cent). Differences of more than 10 per cent were due in some cases to the use of incorrect calculation at the country level – for instance, use of the wrong denominator for indicator 3.2 (share of women in paid employment in the non agricultural sector). The study also suggested that more data are available in countries than in the global database.

34. Mr. Maher Sbieh (Palestinian Central Bureau of Statistics) presented an analysis of discrepancies in net enrolment ratio (NER) data. Palestine applies the ISCED classification, but uses a definition of NER different from the one used by UIS. While UIS calculates the total NER, which includes in the numerator children of primary school age attending secondary school, the Palestinian definition corresponds to NER, which excludes that group of children. Mr. Sbieh indicated that the national definition was based on the 2003 version of the Handbook on MDG indicators and could be changed to meet the current international definition. In addition, primary education is defined differently at the country level: for the UIS, primary education is completed in four years whereas for Palestine primary completion takes ten years of education. The use of different population estimates at the country and international levels also contributes to discrepancies because the national estimates may be different from those released by the UN Population Division.

35. Mr. Yassine Jmal (Institut National de la Statistique, Tunisia) reviewed the reasons for discrepancies in both poverty and literacy indicators. The discrepancies on poverty are mainly

1 Millennium Development Goals: Monitoring framework and proposals for improvement (E/ESCWA/SD/2008/IG.1/4).


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due to the adoption of different methodologies. In Tunisia, up to 2005, the absolute poverty line was calculated on the basis of energy intake requirements different from those used by the World Bank for the calculation of national poverty lines. For 2005, no discrepancies are expected because the NSO used the World Bank methodology (the World Bank data for this year have not yet been released). Another reason for discrepancies is the use of different reference populations for urban and rural areas, as the national estimates differ from those calculated by the UN Population Division.

36. In Tunisia, literacy data come from labour force surveys (LFS) and censuses. The methodology applied to calculate the indicator is the same as the one used by the UIS and therefore discrepancies are small. However, several data sources are missing at UIS (annual LFS from 2005 to 2008). In fact, the NSO does not process and disseminate data on literacy unless there is a specific request for the data. The slight discrepancy in the 2004 census figure (91.4 vs. 91.3) is due to the use of preliminary data by UIS. The final figure is now available.

37. Mr. Dhyaa Awad Kadhum (Central Organization for Statistics, Iraq) presented a comparison of national and international data for indicator 3.2 (Share of women in wage employment in the non-agricultural sector). Both data come from employment surveys but from different years. The population covered in different surveys in not necessarily the same which may be the reason for the significant difference in the levels.

38. Ms. Wafaa Maged (Central Agency for Public Mobilisation and Statistics, Egypt) reviewed the discrepancies in child mortality data. She explained that her country relies mainly on DHS data because vital statistics from civil registration are not reliable. International estimates display trends similar to the national ones, although they are obtained from models applied to a number of sources, including DHS.

39. After the presentations, participants formed four working groups to assess, discuss and clarify with each agency differences between national and international data and to identify data gaps at the international level (see Annex 2). ESCWA will help UNSD in following up with countries after the workshop, to ensure that the data sets missing at the international level are sent to the responsible international agency.


40. The Workshop agreed on a number of recommendations for further actions at the national, regional and international levels to reduce data discrepancies:

a) To harmonize methodologies used by countries to produce the MDG indicators. This can be achieved through statistical capacity building and improved collaboration between UN agencies and NSOs in the calculation of the indicators. Technical assistance to NSSs at all levels of work is needed – from sampling to surveying or calculating the indicators – to ensure that the correct methodology and the adequate standards are being followed.

b) To provide technical assistance to member states upon request – including the organization of workshops covering all MDGs in which specialized international agencies are invited to meet with the NSOs and corresponding line ministries to review estimates and NSOs are exposed to the methodologies used at the international level.

c) To improve communication between NSS and international agencies. Agencies were encouraged to improve dialogue with countries and increase efforts to capture all data


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available at the country level. Countries in turn should communicate any new data to international agencies in a timely manner. Making lists of available data sources in every country, or data repositories, accessible to international agencies will prevent agencies from contacting the countries too often, thus reduc ing the country burden.

d) To regularly organize workshops with countries and international agencies to identify data gaps at the international level and share experiences in producing the MDG indicators. To involve country offices of international agencies so that they become aware of any gaps in the data compiled by international agencies and identify strategies to better capture national data.

e) To organize a follow up workshop in 2010 focusing on indicators not covered in the current workshop and with the participation of other international agencies - such as the UN Population Division, World Bank, WHO and IPU - to further discuss existing discrepancies in other MDG indicators and pave the way for more interactive collaborations.

f) For countries, to disseminate data together with metadata in order to facilitate the use of national data by international agencies.

41. On logistic issues regarding the organization of workshops, countries requested UNSD and ESCWA to send the completed questionnaires – with both national and international data – about two months in advance, so that participants have more time to review the data sheets. To meet this timing, countries should be requested to complete the questionnaire four months before the date of the Workshop.

42. Country participants pledged for a better coordination among international agencies in order to avoid duplicated requests from international agencies to the NSS. They also requested agencies to refer back to the NSOs for data checking and alert them of existing discrepancies.

Coordination of national statistical systems 43. Ms. Neda Jafar (ESCWA) presented the results of the questionnaire on the coordination of national statistical systems and reporting data mechanisms to the international agencies that was sent to NSOs in preparation for the workshop. The results – based on the replies from Egypt, Iraq, Morocco, Occupied Palestinian Territory, Sudan and Yemen – indicated that the NSOs have the role to coordinate the reporting of MDG data produced by various national agencies and are involved in the production of the MDG country reports. However, this authority is not always mandated by law. Some NSOs also lack the authority to verify the quality of the data. Yemen, Occupied Palestinian Territory, Sudan and Morocco have a central repository of data for the MDG indicators available online. Only two countries – Yemen and Morocco – include a calendar of dissemination of official MDG data in the repository: in Morocco, the calendar is updated every two months, in Yemen yearly.

44. Ms Mahasin Abdel Ghani (Central Bureau of Statistics, Sudan ) and Mr. Fadel Alchikh (Central Bureau of Statistics, Syria) presented some of the issues faced by their countries in the coordination of the NSS. Ms. Mahasin Abdel Ghani informed the meeting of the coordination difficulties in Sudan’s NSS because there are two separate statistical systems - one for the north and one for the south of the country. There is a strong need to improve statistical capacity in


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both systems and to ultimately unify them. Mr. Fadel Alchikh emphasized the need to create mechanisms for automatic transfer of data among national agencies and from these to international agencies.


45. In order to improve national coordination, the Workshop recommended:

a) To establish an MDG network at the national and regional levels for MDG focal points, facilitated and maintained by ESCWA.

b) To organize workshops bringing together line ministries and NSOs to promote partnerships.

and for countries:

c) To enhance national coordination and partnerships and establish mechanisms for MDG monitoring and reporting at the national level.

d) To implement central MDG databases in a collaborative manner with the involvement of the NSO and other official statistical units in the country.

e) To unify the data and metadata at the country level. The metadata can be compiled in national handbooks on indicators and disseminated online to users and producers of data.

f) To minimize to the extent possible the delay in reporting data and metadata to international agencies. Timely reporting will improve consistency between national and international data.

g) To disseminate national results of censuses and surveys through national websites thus facilitating access to a larger audience.

46. ESCWA offered to translate the questionnaire on coordination in Arabic and send it again to countries to improve response.

The New MDG Monitoring Framework 47. Ms. Maria Martinho (UNSD) explained the process that lead to the revised MDG monitoring framework. This process started in 2005, when UN Member States at the UN World Summit committed to four new targets. The IAEG was then mandated to identify the indicators to monitor progress towards these targets. The revised MDG framework – with the new targets and indicators – has been effective since January 2008. The framework was presented to the 39th session of the United Nations Statistical Commission, to give representatives of national statistical offices the opportunity to review the new indicators and related implications in terms of their national data collection programmes and their reporting to the international system and to the General Assembly at its 62nd Session in 2007.

48. Ms. Sara Duerto (UNSD) presented the methods of calculation for the new indicators, the data sources and the international agencies responsible for the compilation of data on these indicators. A few participants raised concerns regarding some of the indicator definitions, especially regarding age groups and the population covered in the denominators. For


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comparability purposes, international agencies must use the same age groups for every country while countries individually may use age groups adequate to the ir own national circumstances. For instance, some countries calculate adolescent child fertility from 12 years onwards. Also, in some countries, like Oman, there is a registry of pregnant women and thus the ante-natal care indicator is determined among the registered pregnant women rather than among women with a live birth.


49. During the discussions, the participants recommended:

a) To organize a Workshop for countries in the ESCWA region, on the calculation of the MDG indicators, based on the me thodology explained in the new Handbook and focusing on the calculation of the new MDG indicators.

b) To translate the new Handbook on MDG Indicators into Arabic as soon as possible and to produce a document in which the names of the indicators appear side by side in English, French and Arabic.

c) To train countries on the methods used by international agencies to calculate indicators, produce estimates and fit models.

d) To support countries with scarce statistical capacity in conducting data collection.

e) To strengthen vital statistics in the region.

Other recommendations 50. To ensure the full participation of countries of the ESCWA region in the MDG monitoring process, participants further requested the IAEG:

a) To ensure the participation of countries of the ESCWA region in IAEG meetings.

b) To release an Arabic version of the global MDG website as soon as possible. ESCWA and countries in the region were invited to assist in this task.


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Annex 1. List of participants

Country Participants

Egypt Wafaa MAGED Senior Specialist Central Agency for Public Mobilization and Statistics Cairo, Egypt

E-mail: [email protected] Tel.: (2 022) 4500786 Fax: (2 022) 4024099

Egypt Rasha Awad Director of Policies’ Monitoring and Evaluation Departement The Egyptian Cabinet, IDS C Magless El-Shaab, ElKasr El-Einy, Cairo Senior Economic Advisor to the Minister Ministry of Manpower and Immigration Cairo, Egypt

E-mail: [email protected] Tel: 202 24180859 20227929292 20101670812 Fax: 20227929222

Egypt Maha Ayad Senior Specialist Central Agency for Public Mobilization and Statistics Cairo, Egypt


Iraq Dhyaa Awad Kadhum Director of Human Development Statistics Central Organization for Statistics And Information Technology (COSIT) Ministry of Planning P. O. Box 8001 Baghdad, Iraq

E-mail: [email protected] Tel: (9 647) 901 744224

Iraq Mohammed Jaber Hwoal AlTaae Ministry of Health Baghdad, Iraq

E-mail: [email protected] Tel : 009647901446432

Jordan Basem Kanan Head of monitoring division Ministry of Planning and International cooperation Amman , Jordan

E-mail: [email protected] Tel : +6962777484763


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Jordan Manal Sweidan Department of Statistics Amman, Jordan

Email: [email protected], [email protected] Tel: 00962797194154 Fax: 009625300710

Lebanon Mayssaa Daher Central Adminstration for Statistics Math Statistician Beirut, Lebanon

Email : [email protected] Tel : 961 1 373 167 Fax : 961 1 373 161

Lebanon Lara Badre Central Adminstration for Statistics Sociologist Beirut, Lebanon

Email : [email protected] Tel : 961 1 373 160 Fax : 961 1 373 161

Morocco Noure Ddine Ibnoussina Direction de la Statistique Haut Commissariat au Plan Rue Mohamed Belhassan el Ouazani, Haut Agdal, 10001, Rabat Maroc, BP. 178


Morocco Mostafa Bouabdellah Chef de service des enquetes et analyses statistiques Ministere de l'education Nationale, de l'enseignement superieur, de la formation des cadres et de la recherche scientifique Departement de l'enseignement Scolaire Bab Rouah Rabat, Morocco

E-mail: [email protected] Tel.: (2 125) 37 6872 21 (2 126) 627463 Fax: (2 125) 37 772046

Occupied Palestinian Territory Maher Sbieh Head of MDGs Team in PCBS Director of Education and Culture Statistics Departement PO Box 1647 Ramallah Palestine

E-mail: [email protected] Tel.: (9 702) 2 2982700 Fax: (9 702) 2 2982710

Oman Ahmed Al-Yaqoubi Statistician Ministry of National Economy Oman

E-mail: [email protected] [email protected] Tel.: 00 968 24 695 169 Fax: 00 968 24 695 169


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Oman Salah Al-Muzahmi Director, Health Information and Statistics Ministry of Health Muscat, Oman

Email : [email protected] Tel: 00968 24 605 474 Fax: 0096824696533

Saudi Arabia Abdalla Zayeb AlNoughoushi Statisitcian NSO-Saudi Arabia

Email: [email protected] Tel: 009661401413811360 Fax: 0096614050648

Sudan Mahasin Abdel Ghani Director of the Statistics Office, River Nile State Sudan Government Central Bureau of Statistics PO Box: 700 Khartoum, Sudan


Syrian Arab Republic Sonia Khoury Head, Health Policies Department Ministry of Health Alabed Street, Ministry of Health Planning Directorate Damascus, Syria

E-mail: [email protected] Tel : 963944535022 Fax: 963113340451

Syrian Arab Republic Fadel Alchikh Deputy Director General Central Bureau of Statistics Nizar Kabbani Street Damascus, Syrian Arab Republic


Tunisia Yassine Jmal Head, Household consumption and Living Standards Section National Institute of Statistics

E-mail: [email protected] Tel.: (2 169) 8 915 869 Fax : 21671792559

United Arab Emirates Abdul Gader Ahmed Almusawi National Bureau of Statistics Dubai, United Arab Emirates

E-mail: [email protected]; [email protected] Tel.: (9 712) 612 3271


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Yemen Abdulkarem Ali ALAHMAR General Manager, Training Programme Central Statistical Organization, Ministry of Planning and International Cooperation Al-Huriah St. Sana'a, Yemen P.O. Box 13434

E-mail: [email protected]; [email protected] Tel.: (9 671) 250 108 (9671) 711848989 Fax: (9 671) 250 664

Yemen Omar Hussein Mujalli Assistant Deputy Minister Ministry of Public Health Al Hassaba, Sanaa, Yemen

E-mail: [email protected] Tel.: 967 77779088 Fax: 967 1 251622

Resource persons

United Nations Children's Fund (UNICEF) Danzhen You Division of Policy and Planning United Nations Children's Fund (UNICEF) 3 UN Plaza New York, NY 10017 USA

E-mail: [email protected] Tel.: (1) 212 326 7235

United Nations Children's Fund (UNICEF) Rolf Luyendijk Sr. Statistics and Monitoring Specialist Water and Sanitation UNICEF Statistics and Monitoring Section UNICEF House 3 UN Plaza, Rm. H-480 New York, NY 10017

E-mail: [email protected] Tel.: (212) 303-7975

UNESCO Institute for Statistics Talal El Hourani Statistician, Assistant Programme Specialist Education Indicators and Data Analysis Region of the Arab States UNESCO Institute for Statistics C.P. 6128, Succ. Centre-Ville Montreal, QC, Canada H3C 3J7

E-mail: [email protected] Tel.: (514) 343 7694 Fax: (514) 343 5740

Istituto Nazionale di Statistica Giuseppe Sindoni, IT Senior Technologist Istituto Nazionale di Statistica Via C. Balbo, 16 - 00184 Roma, Italy

Email: [email protected]


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International Labour Organization Valentina Stoevska Department of Statistics ILO Geneva, Switzerland

Email: [email protected] Tel : 39 06 46734473 (4574)

DevInfo Suppot Sameer Thapar DevInfo Expert DevInfo Support Group Delhi, India

E-mail: [email protected]

Organizers

Statistics Division, United Nations Maria Martinho UNSD, DESA 2 UN Plaza, DC2-1656 New York, NY 10017

E-mail: [email protected] Tel.: (212) 963 4947 Fax: (212) 963 9851

Statistics Division, United Nations Sara Duerto Valero UNSD, DESA 2 UN Plaza, DC2-1648 New York, NY 10017

E-mail: [email protected] Tel.: (1212) 963 2038

Economic and Social Commission for Western Asia (ESCWA) Neda Jafar Statistics Division United Nations Economic and Social Commission for Western Asia UN ESCWA PO Box 11-8575 Riad El-Solh Square Beirut Lebanon

E-mail: [email protected] Tel.: (96 11) 978 344 Fax: (96 11) 980 511

Economic and Social Commission for Western Asia (ESCWA) Roy Doumit Statistics Division United Nations Economic and Social Commission for Western Asia UN ESCWA PO Box 11-8575 Riad El-Solh Square Beirut Lebanon

Email: [email protected] Tel: 009611978368


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Economic and Social Commission for Western Asia (ESCWA) Farah Ramadan Statistics Division United Nations Economic and Social Commission for Western Asia UN ESCWA PO Box 11-8575 Riad El-Solh Square Beirut Lebanon

Email: [email protected] Tel: 009611978356

Annex 2. Summary of identified causes of discrepancies

Indicator 2.1: Net enrolment ratio in primary education Egypt CAPMAS does not include the Azhar educational institutes in their education indicators

while UIS data includes Azhar school education. Iraq The Kurdistan region was not included in the UIS estimates because it was not submitted

by the country. Jordan Difference in population estimates; use of different age groups. Lebanon No data submitted. Morocco Different denominator (UIS uses UNPD estimates; country uses national estimates). Occupied Palestinian Territory

1) Basic education was used by the country instead of Primary education, which is used by UIS. 2) Difference between UN population estimates used by UIS and national population estimates used by the country to calculate NER.

Oman No data submitted by country. Sudan No data submitted by country. Syria No data submitted by country. Tunis ia 1) UIS covers primary years of education (total of 6 years of studies -6 to 11 years old),

while country covers basic years of education (8 years of studies). 2) UIS include children of primary age enrolled in secondary education in numerator; country does not. 3) Differences in population estimates (UIS uses UNPD estimates, while country relies on NSO estimates).

United Arab Emirates No data submitted by country. Yemen UIS calculates NER on the primary years of education (total of 6 years of studies -6 to 11

years old), but country covers basic years of education (8 years of studies). Country NER tends thus to be lower than UIS figures (because drop outs increase by age).

Follow-up after the Workshop: - Iraq: submit available data from the Kurdistan region.

Indicator 2.3: Literacy Rate of 15-24 years olds Egypt Consistent data. Iraq IHSES 2007 data missing at UIS Jordan More data available at the country. Lebanon No data submitted by country. Morocco 1990 and 1994 data missing at UIS Occupied Palestinian Territory

Data missing at UIS: 1995 DHS, LFS annually 2000 to 2003, 2005 and 2008.

Oman No data submitted by country. Sudan No data submitted by country. Syria No data submitted by country. Tunisia 2004 census: small discrepancy in value because UIS used preliminary data while the

country uses now the final figure. LFS data years 2005 to 2009 missing at UIS.

United Arab Emirates No data submitted by country. Yemen 2004 census, 2005 HBS and other data missing at UIS

Follow-up after the Workshop: - Iraq, Jordan, Morocco, Occupied Palestinian Territory, Yemen: submit missing data to UIS - Tunisia : submit to UIS 2004 census final data and LFS data years 2005 to 2009.


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Indicator 3.2: Share of Women in wage employment in the non-agricultural sector Egypt For most points, very small data discrepancies (due to rounding), but there is some

difference in the 1990 data (CAPMAS will double check). 2007 data missing at ILO. Iraq 1990, 2004, 2006 and 2008 data missing at ILO. Jordan Discrepancies due to difference in series: ILO only has data on total paid employment;

while country has data on the indicator. 2006 and 2007 data missing at ILO. Lebanon No data submitted by country. Morocco Big discrepancies because country used a denominator different from that used

internationally: the total number of women employed. Occupied Palestinian Territory

2008 data missing at ILO

Oman No data submitted by country. Sudan No data submitted by country. Syria No data submitted by country. Tunisia No data submitted by country. United Arab Emirates No data submitted by country. Yemen 2004 census data missing at ILO.

Follow-up after the Workshop: - Egypt: double check 1990 data value; submit missing data to ILO - Iraq, Occupied Palestinian Territory, Yemen: submit missing data to ILO - Jordan: submit missing data and indicator series to ILO - Morocco: recalculate indicator.

Indicator 4.1: Under-five mortality rate Egypt Level and trends are consistent. Country uses original data sources while Unicef uses a

model. Iraq 1999 values very different Jordan Discrepancies due to different models/methods:

- country uses DHS original sources and linear regression in the years between two DHS surveys; the linear regressions are applied to two data points only; - Unicef uses loess and splines applied to all data points available.

Lebanon No data submitted by country. Morocco Similar trends; Unicef values slightly higher. SPFH 1999 and SPPH 1991 data missing at

Unicef. Occupied Palestinian Territory

Level and trends are very similar.

Oman No data submitted by country.

Sudan No data submitted by country.

Syria No data submitted by country.

Tunisia Level and trends are very similar. United Arab Emirates Unicef data are old and should be updated with more recent data available in the country,

Unicef is using the wrong contact: should use Ministry of Economy instead of Planning. Yemen 2004 census and 2007 data missing at Unicef. Overall levels and trend similar.

Follow-up after the Workshop: - Iraq: confirm 1999 value and communicate to Unicef. - Morocco: submit missing data to Unicef. - UAE: submit recent data to Unicef


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Indicators 7.8 and 7.9: The proportion of population using an improved drinking water source and the proportion of population using an improved sanitation facility, urban and rural Egypt Census 2006 data missing at JMP. Iraq IRHBS 2005 and IHSES 2007 data missing at JMP. Jordan Very different level. No data sources submitted by country. Lebanon No data submitted by country. Morocco No data sources submitted by country. Occupied Palestinian Territory

Country levels are higher. Discrepancy due to use of different units: country uses percentage of households while JMP uses percentage of population.

Oman No data submitted by country. Sudan No data submitted by country. Syria No data submitted by country. Tunisia Census 2004 data missing at JMP. United Arab Emirates No data submitted by country. Yemen Census 1994, MYFHS 2003, Census 2004, MHHBS 2005 and MICS 2006 data missing

at JMP.

Follow-up after the Workshop: - Iraq, Egypt, and Tunisia : submit missing data to UIS. - Jordan: check data to confirm reason for discrepancies.

workshop on mdg monitoring - united...

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