Report on: Workshop on Household Surveys and ...€¦ · Web viewTitle Report on: Workshop on...

Workshop on MDG MonitoringKampala, Uganda, 5-8 May 2008

Report

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

Table of contents

Introduction 3

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

Objectives of the Workshop....................................................................................................................4

MDG monitoring at the sub-national level and administrative data...............................................5

Discrepancies between national and international data................................................................6

Net enrolment ratio and literacy.............................................................................................................6

Poverty....................................................................................................................................................6

Water and sanitation...............................................................................................................................7

Child mortality.........................................................................................................................................8

Working groups and plenary discussion..................................................................................................8

NSS coordination and data reporting..........................................................................................10

The New MDG Monitoring Framework........................................................................................12

Other issues.................................................................................................................................13

Recommendations.......................................................................................................................13

Annex 1. List of participants....................................................................................................16

Annex 2. Session on sub-national monitoring and administrative data: Summary of the

Working Groups...........................................................................................................................22

Annex 3. Session on discrepancies between national and international data: Summary of the

Working Groups...........................................................................................................................25

Annex 4. Summary of gaps and discrepancies between national and international data.......29

2

Introduction1. The Workshop on the Millennium Development Goals (MDG) Monitoring was held in Kampala on 5-8 May 2008. The workshop was organised by the United Nations Statistics Division (UNSD) in collaboration with the United Nations Economic Commission for Africa (UNECA). It was hosted by the Ugandan Bureau of Statistics (UBOS).

2. Participants included representatives from 17 National Statistics Offices - namely Botswana, Cameroon, Ghana, Kenya, Lesotho, Liberia, Malawi, Mauritius, Mozambique, Namibia, Norway, Sierra Leone, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe -, from international agencies - United Nations (UN), United Nations Educational Scientific and Cultural Organisation (UNESCO), United Nations Children’s Fund (UNICEF), World Bank - as well as local participants from Department for International Development (DFID), Ministry of Local Government and Makerere University. The list of participants is available in Annex 1.

3. The Deputy Director of Statistics, Mr. Male Mukasa, welcomed the participants to Uganda and to UBOS. Highlighting on the importance of NSOs in monitoring development goals in general, he further stated that monitoring was central in achieving the MDGs. He noted that only eight years remained to the 2015 MDGs target and that everyone needed to work hard in full throttle in order to achieve the monitoring objectives. He concluded by wishing everyone a productive workshop.

Opening addresses

4. Three opening addresses were given. The first address was given by Mr. Ben Kiregyera, Director for African Centre for Statistics, who noted that much of Africa was in danger of failing to achieve all the MDG. He commended on the strides that had been taken to make statistics one of the clusters imbedded within the thematic clusters for scaling up interventions as identified by the MDG Africa Working Group. Noting the rise in statistics’ profile globally, he stated that statistics were being used to drive the outcomes that policies were meant to achieve. This in itself was a significant move from the traditional role that consisted only of the measurement of outcomes. In contrast to the global picture, he noted the immense challenges that were still facing African policy development where statistics were still not being mainstreamed into planning, policy and budget development. Prior to concluding, he noted the discrepancies between national and international data and the importance of resolving those differences. In conclusion, he saluted UNSD in providing technical leadership that had resulted in improved products and services that were currently being offered by the National Statistics offices (NSO).

5. The second address was given by his Excellency Mr. Percy W. Misika, Food and Agriculture Organisation Representative of the United Nations, on behalf of the UN Resident Coordinator. He began by noting how the adoption of the Millennium Declaration by the UN General Assembly had marked the dawn of a new millennium in ensuring global cooperation. He said that the review at the World Summit in 2005 had indicated that countries needed to triple their efforts in order to achieve these goals. He further went on to say that MDGs are technically feasible but that the current situation in some countries constrained the effectiveness and achievement of these goals. He stated that tracking MDG goals would assist policy makers, civil society and governments in developing appropriate socio-economic policies. He then proceeded on to state that achieving the MDG goals in Africa held the promise of saving many lives and


added that the availing of good statistics would be central in ensuring success. He concluded by calling on countries to go beyond mere interest in regards to the application of statistics tools to meeting the monitoring tools.

6. The third and final address was given by Mr. Ackim Jere, representative of the SADC Secretariat. In unison with the previous two speakers, he welcomed all participants to the workshop. He then gave a brief overview of the SADC Development Account where UNSD is the executing agency. Prior to thanking UBOS for providing the venue, he called all SADC countries to attend an addendum after the workshop to review the progress made on the Development Account.

7. Ms. Francesca Perucci, on behalf of UNSD, welcomed everyone to the workshop noting the huge progress that had been accomplished as well as the progress needed to be made in the future. She stated that MDGs were now well established despite changes in the monitoring framework, systematic tracking was essential in sustaining development and reiterated the African Statistical Commission’s concern regarding the capabilities of the countries in producing the data that was needed. She stated the priority and commitment held by UNSD towards African countries. Ms. Perucci expounded on the key role of coordination in order to improve credibility and quality of the statistics produced. She alluded to the work done by the IAEG in addressing the differences between international and national data and stated that countries needed to work together to improve data quality and credibility.

Objectives of the Workshop

8. This workshop is one of the first initiatives to implement recommendations made by the international agencies and the countries to improve the monitoring of MDG indicators. The objectives of the Workshop are:

To develop guidelines on how to improve coordination of reporting mechanisms; To review existing discrepancies between national and international data, identify data

gaps at the international level and develop recommendations to address the gaps and discrepancies;

Review methodological issues in MDG monitoring at the national and sub-national levels;

Present the new targets and indicators that have been recently adopted, and review the corresponding metadata.

4


MDG monitoring at the sub-national level and administrative data9. The session on MDG monitoring at the sub-national level started with four presentations:

MDG data at the sub-national level: relevance, challenges and IAEG recommendations (by UNSD).

This presentation informed the Workshop about the recommendations on MDG sub-national monitoring of the IAEG on MDG Indicators, outlined the relevance and the challenges of sub-national monitoring for countries, discussed the pros and cons of each data source, reviewed common approaches to combine data sources and briefly listed some advantages of using GIS for sub-national monitoring.

Use of administrative data sources and production of sub-national data for MDG monitoring in African countries: challenges and opportunities (by UNECA-ACS).

This presentation introduced the MDG Mapper, an online tool developed by UNECA for comparing, with maps, progress among African countries towards achieving the MDGs. The MDG Mapper displays sub-national data, when available. The presenter illustrated the use of MDG Mapper for sub-national monitoring with an example of net primary enrolment data for provinces in Ethiopia and poverty rates in different areas within Ghana.

Use of census and administrative data in the monitoring and education indicators: an international perspective (by UIS-UNESCO).

The representative of UIS-UNESCO explained that the enrolment data came mostly from administrative data provided by countries from their annual school census conducted by the Ministry of Education (UIS has data for 140 countries) while literacy data tended to be collected by NSOs in population censuses.

Mauritius: The MDGs-country experience (by Central Statistics Office of Mauritius). The presenter explained that most MDG indicators were not relevant at the sub-national level because Mauritius is a small country. Only two MDG indicators are monitored at sub-national level: indicators 7.8 and 7.9 (respectively, Proportion of population using an improved drinking water source and Proportion of population using an improved sanitation facility) are compiled for urban and rural areas. The data are obtained from housing censuses, with full coverage ensuring the reliability of the monitoring at the sub-national level.

10. After the presentations, the participants were divided into two groups to share experiences and put forward recommendations for improving MDG monitoring at the sub-national level. The conclusions presented by the two groups are shown in Annex 2. During the group work and the plenary discussions, the following key issues were noted:

1) National figures tend to mask sub-national areas where interventions are mostly needed.

2) Geological Information Systems (GIS) are a useful tool in handling geographically referenced data.

3) The collection of administrative data still presents a challenge for many African countries. The representative of Botswana explained how policies in Botswana

5


had enhanced the collection of births data by restricting enrolment into schools and access to medical care to children with registered births. The delegate noted that whilst policy had enhanced the collection of data on births, Botswana still faced the challenge of collecting data on deaths.

Discrepancies between national and international data11. At the session on discrepancies between national and international data, UNSD presented the recommendations by the IAEG and EGM on MDG indicators on resolving discrepancies between international and national data sources. It was clarified that resolving did not mean to eliminate discrepancies but rather to understand the reasons why these discrepancies arise. In some cases, this understanding may lead to the reduction of the data differences, while in others there may be good reasons why the national and international data are distinct. In this case, it is important to clarify the user about the reasons explaining the differences so that the user can make an informed decision on which data are appropriate to use.

12. Presentations were given by UIS-UNESCO, UNICEF, UNICEF-JMP and the World Bank explaining the process used by these agencies to compile data from national sources and to produce international estimates. The representatives of Kenya and Malawi then presented respectively for indicators on water and sanitation (MDG indicators 7.8 and 7.9) and for selected education indicators (MDG indicators 2.1 and 2.3) their findings regarding data discrepancies between national and international data.

Net enrolment ratio and literacy

13. In the UIS-UNESCO presentation, Mr. Said Ould Voffal (UIS-UNESCO) justified the need to produce estimates for enrolment data: since the data received from countries is not comparable, estimation is needed to obtain a set of comparable data. On the other hand, for literacy data discrepancies between national and international data are rare because UIS tends to use directly the data received from the country – discrepancies can only arise if different sources are used or if country data are missing at UIS (in which case, UIS sometimes produces estimates using the GALP model). Mr. Said Ould Voffal also informed the participants that UIS consults with countries after cleaning the data and producing country estimates, but often countries do not reply.

14. Mr. Shelton Kanyanda (Malawi) listed the different data sources used in his country for the indicators 2.1 and 2.3. There are data discrepancies for both indicators. In Liberia, primary education consists of eight years. The international definitions define a shorter period of study for primary education and therefore the country data on indicator 2.1 (total net enrolment in primary) has been adjusted by UNESCO accordingly to maintain international comparability. For indicator 2.3, UNESCO is using different sources: the Malawi NSO provided data from the 1998 and 2005 Integrated Household Surveys while the international MDG database shows data from the 1998 Population and Housing Census.

6


Poverty

15. Mr. Johan Mistiaen (World Bank) started his presentation by clarifying that it is pointless to discuss discrepancies for indicator 1.1 (Proportion of population below $1 (PPP) per day) because this indicator is not adequate for country monitoring. For this indicator, the discussion on discrepancies should focus on the differences in the data used by the World Bank to calculate the indicator and the data available at the national level. The World Bank has an economist assigned to every country who is responsible for gathering the country data and submitting them to the World Bank Headquarters. Mr. Johan Mistiaen indicated that the World Bank has received data from all countries represented at this Workshop, except for Mauritius and Liberia. The representative of Mauritius clarified however that data are available at the country level.

Water and sanitation

16. Mr. Rolf Luyendijk (UNICEF-JMP) explained in his presentation that UNICEF-JMP applies linear regressions to fit all country data points available in order to estimate the water and sanitation indicators (7.8 and 7.9) – except if only two points too close in time are available then the average of the points is used. He also indicated that JMP abandoned the use of administrative data reported by the national governments because the data were unreliable, showing high variability from year to year. Survey data tends to be more consistent and therefore from 1997 onwards, only census and survey data have been used at the international level. Whenever the data values are not consistent, UNICEF-JMP contacts the country authorities to investigate the reliability of the data.

17. In her presentation on indicators 7.8 and 7.9, Ms. Mary M. Wanyoni (Kenya) showed that there are differences between the national and international data, especially for the sanitation indicator (40 versus 80). She explained the differences in definitions and methodologies used:

i. At the national level, improved drinking water sources do not include rain water (because it is rare to find households relying on rainwater) while this source is included at the international level; for sanitation, the international definition only considers the facility improved if it is not shared while the country does not use this criterion. The representative of Kenya indicated that the information on whether the sanitation facility is shared or not only started to be collected from 2003 onwards and requested the international agencies to distribute the methodology used to estimate access to non shared sanitation facilities for years prior to 2003.

ii. The Kenya Bureau of Statistics focuses on percentage of households with access rather than percentage of population (which is used at the international level) because the policy interventions are drawn at the household level. However, there are plans to start compiling data for the population.

18. Overall, the differences between national and international data are often due to the use of different definitions. For example, protected dug wells are included in the international definition of improved drinking water source but excluded from the national definition used by South Africa.

7


19. National data indicate that in some countries, like Kenya and Ghana, large proportions of the population use shared improved sanitation facilities. Country representatives were concerned by the omission of the shared facilities in the international definition of the sanitation indicator. Delegates discussed the issue of shared and public toilets and called on the alignment of definitions used at the national and international levels.

Child mortality

20. Mr. Ediberto Loaiza (UNICEF) explained the method used to estimate the under five mortality rate and listed the recent actions taken on the estimation of child mortality and to improve the discussion between international agencies and countries:

- the inter agency group for child mortality estimation is responsible for setting the methodology used for producing child mortality estimates;

- the child mortality database (CME Info) is now available online (http://www.childmortality.org/) – this tool allows users to apply the method of UNICEF to estimate child mortality rates and provides access to the original data series used by UNICEF in the estimation;

- UNICEF is currently in the process of developing confidence intervals around the estimated levels of under five mortality rates;

- regional workshops have been organized by UNICEF for better coordination with NSS and for capacity building in countries.

21. The NSO representatives expressed interest in working together with international organisations to understand and apply the methodology used at the international level.

Working groups and plenary discussion

22. After the presentations, the participants were divided into three groups to discuss and understand the underlying reasons for the discrepancies between national and international data. Representatives from international agencies - UNESCO, UNICEF, UNICEF-JMP and WHO – rotated among the working groups to discuss specific issues on the indicators and to answer questions regarding the discrepancies between their data and the national data. The conclusions and recommendations found by groups 1, 2 and 3 are included in Annex 3. The data gaps and discrepancies found are listed in Annex 4. The reasons for the discrepancies included:

- Use of different definitions and methodologies;- The use of estimations by international agencies;- The use of modelling techniques (like linear regression) by international agencies;- Missing country data sets at the international level;- Different population estimates.

23. The participants also noted that since UN Population Division only revises data every two years sometimes the UN population estimates used by the international agencies do not reflect the latest population data available (for example, Mozambique has already results from the 2007 Population and Housing Census but these data are not yet reflected in the UN Population Division Estimates).

8

http://www.childmortality.org/


24. In the Working Groups discussion, the participants identified several data gaps in the international databases and agreed to continue after the Workshop the communication between NSOs and the international agencies in order to address these gaps. A summary of the data gaps found and the actions to be taken in the short term to address the current data gaps at the international level are listed below:

INDICATOR 1.1 – Proportion of population below poverty line

Botswana Data from HIES, 2003 missing at the WB.Lesotho NSO will make the missing data sets available to the WB by June 2008.Malawi NSO will provide the latest figures on poverty to the WB by June 2008.Mauritius Data from HBS 1996/7 and HBS 2001/2 missing at the WB.Namibia NSO will provide the latest figures on poverty to the WB by June 2008.Sierra Leone

Data from PHC 2004 missing at the WB. NSO will check and provide consumption and income data to World Bank if these data were collected in the 2006 CWIQ survey.

Zimbabwe Data from CSO (1995, 2001) and MPLSLSW (2003) missing at the WB.

INDICATOR 2.1 - Net enrolment ratio in primary education

Cameroon Data from 2005 EESI and 2006 MICS 3 is missing at UNESCO. MoE indicated to UNESCO these data are not reliable but this is not supported by NSO. Follow up needed between MoE, NSO and Unesco.

Kenya, Malawi, Mauritius, Sierra Leone, Uganda

Several data sets missing at UNESCO – see Annex 4.

INDICATOR 2.3 - Literacy rate

Botswana Literacy survey 1994 missing at UNESCOCameroon Several data sets missing at UNESCO – see Annex 4.Kenya Missing data set at UNESCO: WMS 1997Lesotho Missing data sets at UNESCO.Malawi Missing data sets at UNESCO: IHS 98 and IHS 2005.Mozambique Data from Household Budget Survey 2003 is missing at UNESCO.Sierra Leone, Uganda, Zambia and Zimbabwe

Several data sets missing at UNESCO – see Annex 4.

INDICATOR 4.1 – Child mortality

Botswana, Cameroon, Kenya, Liberia, Malawi, Mozambique, Namibia, Sierra Leone, Tanzania, Uganda, Zambia

Data missing at UNICEF: see Annex 4.

INDICATOR 7.8 - Proportion of population using an improved drinking water source

Botswana Data missing at UNICEF: see Annex 4.Cameroon Data from 1996 and 2001 ECAMs (HH survey) is missing at UNICEF.Liberia Data missing at UNICEF: see Annex 4.Mauritius Data from 1990 and 2000 census is missing at UNICEF.Mozambique Data from LFS 2004/5 is missing at UNICEF.Sierra Leone Data missing at UNICEF: see Annex 4.Swaziland Data from 1997 Census and 2006 DHS is missing at UNICEF.Tanzania, Uganda, Zambia,


9


Zimbabwe

INDICATOR 7.9 - Proportion of population using an improved sanitation facility

Botswana Data missing at UNICEF: see Annex 4.Cameroon Data from 1996 and 2001 ECAMs (HH survey) is missing at UNICEF.Kenya, Liberia, Malawi, Mauritius


Mozambique Data from LFS 2004/5 is missing at UNICEF.Sierra Leone Data missing at UNICEF: see Annex 4.Swaziland Data from 2007 Census is missing at UNICEF.Tanzania, Zambia, Zimbabwe


25. Participants will also follow up after the Workshop on the data discrepancies found. Even when the sources are the same the data displayed by national and international agencies can be different. For example, for indicator 7.9 for Cameroon, both NSO and UNICEF use data from 2006 MICS. However, the level is very different: NSO approx 30% while UNICEF approx. 50%. UNICEF and NSO suspect this is due to the use of different definitions and agreed to check and confirm this after the Workshop.

26. The Workshop recommended the following activities to mitigate the discrepancies:

- International agencies should make an extra effort to get the all the data from the countries.

- Better collaboration between NSS and international organisations.- Consultations with national organisations when projections are done.- International agencies should work with NSOs in particular on the census data to

minimise population discrepancies. Encourage countries to discuss with UN Population Division regarding population estimates.

- World Bank should share with countries the data used as well as the methodology. Local poverty lines ought to be published in addition to the poverty indicators from the World Bank.

27. Participants suggested that sampling errors should be provided with the data in the international MDG database. This practice is already being carried out by some international agencies for some indicators, by presenting confidence intervals (MMR, HIV prevalence).

NSS coordination and data reporting28. At the beginning of the session on the coordination of national statistical systems (NSS) and data reporting, the following presentations were given:

a. Coordination of national statistical systems in Africa (Yacob Zewoldi, UNSD).

b. Mechanisms of data reporting from national statistical systems to international organisations - results of the UNSD Survey (Maria Martinho, UNSD).

10


c. The role of regional commissions in resolving data discrepancies (Ben Kiregyera, UNECA-ACS)

d. Botswana: Coordination of the national statistical system and reporting mechanisms to international agencies, Ms. Anna Majelantle (CSO, Botswana)

e. Tanzania: Coordination of national statistical systems and reporting mechanisms to international agencies (Mr. Ephraim Kwesigabo, NBS, Tanzania)

29. The country participants noted that they had limited awareness of who their stakeholders were and that collaboration with regional and international organisations was currently weak. They also voiced their frustrations at international organisations that send questionnaires in hard copies rather than soft copies causing response delays.

30. Several representatives of NSOs shared the experiences of their own countries:

Country Coordination of NSS and data reportingBotswana Some statistical entities reside in line Ministries, but these report to Central

Statistical Office (CSO). The preparation of the MDG country reports is not done by the CSO but by the Ministry of Finance (under which CSO currently falls). Although CSO is low in the hierarchy of government, the Office has been given the mandate to have its own capital equipment i.e. vehicle and control of the NSS. Plans are underway to make CSO an autonomous organisation.

Cameroon NSO has been requesting for autonomy.Ghana A national association for users and producers of statistics has been created.

There were regular meetings in the beginning but the meetings stopped due to administrative issues. There has been recently a workshop to re-organise the NSDS system and suggest changes to sustain the system.

Kenya Although NSO has recently become semi-autonomous under the Prime Minister’s office, the coordination activities are still being carried out by the Ministry of Planning.

Lesotho Data dissemination and production are done by different entities: NSO is tasked with production but the Ministry of Finance and Planning has the role of disseminating the information. The Director of the NSO is answerable to the Permanent Secretary. However, NSO is currently being restructured to become semi-autonomous.

Liberia LISGIS became autonomous in 2004 but is still experiencing challenges in budgeting and planning.

Malawi The new NSS strategic plan is about to be launched. Overall, coordination is working very well.

Mozambique NSO negotiated for a long time until 1996 when NSO finally became autonomous. The NSO coordinates all data activities done by all ministries. Retention of skills is enabled by the fact that salaries are 75 % higher for NSO staff than other civil servants.

Namibia MDG tracking and monitoring is coordinated by the National Planning Commission even though the NSO is responsible for compiling the data.

11


Swaziland Most of the report writing on MDG is done by the Ministry of Economic Planning. The NSO plays the role of data producer.

Tanzania MKUKUTA is the localised organisation with the mandate to monitor 84 development indicators (including MDG indicators). MKUKUTA’s mandate includes reviewing progress and challenges, and recommending remedial action to resolve the challenges. MDGs are integrated into national planning activities and the NSO is the only agency mandated to coordinate statistical activities in the country. NSO is a semi-autonomous agency, and reports to the permanent secretary.

31. In the discussion on improving national coordination and data reporting, the participants recommended:

a. To establish MDG focal points in each country to deal with international agencies. The most adequate organisation should be the focus point for MDG indicators compilation and tracking (if appropriate, different focal points should be used for different topics).

b. To strengthen coordination at UN level.

c. To speed up the task by ECA/ACS to prepare a database of all statistical activities.

d. For NSO, to take the lead in coordinating statistical activities in their respective countries.

e. For international organisations, to use ICT capabilities in order to improve communication with countries.

32. ECA volunteered to create a register of census/surveys conducted in the African region to be shared with all international agencies.

The New MDG Monitoring Framework33. Representatives of UNSD presented the new MDG monitoring framework and gave an overview of the metadata and data on the new indicators. The participants requested the clarification or provided comments on the definition of the following indicators:

a. Indicator 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs: There is a medical definition of “advanced” HIV infection described in the MDG website (mdgs.un.org; cf. metadata); this term has the potential to lead to data discrepancies. HIV monitoring does not provide this information in most countries.

b. Indicator 5.5 Antenatal care coverage (at least one visit and at least four visits): It was clarified that this indicators includes two series: one corresponding to one visit or more; the other corresponding to 4 visits or more.

c. Indicator 7.7 Proportion of species threatened with extinction: The participants were unclear regarding endangered species and how these would be determined. This indicator is currently only monitored at the global level and includes the species

12


covered under IUCN Red List. This however limits the usability of the indicator as it may omit country specific endangered species.

d. Indicator 7.5 Proportion of total water resources used: Although traditional wells are not counted, they should generally be too small to influence national and global monitoring.

34. Several issues were discussed regarding the Target 1.B - Achieve full and productive employment and decent work for all, including women and young people - and the MDG indicators under this target:

a. Although the target 1.B does not include a time limit, it is assumed that the target date is 2015 because this is the overall target date for MDG.

b. Participants questioned the use of the $1 a day measurement for indicator 1.6 - Proportion of employed people living below $1 (PPP) per day. First, most countries will face methodological issues in the calculation of this indicator because the data on below $1 a day is usually collected at the household level while the employed status is collected at the individual level. Secondly, as with indicator 1.1 - Proportion of population below $1 (PPP) per day -, countries often have their own national poverty lines and should adjust the $ 1 a day based on national needs. It was clarified that the denominator for this indicator is the number of employed people.

c. Countries face challenges in strictly following the International Labour Organisation’s definitions for employment and tend to use their own definitions.

35. Participants encouraged the IAEG to improve the information on the metadata.

Other issues36. There was some discussion on integrated and panel surveys and it became apparent the need to clarify further and build capacity on how to implement these surveys and on related quality issues.

Recommendations37. Based on the workshop discussions, participants identified and proposed the following set of recommendations to improve the production of MDG indicators at the country level and to reconcile national and global MDG monitoring:

1) NSOs should work with all stakeholders in the National Statistical System (NSS) to:

a. Improve data management, archiving, analysis, dissemination and usage, including computerizing administrative records;

b. Improve coordination of statistical production, networking and information sharing amongst stakeholders;

c. Promote statistical advocacy at all levels;

13


d. Involve experts from all relevant sectors in the planning and designing of data collection and data analysis.

2) NSOs should provide technical assistance and guidelines to line ministries and other data producers on statistical production.

3) NSOs should work with relevant national authorities to prioritize MDG indicators according to their relevance to national needs and circumstances.

4) Countries should establish a national coordination committee for MDG monitoring.

5) NSOs should take the initiative to coordinate the NSS, when the Statistics Act does not already specifically assign this authority to any other institution or body (eg. Statistical Board or Committee or Council).

6) NSOs should have the authority to verify and validate the data used for MDG indicators produced by other national agencies.

7) UNECA, UNSD and other development partners should promote the development and setting up of vital and civil registration systems, while countries should put in place the legal frameworks and motivate the population to register vital events—in particular births and deaths.

8) NSOs and development partners should promote integrated statistical activities--in particular integrated household survey programmes—as an important source of MDG indicators.

9) Development partners should use, and countries should encourage, basket funding, in order for countries to be able to prioritize their activities according to their national planning needs and improve the effectiveness of donors’ assistance.

10) International organizations, when financing any statistical activities in the country, should always involve the NSO and other relevant national offices and agencies.

11) International organizations should ensure that while conducting their internationally sponsored survey programmes they also focus on enhancing statistical capacity of countries. All national surveys should be owned by countries regardless of who sponsors them.

12) Countries should take the lead in conducting all surveys, while making an effort in adopting international MDG monitoring standards and methods to allow compatibility and comparability (eg. MICS and DHS).

13) Countries should be involved in the development of concepts, definitions and methodologies and in the setting up of any guidelines for MDG monitoring.

14) International organizations should make all their methodological publications available to NSOs and other producers of statistics in countries and ensure that adequate national capacity be built in countries to make definitions and classifications at national and international levels compatible.

14


15) International organizations should consult country statisticians and other experts before the release of any imputations and estimates (eg. Measurement of child mortality, access to water and sanitation, etc.)

16) NSOs should make an effort to harmonize definitions and classifications used in different data collection instruments over time and between national and sub-national levels.

17) NSOs should ensure that any methodological development—such as the Principles and Recommendations for the Population and Housing Census Programme—be reconciled with the production of MDG indicators. (eg. Refining the definitions used).

18) Country participants took note of the process for the selection of the new indicators and requested that in the future countries be fully involved.

19) Countries should take note of the new MDG indicators and make an effort to ensure the availability of the necessary data.

20) UNSD in collaboration with development partners should provide the necessary assistance to countries to produce the new indicators.

21) UNECA, in collaboration with UNSD, should establish a profile of censuses, surveys and other key statistical activities of countries in the region and share the information with other international organizations and stakeholders.

22) The UN Population Division should initiate consultations with countries on the updating of the population estimates.

23) The World Bank should be fully transparent on the data and methodology used for the calculation of the 1$-a-day poverty measure, and ensure that the 1$-a-day poverty concept and interpretation are well adhered to.

24) Regional and sub-regional organizations should establish repository systems for MDG indicators—including data and metadata—to promote harmonization of standards and definitions and international comparability (eg. SADC, ECOWAS, etc.)

38. These recommendations will be presented to the IAEG on MDG Indicators at its next meeting, and to the Steering Committee of the Development Account Project on “Strengthening statistical capacity-building in support of the Millennium Development Goals in the Southern African Development Community (SADC) region”.

15

Annex 1. List of participants

Countries

BotswanaMs. Anna MajelantleDirector of Statistics DepartmentCentral Statistics OfficePrivate Bag 0024Gaborone, Botswana

E-mail: [email protected] : (267) 367 1302Fax : (267) 395 2201

CameroonMr. Ambroise AbandaChef de ServiceInstitut national de la statistique (INS)BP 134 YaoundeCameroon


GhanaMr. Francis YankeyPrincipal StatisticianGhana Statistical ServicePO Box GP 1098Accra, Ghana

E-mail: [email protected] : (233)24 325 3192Fax :

KenyaMs. Mary WanyonyiSenior Economist/StatisticianKenya National Bureau of StatisticsHerufi House, Lt. Tumbo AvenuePO Box 30266Nairobi, Kenya 00100

E-mail: [email protected] : (254 20) 340929, 317612Fax : (254 20) 315977

LesothoMs. Liengoane LefosaDirectorBureau of StatisticsPO. Box 455Maseru 100Lesotho

E-mail: [email protected] : (266) 2232-3127, (266) 5888-4015Fax : (266) 22310177


LiberiaMr. Kormay AdamsDirector of Economic Statisticsand MDG Focal PersonLISGISStatistics HousePO Box 629Tubman Loulevard, SinkorMonrovia, Liberia

E-mail: [email protected] : (231-6) 533 149Fax :

MalawiMr. Shelton KanyandaChief StatisticianNational Statistical OfficePO Box 333Zomba, Malawi

E-mail: [email protected] : (265 1) 524 377Fax : (265 1) 525 130

MauritiusMs. Meera Bye GanooSenior StatisticianCentral Statistics OfficeLIC CenterJohn Kennedy StreetPort Louise, Mauritius

E-mail: [email protected],[email protected] : (230) 212 2314Fax : (230) 211 4150

MozambiqueMr. Cassiano ChipembeHead of Department of Demographic AnalysisMaputo, Mozambique


NamibiaMr. Sylvester Kabuku MbanguDeputy Government StatisticianNational Planning CommissionCBS NamibiaLuther Street, Government Office ParkBlock D, Room 029Private Bag 13356Windhoek, Namibia

E-mail: [email protected] : (264 61) 283 4000, (264 81) 129 5448 Fax : (264 61) 239 376

17


Sierra LeoneMr. Ahmed Saybom KanuAdministrative ManagerStatistics Sierra LeoneA.J. Momoh StreetFreetown, Sierra Leone

E-mail: [email protected],[email protected] : (232) 22 223 404Fax : (232) 22 223 897

SwazilandMr. Henry Sibusiso Sandla GinindzaStatisticianCentral Statistical OfficePO Box 456Mbabane, Swaziland H100


TanzaniaMr. Gabriel MadembweStatics Training OfficerNational Bureau of StatisticsP.O.Box 796Dar-es-Salaam, Tanzania

E-mail: [email protected] : (255) 22 212 722/3/4Fax : (255) 22 2123 0852

TanzaniaMr. Ephraim KwesigaboDirector, Population Census and Social StatisticsNational Bureau of StatisticsPO Box 796Dar-es-Salaam, Tanzania

E-mail: [email protected] : (255 22) 212 2722 / 314Fax : (255 22) 213 0852

UgandaMr. Ben Paul MungyerezaDirectorStatistical Coordination ServicesUganda Bureau of StatisticsPlot 9, Colville StreetPO Box 7186KampalaUganda


18


UgandaMr. Mwayafu Mujasi DavidProgram OfficerUganda Coalition for Sustainable DevelopmentP.O.Box 27551Kampala, Uganda

E-mail: [email protected] : (256) 414 752 521 230Fax :

UgandaMr.Moses GavaSenior Quality Assurance OfficerP.O.Box 20026Kampala, Uganda

E-mail: [email protected] : (256) 414 772 720 374Fax :

ZambiaMrs. Margaret Tembo MwanamwengeSenior DemographerCentral Statistical OfficePO Box 31908Lusaka, Zambia

E-mail: [email protected], [email protected] : (260) 977 842098Fax : (260) 211 253468

ZimbabweMs. Taizivei MungateAssistant DirectorHarare, Zimbabwe

E-mail: [email protected], [email protected].: (263 04) 706681 / 8(263 04) 703971 / 7Fax : (263 04) 708854(263 04) 728529

International organizations

United Nations Statistics Division Ms. Francesca PerucciChiefStatistical Planning and Development SectionUnited Nations Statistics Division, DESA2 UN Plaza, DC2-1652New York, NY 10017


United Nations Statistics Division Ms. Maria MartinhoUnited Nations Statistics Division, DESA2 UN Plaza, DC2-1656New York, NY 10017


19


United Nations Statistics Division Mr. Yacob ZewoldiChiefStatistical and Geographic Conferences UnitUnited Nations Statistics Division, DESA2 UN Plaza, DC2-1644New York, NY 10017


UN Economic Commission for AfricaMr. Ben KiregyeraDirectorAfrican Centre for StatisticsUN Economic Commission for AfricaPO Box 3001Addis Ababa, Ethiopia

E-mail: [email protected] : (251) 11 5511056, (251) 11 544 3210Fax : (251) 11 551 0389

UN Economic Commission for AfricaMr. Dmitri SangaSenior StatisticianAfrican Centre for StatisticsEconomic Commission for Africa Addis AbabaEthiopia

E-mail: [email protected] : (251) 11 544 3050Fax : (251) 11 551 0389

UNESCOMr. Said Ould VoffalEducation Indicators and Data AnalysisUNESCO Institute for StatisticsMontreal, (QC) Canada H3C 3J7


World BankMr. Johan MistiaenEconomist/StatisticianNairobi, Kenya

E-mail: [email protected] : (264) 20 322 6373Fax : (264) 20 322 6382

UNICEFMr. Edilberto LoaizaSenior Programme OfficerStrategic Information SectionDivision of Policy and PlanningUnited Nations Children's Fund (UNICEF)3 UN PlazaNew York, NY 10017USA


20


UNICEF - JMPMr. Rolf LuyendijkWHO/UNICEF Joint Monitoring Programfor Water Supply and Sanitation (JMP)Strategic Information SectionDivision of Policy and PlanningUnited Nations Children's Fund (UNICEF)3 UN PlazaNew York, NY 10017USA

E-mail: [email protected],[email protected] : (212) 3775 6499 (Morocco)Fax :

South African Development Community (SADC)

Mr. Ackim JereSenior Policy & Programme Manager Statistics, SADC SecretariatP/Bag 0095Gaborone, Botswana

E-mail: [email protected], [email protected] : (267) 395 1863Cell : (267) 7248 4487Fax : (267) 392 4099, (267) 397 2848

Ms. Chishuvo GundaSADC Development Project Account CoordinatorStatistics, SADC SecretariatP/Bag 0095Gaborone, Botswana

E-mail: [email protected], [email protected] : (267) 742 00541Fax : (267) 395 2201

Others

DFIDMr. Richard HarrisDepartment of International DevelopmentUnited Kingdom

E-mail: [email protected] : (44) 0 20 7023 1301Fax : (44) 0 20 7023 0284

Statistics NorwayMr. I B ThomsenSenior Research FellowP.O.Box 8131-DEPOslo, Norway

E-mail: [email protected] : 047-2109 4275 Fax :

21

Annex 2. Session on sub-national monitoring and administrative data: Summary of the Working Groups

GROUP 1

Table1: MDG Monitoring at sub-national level and use of administrative data Indicator Monitoring at Sub-national Level Use of administrative

data Poverty Information

Some countries use their own national poverty lines from surveys. In most countries data for regions, provinces urban and rural are available generally every 5 years

There are no administrative data

Net enrollment ratio

Data is available at both national, regional and district level. Some countries like Ghana use data from surveys

Other countries use administrative sources.

Child mortality

In some countries, data is available at national and regional level. Data is from censuses and demographic health surveys. In Botswana and Mauritius due to the small population size, data is not available at district level.

Countries like Malawi and Mauritius administrative data is used.

Use of improved water source

Most countries use census data and surveys data for sub-national information.

There are no administrative data on this indicator.

Table 2: Gaps and Challenges in the Use of Sub-national and Administrative DataGaps Challenges

Sub-national data

Mortality data Surveys are not annual

Periodicity Financial and other resource constraints Lack of coordination Poor estimation for smaller areas

Administrative data

Poor quality of statistics

Limited scope Implementation

outside of statistical standards

Exaggeration by local managers Definition Capacity in regards to skilled personnel Poor timeliness of production of data Misaligned definitions Statistics not a priority at sector level Lack of coordination at sector level Poor coverage


Table 3: Measures taken and RecommendationsMeasures taken Recommendations

Sub-national data

Built capacity at sub-national and across sectors

Coordinate financial support

NSO to provide technical and financial assistance Scale (increase) up statistical development at all

levels Improve data management , archiving, analysis ,

dissemination and usage Improve coordination of statistical production,

networking and information sharing amongst stakeholders

Promote statistical advocacy at all levels Promote integrated surveys Prioritise MDG indicators according to national

needsAdministrative data

Provide guidelines for statistical production

Built capacity at sub-national and across sectors

Coordinate financial support

Promote in service statistical training at all levels

Computerise administrative records More collaboration with NSS e.g. include more

experts in data collection design Give more incentives for people to register births

and deaths NSO to provide technical and financial assistance Scale (increase) up statistical development at all

levels Provide guidelines/ manual for statistical

production Improve data management , archiving and

analysis, dissemination and usage Promote civil registration at national level Improve coordination of statistical production,

networking and information sharing amongst stakeholders

23


GROUP 2

A. Availability of Sub-national and Administrative data for MDG Monitoring

For which indicators are sub-national data more available?

• Poverty ind: in most countries, data for regions and province are available, as well as urban and rural. Data available generally every 5 years. Some countries use their own national poverty lines.

• Net enrolment ratio: Data mostly at regional and district levels from administrative sources, but a few countries (e.g. Ghana) have data from national surveys. Data are available on a yearly basis.

• Child mortality: mostly data for region/provinces (not districts). Data mainly from health surveys conducted every 4 to 5 years. Other countries, like Malawi, use also administrative sources, but use only survey data for reporting to international agencies. In Mauritius, on the other hand, only administrative data are used.

• Use of improved water source: most countries use census data where data are available by province and district. Some countries have sub-national data from surveys (DHS and Living conditions surveys) every 4-5 years.

Gaps and Challenges in the Use of Sub-national Data and Administrative Data

Gaps and challenges

Sub-national data No periodicity. Estimation in inter-census/surveys years is more difficult for

small areas than for national level. Lack of resources.

Administrative data Lack of skilled personnel in Ministries. Data takes long time to be compiled. Definitions are different than those used for the MDGs. Lack of coverage.

B. Measures Taken and Recommendations

Administrative Data

- Computerize the administrative records;- More collaboration within NSS, e.g. include experts in data collection design;- More incentives for people to register (e.g. births and deaths).

24

Annex 3. Session on discrepancies between national and international data: Summary of the Working Groups

GROUP 1

Discrepancies between National and International DataCHILD MORTALITY DATA

• Definition• Methodology• Most of data used not updated

Recommendations• Send an experts for verification where the survey was conducted by other Partners, Or• Develop a standard format to be followed by all Partners• Need for a workshop which should consist of all subject matter specialists (including a representative from

Training Institutions – Demographers) together with the International Agencies• The respective countries need to stick to agreed country figures which have been monitored over time. If

there is any imputation, the respective country should be contacted.• There is a need of change in method for the International Agencies, instead of extracting data to

enhancing capacity of the countries. All survey should be owned by the countries regardless of who sponsored it

• Consider using the ECA in case of resistance

Recommendations on How to Reconcile National and International Data

1. ENROLMENT RATE & LITERACY• Strengthen coordination at UN level so that they can be aware of what is happening in each country

– NSOs be encouraged to develop release Calendars for their statistical releases• To bring together countries to discuss population projections with their own data• Speed up the process by ECA/ACS to prepare a Database of all statistical activities taking place in the

countries• UNSD to talk to UN country offices to send the appropriate data the Un agencies

2. ENVIRONMENT INDICATORS• NSOs should know where to send the data and maintain the release calendar• Metadata to be developed for all users and this should be done in collaboration with the International

agencies• To work together with international Agencies and countries on definitions• Reconcile Population Census Principles and recommendations with the MDG indicators• Strengthen the coordination function in the NSOs

3. POVERTY INDICATORS• The data bank for the Bank is growing, the Bank need to have a system which will enable them to

participate in meetings like this held in the regions• Where the WB changes the figure for the respective country, there is a need of communicating with the

country for verification– NSOs are to establish a Focal Point in this respect

• Local Poverty line is to be published in addition to the indicator of $ 1 a day for comparison and to avoid the misconceptions.

• Create more awareness to the public on the meaning of the indicator of $ 1 a day to eliminate the existing misconceptions. This is to be a sustained activity within the country.


GROUP 2

All countries have observed some discrepancies in the indicators under discussion

Indicators for which the Discrepancies are More Striking

Indicator Reasons for discrepancy

1.1 $1/day • Missing components of calculating the indicator (eg, PPP, CPI)• Some data are new and not yet published• Country data not available at World Bank (Mauritius)

2.1 Enrolment ratio • This is explained by differences in the population data used• Population estimates are obtained from UNPD

2.3 Literacy rate • Unavailability of data, in which case UNESCO uses modeling• Modeling used is acceptable by countries

7.8 Proportion of population using improved drinking water

• Agency data points are estimated using regression• Latest figures from surveys and censuses are not published• Country data missing at UNICEF• Some countries use proportion of households

7.9 Proportion of population using improved sanitation facility

• Shared facilities not being considered as improved • Latest figures from surveys and censuses are not published

Reasons for Discrepancies

1.1 1.8 2.1 2.3 4.1 5.2 7.8 7.9

Different definitions X X XDifferent population estimates X Different methods (explain differences) Other (explain)

Recommendations on How to Reconcile National and International Data

Indicators 2.1 and 2.3- Encourage countries to discuss with UN Population Division the population estimates- UN Population Division should revise data every time country has new data (as opposed to every

two years)- Improve reliability of data on the age of children enrolled- Extend coverage of enrolment data to private schools- Collect data on enrolment through /parents’ associations to get information about children who

receive education at home or in schools which are not registered.- All new data should be sent to Unesco as soon as available

Indicators 1.1- The World Bank should put a system in place to gather data from countries and to compile

indicators- The World Bank should share with countries the data used as well as the methodologies

underlying the calculation of the indicator 1.1- Setting a focal point in each country for data and information exchange with the World Bank- All new data should be sent to the World Bank as soon as available

26


Indicators 4.1- Countries should inform Unicef of forthcoming surveys- Capacity building on the direct and indirect methods is needed- Countries should consult the Unicef database and report any missing or outdated data- To establish a resource person in each country to populate the Unicef database- All new data should be sent to Unicef as soon as available

Indicators 7.8 and 7.9- ECA or UNSD should establish a register of census and surveys in the region- All new data should be sent to Unicef as soon as available

GROUP 3

Discrepancies between National and International Data

World Bank: Poverty- Most recent data set are missing in the WB series. - Lack of transparency in methodology used in estimations and imputations.- Lack of internal coordination within international organization

Recommendations- The WB should make extra effort to collect the data and engage the NSOs on a regular basis. - It is very important that the WB works on its internal coordination before publishing results.- Transparency in methodology including estimations and imputation. - The WB will get back to the countries when data changes.

Actions to be taken on discrepancies between national and international data on poverty• Lesotho to make the missing data sets available to the WB by June 2008• WB to make available the poverty line to Lesotho by mid-May 2008• Malawi and Namibia will provide the latest figures on poverty to the WB by June 2008• Sierra Leone will check and provide consumption and income data if they were collected in the

2006 CWIQ survey

UNESCO: Enrolment and literacy2.1 Net enrolment ratio

• missing data sets from NSO and MoE Recommendations on 2.1 Net enrolment ratio• Need for better collaboration between NSOs, MoE and development partners• When international organization make projections and estimations they should consult with

national institutions; similarly national offices should be proactive on this exercise• When international finance any national statistical activity they should also involve the NSO and

other pertinent offices/agencies.2.3 Literacy rates

• Missing data sets (Lesotho, Malawi, Zambia)• Discrepancy in national figures between 2 time points (Zambia)Recommendations on 2.3 Literacy rates• make extra effort to get data from countries rather than relying on models

UNICEF: Water and SanitationImproved drinking water source- Missing datasets should be addressed- Where extrapolations are used the methodology should be shared with countries before publishing

results- definitions and classifications at national and international levels should be compatible.

27


- Harmonizing national instruments of data collection using different sources- Discrepancy between national and international data for one country

Sanitation- Discrepancy between national and international definitions regarding “improved/shared” toilets- Incompatible terms used to describe toilets at national level and international classifications

Recommendations on sanitation• Provide clear definitions of “improved” and “shared” toilets• Need to standardize terms used to describe toilets at national level and international

classifications in order to make them compatible• At national level other producers (line ministries, NGOs) and international organizations should

coordinate with NSO when conducting surveys such as the WHS• Harmonizing national instruments of data collection for different sources of data

Recommendations on <5 mortality• definitions and classifications at national and international levels should be compatible

• Harmonizing national instruments of data collection for different sources of data

28

Annex 4. Summary of gaps and discrepancies between national and international data

INDICATOR 1.1 – Proportion of population below poverty lineData gaps at the international level and differences between national and international data

B = common sources at Both national and international levelsC = extra Country sources (not available at the international level)I = sources used at International level but not included in the NSO data submissionD = Y: Difference between data points of country and international agencies >=5% (for at least one data point); N: otherwise.

Country B C I DBotswana HIES 1993-95 HIES, 2003 N Y

Cameroon Country uses international data

N N N

Ghana Questionnaire was not submittedKenya WMS 1997 N WMS 1992 NLesotho Questionnaire was not submittedLiberia No data at international level; country uses UNDP estimates*Malawi IHS 2005 N N Y

Mauritius N HBS 1996-97; HBS 2001-02

N No data available at the international level

Mozambique HBS 1996-97, HBS 2002-03

N N Y

Namibia Country did not submit any data HIES 1993 Country did not submit any data

Sierra Leone IHS 2002-03 Census 2004 HEEAS 1989/90 Country did not submit any data

Swaziland Questionnaire was not submittedTanzania HBS 2000-01 HBS 1991-2 HIES 1991 YUganda NHS 1992, NHS1997,

NHS 1999/00, NHS 2002/3, NHS 2005/6

N N Country did not submit any data

Zambia N N HBS 1991, HBS 1993, LCMS 1996, LCMS

1998, LCMS 2002/3, LCMS 2004,

Country did not submit any data

Zimbabwe No data at international level

Data from CSO (1995, 2001) and MPSLSW

(2003)

No data at international level

No data at international level

* CWIQ 2007 data will be released soon.


INDICATOR 2.1 – Net enrolment ratio in primary educationData gaps at the international level and differences between national and international data


Country B C I DBotswana Country provided data from Education Statistics report while UNESCO

uses data received from MoE – original data sources not providedY

Cameroon ECAM 1996, ECAM 2001

EESI 2005, MICS 3 2006

N N

Ghana Questionnaire was not submittedKenya Original data sources

used by UNESCO not provided

DHS 1993, WMS 1994, WMS 1997, DHS 1998,

Census 1999, LFS 2000, DHS 2003, IHBS

2005/6

Original data sources used by UNESCO not

provided

Y

Lesotho Questionnaire was not submittedLiberia MoE/Unicef survey

2002Original data sources not provided Y

Malawi Original data sources used by UNESCO not

provided

IHS 1998 and HIS 2005 Original data sources used by UNESCO not

provided

Y*

Mauritius Original data sources used by UNESCO not

provided

Annual survey in schools


provided

Y

Mozambique Both NSO and UNESCO provide data received from MoE - original data sources not provided

Y

Namibia No data provided by the countrySierra Leone Original data sources

used by UNESCO not provided

MICS 1 1995, MICS 2 2000, Census 2004,

MICS 3 2005


provided

No data submitted by country

Swaziland Questionnaire was not submittedTanzania Country provided data from Basic Education Statistics report while

UNESCO uses data received from MoE – original data sources not provided

Y

Uganda Original data sources used by UNESCO not

provided

NHS 1992, NHS1997, NHS 1999/00, NHS

2002/3, NHS 2005/6


provided

No data submitted by country

Zambia No data submitted by countryZimbabwe Both NSO and UNESCO provide data received from MoE - original data

sources not providedY

* Since the national duration of Primary Education in the country is longer than the period considered in the international definition, the country data were adjusted by UNESCO.

30


INDICATOR 2.3 – Literacy rate of 15-24 year-oldsData gaps at the international level and differences between national and international data


Country B C I DBotswana 2003 Literacy survey Literacy survey 1994 Census 1991 NCameroon N ECAM 1996, ECAM

2001, DHS 2004N No data available at

the international levelGhana Questionnaire was not submittedKenya MICS 2000 WMS 1997 N YLesotho Questionnaire was not submittedLiberia N DHS 2000, CWIQ 2007 N YMalawi N IHS 1998, IHS 2005 PHC 1998 Y

Mauritius PHC 1990, PHC 2000 N N NMozambique PHC 1997 HBS 2003 N NNamibia 1991 PHC? 2001 PHC?

(country source not indicated)

N N Y

Sierra Leone 2004 census MICS 1 1995, MICS 2 2000, MICS 3 2005,

CWIQ 2007

Country did not submit data

Swaziland Questionnaire was not submittedTanzania 2002 PHC N N NUganda N NHS 1992, NHS1997,

NHS 1999/00, NHS 2002/3, NHS 2005/6

1991 PHC, 2002 PHC Country did not submit data

Zambia 1990 PHC 2000 PHC, 2002 DES 1999 survey YZimbabwe N 2002 and 2004 data

(country source not indicated)

1992 PHC N

31


INDICATOR 4.1 – Under-five mortality rateData gaps at the international level and differences between national and international data


Country B C I DBotswana PHC 1991, MICS 2000 DHS 1996, PHC 2001 FHS 1996 YCameroon DHS 1991, DHS 1998,

MICS 2000, DHS 2004, MICS 2006 N Y

Ghana Questionnaire was not submittedKenya DHS 1993, DHS 1998,

MICS 2000, DHS 2003PHC 1999, IHBS

2005/6N Y

Lesotho Questionnaire was not submittedLiberia DHS 2007 DHS 1999/2000 N YMalawi N IHS 1998, IHS 2005 DHS 1992, DHS 2004 Country did not

submit dataMauritius Vital registration N N NMozambique DHS 1997, PHC 1997 DHS 2003 MICS 1995 YNamibia PHC 2001? (country

did not indicate source)

1991 data (country did not indicate source)

DHS 1992, DHS 2000 Y

Sierra Leone MICS 2000, MICS 2005 MICS 1995, PHC 2004 N Country did not submit data

Swaziland Questionnaire was not submittedTanzania DHS 1991/2, DHS

2004/5RCHS 1999 DHS 1996, DHS 1999,

PHC 2002Y

Uganda DHS 1995, DHS 2000/1

DHS 2006 N Country did not submit data

Zambia DHS 1992, DHS 1996, DHS 2002

PHC 1990, PHC 2000 N Y

Zimbabwe DHS 1994?, DHS 1999?, DHS 2005? (country did not

indicate data source)

N PHC 1992, ICDS 1997 Y

32


INDICATOR 7.8 – Proportion of population using an improved drinking water sourceData gaps at the international level and differences between national and international data


Country B C I DBotswana PHC 1991, MICS 2000 PHC 2001, AIS 2004 N YCameroon DHS 1998, MICS 2006 ECAM 1996, ECAM

2001DHS 1991, MICS 2000, DHS

2004N

Ghana Questionnaire was not submittedKenya MICS 2000 WMS 1994, IHBS

2005/6DHS 1993, DHS 1998, PHC

1999, DHS 2003, WHS 2003Y

Lesotho Questionnaire was not submittedLiberia DHS 1999/2000 Data from MPEA

1997/8, CWIQ 2007N Y

Malawi N PHC 1998, IHS 2005 DHS 1992, MICS 1995, DHS 1996, DHS 2000, CWIQ 2002, WHS 2003, DHS 2004, MICS

2006

Y

Mauritius N PHC 1990, PHC 2000 WHS 2003 NMozambique HBS 2003 LFS 2004/5 PHC 1997, DHS 1997, QUIBB

2001, DHS 2003Y

Namibia PHC 1991?, PHC 2001? (country did not indicate source)

N DHS 1992, DHS 2000, WHS 2003

N

Sierra Leone MICS 2000, MICS 2005/6

MICS 1995, PHC 2004, CWIQ 2007

N Country did not submit data

Swaziland Questionnaire was not submittedTanzania HBS 2000/1 HBS 1991/2 DHS 1992, LSMS 1993,

DHS1994, DHS 1996, MICS 1996, DHS 1999, PHC 2002,

AIS 2003, DHS 2005

N

Uganda NHS 1999/00, NHS 2002/3, NHS 2005/6

NHS 1992, NHS1997 1991 PHC, DHS 1995, DHS 2001, PHC 2002, DHS 2005


Zambia DHS 1992, DHS 1996, DHS 2002, LCMS 2003

PS 1991, PS 1993, LCMS 1996, PHC 2000,

LCMS 2004

PHC 1990, MICS 1999, WHS 2003

Y

Zimbabwe DHS 1994?, DHS 1999?, WHS 2003?, DHS 2005? (country did not indicate data

source)

1992 and 2002 data (country did not


ICDS 1997 N

33


INDICATOR 7.9 - Proportion of population using an improved sanitation facilityData gaps at the international level and differences between national and international data


Country B C I DBotswana PHC 1991 PHC 2001? Aids Impact

Survey 2004, 2006 data (source not indicated)

MICS 2000 Y

Cameroon MICS 2006 ECAM 1996, ECAM 2001 DHS 1991, DHS 1998, MICS 2000, DHS 2004

Y*

Ghana Questionnaire was not submittedKenya PHC 1999, MICS 2000 WMS 1994, IHBS

2005/6DHS 1993, DHS 1998, DHS 2003, WHS 2003

Y

Lesotho Questionnaire was not submittedLiberia DHS 2000 CFSNS 2006, CWIQ 2007 N YMalawi N PHC 1998, IHS 2005 DHS 1992, MICS 1995,

DHS 1996, DHS 2000, CWIQ 2002, WHS 2003, DHS 2004, MICS 2006

N

Mauritius N PHC 1990, PHC 2000 WHS 2003 NMozambique HBS 2003 LFS 2004/5 PHC 1997, DHS 1997,

QUIBB 2001, DHS 2003Y

Namibia PHC 1991?, PHC 2001? (country did not indicate source)

N DHS 1992, DHS 2000, WHS 2003

Y

Sierra Leone MICS 2000, MICS 2005/6

MICS 1995, PHC 2004, CWIQ 2007

N Country did not submit data

Swaziland Questionnaire was not submittedTanzania HBS 2000/1 HBS 1991/2 DHS 1992, LSMS 1993,

DHS1994, DHS 1996, MICS 1996, DHS 1999, PHC 2002, AIS 2003,

DHS 2005

Y

Uganda DHS 1995, DHS 2000/1, DHS 2005

N 1991 PHC, NHS 2001, PHC 2002, NHS 2003,

NHS 2006


Zambia DHS 1992, DHS 1996, DHS 2002, LCMS 2003

PS 1991, PS 1993, LCMS 1996, PHC 2000, LCMS

2004

PHC 1990, MICS 1999, WHS 2003

Y

Zimbabwe DHS 1994?, DHS 1999?, DHS 2005? (country did

not indicate data source)

1992 and 2002 data (country did not


ICDS 1997, WHS 2003 Y

* Both NSo and UNICEF use data from 2006 MICS. However, the data values are different: NSO approx. 30% while UNICEf approx. 50%. This may be due to the use of different definitions: NSO and UNICEF will check.

34

Abbreviations:AIS, Aids Impact SurveyCSO, Central Statistical OfficeCWIQ, Core Welfare Indicator QuestionnaireCFSNS, Comprehensive Food Security and Nutrition Survey DES, Demographic and Education SurveyDHS, Demographic and Health SurveyECAM, Enquête Camerounaise auprès des Ménages (Cameroon Household Survey)FHS, Family Health SurveyHBS, Household Budget SurveyHIES, Household Income and Expenditure SurveyHEEAS, Household Expenditure and Economic Activities SurveyICDS, Inter-censal Demographic SurveyIHBS, Integrated Household Budget SurveyIHS, Integrated Household SurveyLCMS, Living Conditions Monitoring SurveyLFS, Labour Force SurveyLSMS, Living Standards Measurement SurveyMICS, Multiple Indicator Cluster SurveyMoE, Ministry of EducationMPEA, Ministry of Planning and Economic AffairsMPSLSW, Ministry of Public Service Labour and Social WelfareNHS, National Household SurveyPHC, Population and Housing CensusPS, Priority SurveyQUIBB, Questionnaire sur les Indicateurs de Base du Bien (Questionnaire of Indicators of Well-being)RCHS, Reproductive and Child Health SurveyWHS, World Health SurveyWMS, Welfare Monitoring Survey

Report on: Workshop on Household Surveys and ...€¦ · Web viewTitle Report on: Workshop on...

Documents

Transcript of Report on: Workshop on Household Surveys and ...€¦ · Web viewTitle Report on: Workshop on...