Best practices report on millennium development goals monitoring and reporting at National and Subnational levels in African countires

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ECONOMIC COMMISSION FOR AFRICA African Centre for Statistics

Best Practices Report on Millennium

Development Goals Monitoring and Reporting

at National and Sub-National Levels in African Countries

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United Nations

Economic Commission for Africa

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Best Practices Report on Millennium Development C o a l s Monitoring a n d Reporting at National a n d Subnational Levels in African Countries

2012

African Centre for Statistics

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Economic Commission for Africa

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Best Practices Report on Millennium Development Goals Monitoring and Reporting at National and Subnational Levels in African Countries

2012

African Centre for Statistics

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Foreword

This report has been prepared by the African Centre for Statistics (ACS) with the main objective of compiling best practices on Millennium Development Goals (MDG) monitoring and reporting at national and subnational levels. These have been adopted by countries in Africa, with regard to six criteria that were adopted by all United Nations Regional Commissions. The Economic Commission for Africa (ECA) added one criterion that was considered particularly important for Africa. Other countries can review these best practices, and, it is hoped, adopt those most suited to their country situations.

African countries are fully embarked on the global strategy to improve monitoring and evaluation of the MDGs. However, despite efforts and progress made in various areas, it has to be recognized that more actions are needed to improve and facilitate the means of measuring and reporting on MDG indicators.

The report presents an overview of the monitoring and reporting on the MDGs in African countries in, including monitoring at the regional, national and subnational levels. The statistical challenges of monitoring and reporting on MDGs in African countries that have been discussed on different oc

casions include: a) Lack of data for compilation of some indicators; b) data discrepancies between national and international organizations; c) Statistical coordination within the national statistical system (NSS) and between NSSs and international organizations; d) methodological issues; statistical capacity; e) creation of databases; f) comparability of MDG statistics; and g) specific compilations , such as CO, data.

The terms "best practice" and "good practice" are defined in the report. A "best practice" is a method, process, activity, incentive, or reward that conventional wisdom regards as more effective for delivering a particular outcome than any other technique, method, process, etc. when applied to a particular condition or circumstance. A "good practice", on the other hand, refers to a specific method that produces results that are in harmony with the values of the proponents of that practice.

Some argue that what is called "best practice" should simply be called "good practice". This docu

ment refers to all selected country practices as "good practices". However, the term "Best Practices"

is maintained in the title of the report.

This first Best Practices Report on Millennium Development Coals Monitoring and Reporting at National and Subnational Levels in African Countries highlights seven criteria: a) Strong political commitment; b) effective coordination mechanisms of the NSS; c) Aligning national data-collection undertakings to meet the requirements of international concepts and standards; d) production of disaggregated data on MDG indicators; e) clarity of indicators and metadata; f) effective and efficient storage and dissemination of MDG data; and g) establishment of a complete CRVS system .The re

port also highlights the importance of each criterion and the challenges African countries are likely to encounter ; i meeting it.

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Finally, the good practices experienced by some African countries are described. Each good practice is presented using standardized subheadings: background, description of the good practice, benefits, challenges and way forward.

We hope that these remarkable inputs on good practices from country-level policies, institutions and programmes will inform and enrich the development of CRVS systems in all of Africa.

Dimitri Sanga Director

African Centre for Statistics

vi Best Practices Report on Millennium Development Goals Monitoring and Reporting at National and Subnational Levels in

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Acknowledgements

This Best Practices Report on Millennium Development Coals Monitoring and Reporting at Na

tional and Subnational Levels in African Countries was prepared under the overall supervision of Mr. Dimitri Sanga, Director of the African Centre for Statistics (ACS). Mr. Raj Gautam Mitra, Chief of the Demographic and Social Section at the ACS, provided direct supervision and guidance in the preparation of the report.

Ms. Fatouma Sissoko coordinated the preparation of the report, with the support of Mr. Issoufou Seidou. The report benefited from the remarkable contributions of Mr. Oumar Sarr, Statistician at the ACS.

UNECA greatly appreciates the valuable support of the United Nations Development Account (UNDA) project, whose funding contributed to implementation of the study, and of the United King

dom Department for International Development (DFID) for financial support of the organization of the Expert Group Meeting (EGM) in Yaounde, Cameroon.

Special thanks to Mr. Enock F. Ching'anda, consultant, who helped to put this report together. Spe

cial thanks also to the Institut National de la Statistique du Cameroun, which hosted the EGM organ

ized in Yaounde from 21 to 24 November 2011, during which countries presented invaluable inputs and information, summarizing and sharing their good practices for inclusion and thereby greatly enriching the meeting report.

Appreciation is also extended to the staff of the UNECA Publication and Conference Management Section (PCMS) for its efficient handling of the editing, text processing and printing of the report.

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List of Acronyms and Abbreviations

ACS AfDB AFRISTAT AUC CEDAW CO, COHR COPI CRVS CSO DCRP Devlnfo

DHA DHS DOH DSD EA ECLAC EGM Gamlnfo GER

HIV/AIDS IAEG ICT ILO JMP KNBS EGA M&E MASEDA MDA MDG MICS MoFEA NGO NHIS NHSCP

African Centre for Statistics African Development Bank

Economic and Statistical Observatory for sub-Saharan Africa African Union Commission

Convention on the Elimination of Discrimination against Women Carbon Dioxide

Congo Observatory of Human Rights

Congo Observatory of Poverty and Inequality Civil Registration and Vital Statistics System Central Statistics Office

"Strategie de Croissance et de Reduction de la Pauvrete"/ Strategy for Growth and Poverty Reduction

A database system for monitoring human development. Devlnfo was endorsed by the United Nations to monitor progress towards achieving the MDGs and other national priorities. It is a powerful tool for organizing, storing and presenting data in a uniform way, allowing it to be easily and quickly shared across government departments, United Nations agencies and other development organizations.

Department of Home Affairs Demographic and Health Survey Department of Health

Data Structure Definition Enumeration area

Economic Commission for Latin America and Caribbean Expert Group Meeting

An adaptation of the Devlnfo database system by the Gambia Bureau of Statistics Gross Enrolment Ratio

Human Immunodeficiency Virus / Acquired Immunodeficiency Syndrome Inter-Agency Expert Group

Information and Communication Technology International Labour Organization

Joint Monitoring Programme

Kenya National Bureau of Statistics Local Government Area

Monitoring and Evaluation

Malawi Socio-economic Database Ministries, Department and Agencies Millennium Development Goal

Multiple Indicator Cluster Survey

Ministry of Finance and Economic Affairs Non-governmental Organization

National Health Information System

National Household Capability Programme

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Nigerinfo An adaptation of the Devlnfo database system by Niger National Institute of Sta

tistics.

NSDS National Strategy for the Development of Statistics NSO National Statistical Office

NSS National Statistical System

OECD Organisation for Economic Co-operation and Development PARIS21 Partnership in Statistics for Development in the 2151 Century RES Post-enumeration survey

PHC Population and Housing Census

PNSD Plan for National Statistical Development PRSP Poverty Reduction Strategy Paper

RDP Reconstruction and Development Programme RRSF Reference Regional Strategic Framework

SAGA Semi-autonomous Government Agency SDMX Statistical Data and Metadata Exchange

SIRDC Science, Industrial Research and Development Centre/Zimbabwe SMS Short Message Service

SSC Sector Statistics Committee

SSPS Sector Strategic Plan for Statistics StatCom-Africa Statistical Commission for Africa Tinkhundla Political demarcations (Swaziland) UBOS Uganda Bureau of Statistics

UNDA United Nations Development Account UNDG United Nations Development Group UNDP United Nations Development Programme

UNECA United Nations Economic Commission for Africa UNFPA United Nations Population Fund

UNICEF United Nations Children's Fund UNSD United Nations Statistics Division

VTVSN Villages and Townships Vital Registration Network WEF Women Enterprise Fund

WHO World Health Organization YEF Youth Enterprise Fund

ZIMDAT Zimbabwe Statistics Database

ZIMSTAT Zimbabwe National Statistics Agency

ZUNDAF Zimbabwe United Nations Development Assistance Network

Best Practices Report on Millennium Development Goals Monitoring and Reporting at National and Subnational Levels in

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Executive Summary

1. The main objective of this report is to compile best practices on millennium development goal monitoring and reporting at national and subnational levels adopted by countries in the African region, in relation to the six criteria proposed by the Economic Commission for Latin America and Caribbean (ECLAC) and adopted by all United Nations Regional Commissions. One criterion was added by UNECA because of its importance in the African region.

2. The seven criteria are: strong political commitment; effective coordination mechanisms of the NSS; aligning national data collection undertakings to meet the requirements of international concepts and standards; production of disaggregated data on MDG indicators; clarity of indicators and metadata; effective and efficient storage and dissemination of MDG data; and establishment of a complete CRVS system.

3. The need to monitor progress towards achieving the MDGs has provided many developing countries with an opportunity to develop their statistical systems and produce better information in support of evidence-based policies for development. The purposes of MDG monitoring are: a) track

ing: assessing progress towards achieving MDG targets; b) advocacy and communication for MDGs;

c) enabling national, international and bilateral agencies to develop policy for poverty reduction;

d) ensuring the relevance and sufficient scope of terminology; and e) providing an opportunity to improve national and international statistical processes for this purpose.

4. It is difficult to call a practice "best" since what is best now may be improved later, in a short period of time. It is also true that all practices continue to be refined. Therefore, for the purpose of this document, it was decided to call successful practices in some the African countries "good" as this leaves room for improvement. It was, however, decided to maintain the term "Best Practices" in the original title of the document.

5. The report describes each of the seven criteria, explains its importance and outlines the chal

lenges that countries encounter in meeting each.

6. An overview is given of the monitoring and reporting of the MDGs in African countries and the various organs involved in monitoring at the global, regional, national and subnational levels.

The statistical challenges involved in the monitoring and reporting on MDGs in the selected African countries are outlined. These include: lack of data for compilation of some indicators; data discrep

ancies between national and international organizations; statistical coordination within the NSS and between NSSs and international organizations; methodological issues; statistical capacity; creation of databases; comparability of MDG statistics; and the need for specific compilations such as CO data. Nineteen "Good Practices" from 12 African countries are described under five headings: back

ground; description of the good practice; benefits; challenges; and way forward.

7. The following conclusions and recommendations are made: (a) the good practices presented in this report should be considered current models; (b) countries should learn from the countries that have presented good practices and organize study tours to countries of their choice, depending on

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resources, in order to learn more about how to improve their monitoring and reporting of MDGs;

and (c) when selecting countries to learn from, priority should be given to those that are similar to one's own in terms of economy and other socioeconomic factors.

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Chapter 1: Introduction

1.1 Objectives of the Report

8. This report was prepared by the African Centre for Statistics (ACS) of the United Nations Eco

nomic Commission for Africa (UNECA). The study was financed by the United Nations Development Account (UNDA) Project on MDGs, entitled "Strengthening statistical and inter-institutional ca

pacities for monitoring the Millennium Development Goals through interregional cooperation and knowledge-sharing". The UNDA project on MDGs, which is essentially an interregional programme, aims to increase the availability of up-to-date and comparable MDG data at the national, regional and global levels, by improving the capacities of countries to monitor their progress towards achiev

ing the MDGs. The project is implemented by the five United Nations Economic Commissions:

Economic and Social Commission for Asia and the Pacific (ESCAP), ECA, Economic Commission for Europe (ECE), Economic and Social Commission for Western Asia (ESCWA) and ECLAC, the latter being the lead agency.

9. The main objective of this report is therefore to compile good practices that countries in Africa have adopted, according to the six criteria proposed by ECLAC and adopted by all United Nations Regional Commissions. One criterion was added by UNECA because of its importance to Africa. Thus, there are seven criteria to be considered in the compilation of good practices.

10. The seven criteria are: strong political commitment; effective coordination mechanisms of the national statistical system (NSS); aligning national data collection undertakings to meet the re

quirements of international concepts and standards; production of disaggregated data on MDG indi

cators; clarity of indicators and metadata; effective and efficient storage and dissemination of MDG data; and establishment of a complete civil registration and vital statistics (CRVS) system.

1.2 The need to monitor progress

11. Monitoring progress towards the MDGs [1 ] is a continuous process whose primary purpose is to provide managers and the main stakeholders with regular feedback and early indications of progress or lack of progress in the achievement of intended results. Monitoring tracks the actual per

formance or situation against what was planned or expected according to predetermined standards.

Monitoring generally involves collecting and analysing data on programme processes and results and recommending corrective measures.

12. The need to monitor progress towards MDGs has been an opportunity for many developing countries to enhance their statistical systems and produce better information in support of evidence- based develop nent policies. Consequently, the need for data in order to monitor these development indicators has contributed to the establishment of an international reporting mechanism that has enhanced the collaboration between all stakeholders.

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13. In order to enable adequate monitoring of progress towards the MDGs, task forces, such as the MDG task force of the United Nations Development Group (UNDG) and the WHO/UNICEF Joint Monitoring Programme (JMP) for Water and Sanitation have been established.

14. The aims of monitoring progress towards the MDGs [2] include the following:

(a) Tracking: assessing progress towards achieving MDG targets. This ensures comparable and well-defined terminology, content validity; quality of the data (timeliness, baseline, questionnaire and sampling); data management and analysis;

(b) Advocacy and communication for MDGs;

(c) Enabling national, international and bilateral agencies to develop policy for poverty re

duction, to plan strategies and actions to carry out these policies, and target the resource allocation;

(d) Ensuring relevance and sufficient scope of terminology; and

(e) Providing an opportunity to improve national and international statistical processes for this purpose.

1.3 General challenges of the aims of MDG monitoring

15. The monitoring of aims a) to e) has their own challenges at both national and international levels as follows:

(a) Terminology definition can be a challenge. For example, access to safe drinking water can be defined in terms of access to safe water or access to an improved source of water. There is therefore a need to standardize the terminology used by countries and at the international level;

(b) Improved access to information through websites, thematic reports and annual reports is required at both national and international levels. There is also need to strengthen the ownership of MDGs and interaction between data users and data providers at national level;

(c&d) With regard to relevance and sufficient scope of data for policy and planning purpos

es, problems include minimizing data gaps, and the fact that the scope of monitoring data is not broad enough for planning and coherence in terms of providing gender- disaggregated data, for example.

(e) With regard to the opportunity to improve national and international statistical pro

cesses for monitoring, there are problems of national statistical capacity in terms of standardizing definitions in order to collect appropriate and reliable data and analyse it on a regular basis.

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Chapter 2: Best Versus Good Practice Criteria, Importance and Challenges

2.1 Introduction

16. A "best practice" is defined as a method, process, activity, incentive, or reward that conven

tional wisdom regards as more effective at delivering a particular outcome than any other technique, method, process, etc. when applied to a particular condition or circumstance. It has been argued that the term "good practice" should be used instead of "best practice". A "good practice" is defined as a specific method that produces results that are in harmony with the values of the proponents of that practice. Section 2.9 of this chapter discusses further the decision to adopt the term "good practice".

1 7. UNECA has defined seven criteria as best practices. Each criterion will be described, as well as its importance and the challenges countries encounter in meeting the criterion.

2.2 Criterion 1: Strong political commitment

Description

18. It is important for a country to adopt strong statistical legislation that provides an adequate le

gal and institutional framework to empower the National Statistical Office (NSO) in order to ensure effective coordination, standardization and harmonization of the activities of the NSS, including the responsibility of monitoring MDGs. This political commitment should include a clear vision, mis

sion and strategies, effective funding and staffing for the NSS, and its insulation from any political influence.

Importance of the criterion

19. The importance of the criterion is as follows:

» It is a fundamental prerequisite for an effective national statistical system.

» It underpins the institutional arrangements for the collection, processing and dissemina

tion of official statistics, including data for compilation of MDG indicators.

» Strong statistical law is required to produce good quality data and to coordinate the NSS efficiently.

» It takes into account the fundamental principles of official statistics.

» Ensures professional independence (autonomy) of the NSO and other agencies within the NSS in order to enhance the integrity, impartiality, credibility and confidentiality of official statistics.

» Provides a legal basis for collection and compilation of MDG and other indicators.

» Ensures that funding for statistical activities is provided by the State.

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» It is central to the protection of confidentiality and assurance of impartiality and objec

tivity of official statistics.

Challenges countries encounter in meeting the criterion 20. Countries usually encounter the following challenges:

» In some countries, professional independence (autonomy) is hard to achieve due to gov

ernment bureaucracy and a belief that the NSS should continue to be under the control of the Government of the day.

» Outdated statistical legislation because the national strategy for the development of sta

tistics (NSDS) has not been reviewed or simply does not exist.

» Statistical legislation does not define mechanisms for coordination among producers of statistics or between producers and users of statistics.

» Effective leadership of the NSS is required to ensure that strong statistical legislation is put in place.

» Lack of technical experts may compromise efforts to put strong statistical legislation in place.

2.3 Criterion 2: Effective coordination mechanism of the national statistical system

Description

21. A country should have a strong and effective national coordinating body under the leadership of the NSO to carry out statistical work in the country, including coordination of the collection, com

pilation and dissemination of timely and reliable data on MDG indicators. The NSO in the country should play a leading role in the collection, compilation, dissemination and development of data, as well as in monitoring the MDG process. It should also coordinate the data-validation process and data exchange with international institutions.

Importance of the criterion

22. The importance of this criterion is as follows:

» Harmonization of programmes on MDGs is ensured

» Collaboration of organizations or units responsible for a particular MDG and associated strategies is promoted

» Standardization of work processes (concepts, definitions, etc.) on MDG data is promoted

» Development of human and financial infrastructure is promoted

» Database development on MDGs is coordinated and avoids duplication

» Ensures coordinated dissemination of MDGs to ensure timely data supply

» Enables the formation of committees and task forces on MDG data and indicators

» Duplication of MDG activities is minimized.

Challenges that countries encounter in meeting the criterion 23. Countries often encounter the following challenges:

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» Lack of updated statistical legislation to enable formation of committees and task forces on the MDGs

» Inadequate technical capacity at the NSS for MDG database development

» Lack of a NSDS with well-articulated goals and strategies

» Ineffective leadership at the NSO for coordinating work on MDGs throughout the NSS.

2.4 Criterion 3: Aligning national data collection undertakings to meet the requirements of international standards and concepts

Description

24. Countries conduct censuses and various household surveys to collect data on social and economic indicators, including MDG indicators. It is important to ensure that the tools used in this regard are designed to collect data in accordance with international standards and concepts, in ad

dition to meeting the primary requirement of the country itself. If a country collects data that may diverge from official MDG data, complementary MDG indicators and data should be clearly defined and reported.

25. The importance of this criterion is as follows:

» Comparability of data within the country, between countries and internationally

» Enables the use of the same frameworks and classifications on statistics

» Makes it easy to request and receive international assistance.

Challenges that countries encounter in meeting the criterion

» Meeting international as well as national requirements

» International estimates may contradict national estimates.

2.5 Criterion 4: Production of disaggregated data on MDG indicators

Description

26. MDGs address the overall social and economic development of a country. Countries have undertaken to work towards achievement of the MDGs through a set of agreed indicators with clear targets set to be achieved by 2015. However, these targets are only to be achieved at the national level. In order for a country to achieve overall development, it is imperative that data are available at disaggregated levels in order to inform policy and programme implementation at the local level and also to ensure equitable and inclusive development.

27. Disaggregation of data is about individuals or entities. Data may be disaggregated by gender, socioeconomic status, race and ethnicity, urban versus rural, etc.;

» It is important to unbundle national progress into progress for women and men, for rural and urban residents, for young and older people, for the poor and the non-poor, and for regions, states and districts.

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» Disaggregated data provide a clearer picture of social and economic disparities.

» When carefully analysed and mapped, disaggregated data are key to identifying margin

alized and underserved population groups and less-developed regions.

» Pro-poor reforms start with disaggregation, for that which is measured influences what needs to be done.

» Disaggregation of household survey and census data by gender, region, ethnicity, age, and urban/rural residents has increased as a result of raised awareness of its importance and substantial efforts made to strengthen national statistical capacity.

Challenges that countries encounter in meeting the criterion

» Definitions of some disaggregation criteria may not be standardized. For example defini

tion of slum areas may not be very clearly documented and it may therefore be difficult to collect data for those areas. The same may be true for poor and non-poor populations:

there may be no clear poverty line.

» Resources for collecting disaggregated data are another challenge. The time required to collect such data may be quite substantial and require a sizeable financial budget.

» Statistical capacity for designing data collection systems and analysing data is generally weak or is entirely lacking in many countries, especially regarding disaggregated data.

2.6 Criterion 5: Clarity of indicators and metadata

Description

28. The MDG monitoring process should be supported by clearly defined core and complemen

tary MDG targets/indicators and supporting harmonized metadata agreed by all national stakehold

ers. This will help to explain discrepancies between national and international data sets and even differences of data on the same indicators within a country. This can be achieved by publishing metadata, accompanied by sources, definitions, methodologies, etc. in order to allow users to use and analyse data correctly. Metadata are data that describe the meaning, accuracy, availability and other important features of data. Such data are structured information that describe, explain, locate or otherwise make it easier to retrieve, use or manage an information resource 112]. It includes, for instance, the source of data and precise technical definitions of the variable. Metadata can be or

ganized into several levels, ranging from simple listing of basic information about available data to detailed documentation about individual data sets.

29. The importance of metadata is as follows:

» Metadata serve many important purposes, including data browsing, data transfer and data dissemination.

» At a fundamental level, metadata may support the creation of an inventory of the data holdings of a State or local government agency.

» Metadata are also important in the creation of a spatial data clearing house, where po

tential users can search for the data they need for their intended application.

» At a more detailed level, metadata may be regarded as insurance. Metadata ensure that potential data users can make an informed decision about whether data are appropriate for the intended use. Metadata also ensure that an organization's data holdings are well

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documented and that the organization is not in danger of losing all knowledge of its data when, for example, key employees retire or leave.

Challenges countries encounter in meeting the criterion

» The lack of common standards for exchanging and sharing data and metadata at the country level. The objective is to establish a set of commonly recognized standards mak

ing it possible, not only to have easy and timely access to statistical data, but also access to metadata that make the data more meaningful and usable.

» Production of quality data for computation of the indicators.

» Capacity to produce metadata for all data series.

» Resources for producing metadata.

2.7 Criterion 6: Effective and efficient storage and dissemination of MDG data

Description

30. Creating an efficient mechanism for the reporting, storage and dissemination of MDG indica

tors and metadata. It is important for NSS/MDG committees to regulate the format, content, regular

ity and frequency of reporting and to define and agree on the method of MDG indicator reporting, validation, storage and dissemination. It is also important for the NSO to establish an easily acces

sible and usable, Internet/web dissemination platform for an up-to-date national MDG database.

» Efficient and effective storage of MDG data will guard data against hardware or system failure, human error and software corruption.

» Efficient and effective dissemination of MDG data will ensure availability of data to all sectors of the economy, including planners, decision-makers and research workers.

» Ensures adequate monitoring of MDG indicators.

» Human and financial resources

» Financial resources for acquiring sufficient storage for MDG data

» Lack of committees, such as producer-producer or producer-user, to facilitate MDG data dissemination.

2.8 Criterion 7: Establishment of a complete civil registration and vital statistics system

Description

31. CRVS is not only a regular and permanent source of some key MDG indicators, it also pro

vides much needed population data on a constant basis that can be used as denominators for the estimation of MDG indicators. Except in two or three countries, the CRVS system is functioning at sub-optimal level. It is important for countries to develop and implement a clear plan for improve

ment of their CRVS system under the Africa Programme on Accelerated Improvement of Civil Regis

tration and Vital Statistics.

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» The CRVS ensures reliable vital statistics.

» The CRVS ensures that reliable inputs for calculating the indicators are available for many MDG indicators (approximately 42 out of 60).

» In many African countries, CRVS systems are of value for planning and formulation of public policy, promoting good governance, promoting and safeguarding human rights, including the rights of the child, etc.

» CRVS ensures the production of reliable inter-census population estimates, including reductions in data gaps and discrepancies.

» A civil registration system remains the single best source of information on vital events for administrative, demographic and epidemiological purposes.

» The majority of African countries have not made serious and focused efforts to develop CRVS systems following the conventional technical, operational and logistical require

ments and procedures.

» Demography courses taught at training institutions in Africa and elsewhere do not have modules on CRVS.

» Lack of political support for reforming and improving CRVS from African policymakers.

Political support for CRVS was declared only recently, in August 2010 at the first Minis

terial Conference on Civil Registration, Addis Ababa, Ethiopia. [13].

» Lack of funding for establishing and maintaining CRVS systems.

» Lack of a legal basis for CVRS systems.

» Infrastructure of the statistical organization to implement the CRVS system.

» Ensuring the registration function of the CRVS system.

» Implementing the statistical functions (collection, compilation, evaluation, tabulation and publication) of vital statistics.

» Lack of adequate staff and limited training for implementation of the CRVS system.

» Active cooperation and participation of the public.

2.9 Adopting the term "good practice"

32. As discussed in the introduction to this chapter, it is difficult to call a practice "best" since what is best now can always be improved later. Therefore, for the purpose of this document, it was decided to modify the terminology by calling the "best practices" of the selected African countries

"good", as this leaves room for improvement. It is also true that all practices continue to be re

fined. It was, however, decided to maintain the words "Best Practices" in the title of the document, confining the change to the text.

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Chapter 3: Overview of Millennium Development Goal Monitoring and Reporting in African

Countries

33. This overview will review the various organs involved in monitoring at the national, regional and international levels, the challenges, such as data discrepancies, data gaps, potential remedial measures and opportunities, and the way forward in monitoring MDG indicators.

34. Tracking the progress or lack of it towards achieving the MDGs is in general data-intensive.

In Africa, the establishment of the MDGs in 2000 increased the demand for quality data in order to monitor, evaluate, track progress, and report. This put additional pressure on the already fragile sta

tistical systems of Africa. To put it more positively, the MDGs gave African countries an opportunity to develop their capacity to deliver the data required.

35. This chapter will discuss the monitoring and reporting of the MDGs in African countries, starting at the country level and moving to the regional level. However, before going into detail about monitoring at the national and regional levels, it is important to say something about monitor

ing at the global level.

3.1 Monitoring MDGs at the global level

36. Monitoring MDGs at the global level is a huge task involving committees, task forces, meet

ings and conferences, etc. In order to ensure appropriate monitoring and reporting, the Interagency and Expert Group (IAEG) on MDG indicators was established at the global level by the United Na

tions Secretariat and is coordinated by UNSD.

37. Membership in the IAEG on MDG indicators includes various departments of the United Nations Secretariat, a number of United Nations agencies, various government agencies, national statisticians, experts and other organizations concerned with the development of MDG data, includ

ing development partners, and regional and international organizations.

38. The IAEG on MDG indicators is responsible for:

» Compiling data and undertaking data analysis for the purpose of monitoring progress towards the MDGs at the global and regional levels.

» Reporting on the status of annual progress through reports, progress charts and data

bases.

» Reviewing and preparing guidelines on methodologies and technical issues regarding the indicators.

» Helping define priorities and strategies to support countries in data collection and analy

sis and reporting on MDGs.

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39. Data on MDGs compiled through the global effort are organized in the UNSD database.

Under the guidance of the IAEG, one or more United Nations and other international agencies have been designated to provide the data and develop the collection and processing methods for each indicator. Initially and until 2007, there were 48 indicators. Thereafter, the indicators were increased to 60, following the introduction of new ones and the deletion and modification of others. The UNSD has published a Handbook on Monitoring the Millennium Development Goals: Definitions, Rationales, Concepts and Sources |14].

3.2 Statistical challenges in monitoring and reporting on MDGs in African countries

40. With regard to the development and availability of MDG indicators, there are a number of challenges that hamper the capacity of African countries to report on progress towards achievement of the MDGs. Some of these challenges are outlined below:

Lack of data for compilation of some indicators

41. Some African countries lack baseline data for the development of some indicators, e.g. pov

erty indicators. This may be because their administrative data are not developed in that area or be

cause surveys have not been undertaken in it, making it impossible to develop the MDG indicator.

Examples of this are sanitation and measuring slum populations.

Data discrepancies between national and international organizations

42. The increased demand for data to measure and monitor indicators requires evaluation of the different sources, organizations and methods for producing MDG-related data. Most data are col

lected through NSSs, some of which are not coordinated. National data enter the international statis

tical system in a process through which specialized agencies review and further standardize national data in order to produce certain Indicators. This approach has sometimes aroused controversy as countries complain that they were not consulted when certain computations were made, while, in some cases international estimates contradicted national estimates (United Nations Statistical Com

mission, 2005) [3]. This is a public relations challenge that can be resolved by close consultation between countries and international agencies. Data exchange between national and international organizations could eliminate some of the problems.

Statistical coordination within the NSS and between NSSs and international organizations

43. Coordination among statistical agencies within countries is essential in order to achieve consistency and efficiency in the statistical system (Lievesley, 2001) [41. However, many African countries lack mechanisms for coordination among producers of MDG data. For example, there is lack of coordination for the harmonization of definitions, concepts and classifications among dif

ferent sources. Inter-institutional collaboration, both at the national and international levels, can go some way towards enhancing coordination. In this way, duplication of data and dissemination of contradictory and non-comparable data can be minimized and the use of scarce resources maxi

mized (Economic Commission for Latin America and the Caribbean (ECLAC)) [5]. It has often been

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argued that "competing, inconsistent results on the same issue introduce scepticism and doubts in users' minds and the quality of such data is viewed with suspicion. This undermines confidence in statistics" (Everaers, 2002) [6].

44. In order for international organizations to receive quality MDG-related information, closer partnerships need to be developed between international agencies and national statistics systems.

Mechanisms should be put in place to harmonize requests for metadata. Coordination of MDG data could also facilitate the interlinking and standardization of data from different sources for those countries where this aspect is still a problem.

Methodological issues

45. Since population census data are used as denominators for most MDG indicators, the meth

odological challenges concerning the conduct of population censuses need to be examined, par

ticularly census mapping, the pilot census and post-enumeration surveys.

46. Census mapping: Some African countries have conducted censuses without completing the mapping exercise covering the whole country. In this case, some enumeration areas (EAs) were de

marcated while the enumerators were in the field. This meant producing rough sketches of EA maps with poorly defined boundaries and very rough estimates of population sizes. Such sloppy handling of the delineation of EAs resulted in under-coverage in some areas owing to missing boundaries and some duplication owing to the overlap of some EAs. In principle, EAs should be mutually exclu

sive and cover the whole country. However, under-coverage is the main problem associated with many African censuses. Where post-enumeration surveys have been conducted, under-coverage rates have been as high as 1 7 per cent. This would have had an impact on the reliability of the results used to estimate some MDG indicators. Under-coverage is one of the components of non-sampling errors associated with censuses. It is therefore important to control non-sampling errors at every stage of census activities, from planning to analysis of results. With regard to minimizing coverage error, it is incumbent upon African countries to carry out a comprehensive census-mapping exercise that produces well-defined EAs and covers the whole country or the part of the country designated for the conduct of the census. Census enumerators and supervisors should be well trained in the art of identifying and covering their assigned EAs.

47. Pilot census: Prior to conduct of the census, a pilot census should be carried out, preferably under similar census conditions, a year before the census. The pilot census is a rehearsal of the ac

tual census that allows the field conditions, logistics, draft census questionnaire, data processing, and so forth to be tested. The results are used to refine and finalize the census questionnaire; deter

mine workloads of field staff; review logistics; and determine the data-processing strategy. All these efforts are intended to enhance the quality of census results, including data used in computing MDG indicators, by minimizing non-sampling errors (errors not caused by sampling but human errors such as data-entry errors, biased questions in the questionnaire, biased processing, false information pro

vided by respondents, etc.).

48. Post-enumeration surveys: A census is traditionally a massive operation making errors inevi

table, whatever precautions are put in place; the difference among countries is the degree of error.

The primary objective of a census evaluation programme is to determine the sources and magnitude

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of coverage and content errors for some selected variables. For many developing countries, the post- enumeration survey (PES) has become a plausible independent evaluation programme. This is partly because other independent sources of data with relevant, comprehensive and reliable information are still rare (ECA, 1999) [7], for example, civil registration.

49. The PES is a complete re-enumeration of a representative sample of a census population matching each individual enumerated in the PES with information from the census enumeration (United Nations, 2008) [8]. Thus, the results of the comparison are mainly used to measure cover

age and content error in the context of the census. Coverage error refers to people missed in the census or those included erroneously. On the other hand, content errors are identified by evaluating the response quality of selected questions in a census. It is also a basis for evaluating the reliability of some characteristics reported in the census. Evaluation of the magnitude and direction of errors in a census is necessary in order to present to users the extent of reliability and accuracy of some characteristics reported in the census; and it is advisable to conduct a post-enumeration survey as part of the pilot census and immediately after the census.

50. In addition to evaluating coverage and content errors for some census items, the results of a PES have other practical uses: they offer an opportunity to learn from procedural and conceptual limitations in the census that need improvement in future censuses; a PES can identify erroneous procedures used in a census; in conducting subsequent censuses, some lessons learned from the PES can be used to improve implementation and methods of future censuses.

Statistical capacity

51. Statistical capacity encompasses a number of elements such as: the organizational structure of the NSS; human and financial resources; statistical training, and data collection, processing, analysis and dissemination capabilities. In many African countries, the statistical capacity is weak.

As stated earlier, collecting a myriad of MDG statistics requires a mixture of sound data sources such as civil registration, sample surveys, censuses and administrative records, all of which require viable national statistical systems. In some countries, NSOs and other producers of statistics, such as line ministries, do not have the capacity to produce high-quality MDG-related statistics as a result of the lack of trained human resources, high staff turnover, and inadequate resources 19]. In July 2006, the United Nations Economic and Social Council adopted its resolution 2006/6 on strengthening statistical capacity in countries and included a set of recommendations to improve the coverage, transparency and reporting on all indicators.

Creation of databases

52. The major sources of data for MDG indicators include censuses, surveys and administrative records, so the relevant data needs to be in one or more databases. In this regard, the NSO would be the appropriate agency to maintain the database as in most countries it is the institution responsible for statistical coordination.

53. Advances in information and communication technology, coupled with improved coordina

tion in establishing priorities and standards by the national statistical system, can make a big dif-

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ference. Statistical outcomes will show more consistency and better comparability over space and time.

54. At the international level, the IAEG set up a Task Force to work out a mechanism for the exchange of MDG data within a country and between international agencies. The Task Force has been working on the Data Structure Definition (DSD) and Code Lists for MDG indicators. The Sta

tistical Data and Metadata Exchange (SDMX) standards give technical specifications for exchanging statistical and metadata. They have created the DSD, which is flexible, enabling agencies to report data using SDMX (UNSD, 2010) [10]. The challenge is to use SDMX universally as a basis for data exchange between countries and international organizations.

Comparability of MDG statistics

55. One of the major challenges to MDG statistics is ensuring comparability of data over a period of time within a country and at international level. Comparability of data is a problem especially when different sources are combined. In general, statistics have greater usefulness when they are amenable to comparison over space and time (Depoutot, 1998) [11]; this is certainly true for most MDG statistics. In order to monitor change across geographic, sectoral, and temporal dimensions, the comparability of MDG statistics needed requires the use of common concepts, definitions and, to some extent, methodologies for data collection and analysis.

56. While the collection of comparable MDG statistics is difficult, their importance is increasing.

Problems associated with assembling cross-national comparable data include the need for the low

est common denominator, the burden created on responding countries while the cross-national data may not be specific to national needs. There is a lack of metadata supporting most cross-national data, making the interpretation and comparison of MDG data problematic.

Specific compilations such as CO, data

57. Countries are obviously facing problems in the compilation of specific indicators that are not regularly produced by national statistical authorities because they are not relevant in the national context or are not among their specific priorities. An example of this is CO emissions data. Inter

national organizations responsible for the MDGs and this particular indicator should assist African countries in addressing the specific data required and the recommended method for computing the indicator.

3.3 Monitoring MDGs at national level

58. Monitoring at national level involves many stakeholders and institutions. The stakeholders range from planners, decision-makers, research workers, NGOs, the public, development partners, etc. These stakeholders should be informed of progress being made in the achievement of the MDGs.

They could be informed through publications that they can obtain or purchase on the designated release dates, or by accessing reports through the Internet on a dedicated website or institutional website, such as that of the NSO.

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59. The ideal institutional framework for MDG monitoring and reporting at national level would involve the major producers of the MDG indicators, major users and an MDG committee with spe

cific objectives.

Figure 3.1: MDG Data Compilers and Users at National Level

60. Ideally, individual line ministries have the responsibility of compiling MDG data that are rel

evant to them and compute the relevant indicators. Other agencies have the same responsibility as line ministries. The NSO is the central repository of all the MDG data because it produces and stores the largest socioeconomic data set in the country and therefore has the central MDG database, and responsibility for reporting and dissemination to international agencies.

61. An MDG committee generally has the following tasks:

» Take stock of the MDG indicators

» Review existing indicators

» Propose indicators relevant to national development strategies

» Propose means of computation of indicators.

62. Monitoring indicators at national level can be presented in a Mapper format.

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3.4 Monitoring MDGs at subnational level

63. The major sources of data for MDG indicators are population and housing censuses, house

hold surveys and administrative data sources. In most African countries, MDG data are available mainly at national level but not always at subnational level. This is because most surveys are con

ducted with the objective of producing estimates at national level. Sometimes surveys are conducted for urban areas only. The only exception is the decennial population and housing census which covers the whole country, making it possible to produce statistics for the smallest unit of village or enumeration areas, for example. Another problem is the availability of vital statistics.

64. Most countries do not have a CRVS system in place, making it difficult to produce some of the indicators at subnational level since the data at that level are simply unavailable. Administrative data, which is another major source of MDG data if available, can facilitate the computation of some of the indicators at the subnational level. They can be used to compile data at provincial and district level. These statistics can enable computation of some indicators, such as the school enrolment ra

tio.

65. If data and indicators are available at subnational level, they can be presented in a Mapper format.

3.5 Monitoring MDGs at Regional Level

3.5.1 MDG Africa Steering Group and MDG Africa Working Group

66. In 2007, leaders of the multilateral development organizations established an MDG Africa Steering Group to identify the practical steps needed to achieve the MDGs and other internation

ally agreed development goals in Africa. In order to achieve this objective, an MDG Africa Working Group was formed to support the work of the MDG Africa Steering Group.

67. One of the technical thematic groups formed by MDG Africa Working Group was in the area of statistics: the Statistics Cluster of the MDG Africa Working Group. This group was made up of the following organizations:

» The African Development Bank (AfDB)

» The African Union Commission (AUG)

» AFRISTAT

» The Partnership in Statistics for Development in the 21st Century (PARIS@OECD)

» The United Nations Development Programme (UNDP)

» United Nations Economic Commission for Africa (UNECA)

» The World Bank

» The United Nations Statistics Division (UNSD)

68. One of the objectives of the Statistics Cluster of the MDG Africa Working Group was to assist countries to build statistical capacity on a sustainable and scaled-up basis within the Reference Re

gional Strategic Framework (RRSF) to collect, manage and use statistics for evidence-based decision

making and tracking progress made towards the achievement of goals, including the MDGs.

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3.5.2 The Statistical Commission for Africa Working Group on MDG Indicators

69. During the first meeting of the Statistical Commission for Africa (StatCom-Africa) in January 2008, participants discussed the setting up of the Working Group on Development Indicators. The rationale behind this was that many challenges would hamper the capacity of African countries to report on progress or lack of progress in reaching the MDGs.

70. The main objective of the Group was to support StatCom-Africa in its quest to address the MDG monitoring challenges facing African countries. The Working Group was expected to meet annually. Its specific objectives are to address:

» Problems posed by the overall reporting mechanism on the MDGs

» Lack of data on a number of indicators

» Lack of some subnational data in support of targeted policy decision-making

» Discrepancies between country-published data and those generated by international organizations

» Inadequacy of mechanisms for validation with NSOs

» Estimates made by international organizations for missing country data

» Lack of coordination among statistical producers and users within the country, between countries and international organizations, and among international organizations

» Lack of harmonization of concepts, methodologies, standards

» Lack of sustainability of data production for MDG indicators

» Lack of human and financial resources undermining the statistical capacity to generate, process, package/disseminate MDG indicators by the NSS.

71. The membership of the StatCom-Africa Working Group is African countries, subregional and regional organizations, and international organizations. ACS serves as the secretariat of the Working Group.

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Chapter 4: Description of Good Practices in Selected African Countries

This chapter describes the good practices that have been adopted by African countries for monitoring and reporting on the MDGs at national and subnational levels.

4.1 Sources of Information

73. The information sources used for the preparation of this chapter included the following:

(a) Presentations made by countries that attended the Regional Workshop on MDG Moni

toring in Africa: Issues, Challenges and the Way Forward, 7-11 March 2011, Kampala, Uganda. Participants at this meeting also attended the EGM for the 2011 Report on As

sessing Progress Towards Attaining the MDGs in Africa, 10-11 March, Kampala, Uganda;

(b) Draft questionnaire sent by the ACS to heads of NSOs in order to identify countries to be featured as undertaking good practices with regard to the following seven criteria:

(i) strong political commitment; (ii) effective coordination mechanisms of the NSS; (iii) aligning the national data-collection undertakings to meet the requirements of interna

tional standards and concepts; (iv) production of disaggregated data on MDG indicators;

(v) clarity of indicators and metadata; (vi) effective and efficient storage and dissemina

tion of MDG data; (vii) establishment of a complete CRVS system ;

(c) Presentations made by countries at the Expert Group Meeting on the Millennium Devel

opment Goals, 21 -24 November 2011, Yaounde, Cameroon;

(d) Best practices write-ups submitted by countries following presentations made at the Expert Group Meeting on the Millennium Development Goals, 21-24 November 2011, Yaounde, Cameroon;

(e) Other documents prepared by UNECA, UNSD and other organizations.

4.2 Highlights of good practices

74. The highlights of good practices that are presented below are a combination of those that were identified from the responses to the "Questionnaire for the Best Practices Report on MDC Monitoring and Reporting at National and Subnational Levels in African Countries" and best prac

tices write-ups submitted by countries following the presentations they made at the EGM on the Millennium Development Goals, 21-24 November 2011, Yaounde, Cameroon.

4.2.1 Strong political commitment

Criterion Number 1

Title Strong political commitment

Country Botswana

National Statistical Office Statistics Botswana

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(i) Background

75. Until 2009, Statistics Botswana operated under the Statistics Act of 1967. The Government of Botswana provided a legal framework requiring official statistics to be produced by means of a process guided by the NSS. This framework provided principles, including aspects of relevance and confidentiality, for the production and dissemination of official statistics. In 2009, Parliament passed the new legislation, which established a semi-autonomous body known as "Statistics Botswana".

The legal framework also ensures that the Government provides funds for the compilation of official statistics. The new legislation also empowers Statistics Botswana to coordinate the activities of the

NSS effectively.

(ii) Description of the good practice

76. In order to demonstrate its commitment to this criterion, the Government of Botswana has undertaken the following:

» All surveys and censuses are funded by the Government, most recently the 2011 Popula

tion and Housing Census.

» The Office of the President has committed itself to funding the Botswana Aids Impact Survey every four years to enable monitoring of HIV indicators. The next Aids Impact Survey was conducted in 2012.

» The Government of Botswana requested Statistics Botswana to conduct the Core Wel

fare Indicator Survey in 2009, although it was not part of its planned statistical activities.

» The Government of Botswana now requires poverty indicators to be updated every five years, as opposed to the previous practice of updating poverty indicators every ten years.

(iii) Benefits

77. The benefits of this good practice have so far included the following:

» Development of a centralized website that has enabled theVision 201 6 MDGs database to be managed by Statistics Botswana.

» Production of a yearly Statistics Brief on Vision 2016 and MDG Indicators.

» Statistics Botswana staff seconded to line ministries are responsible for data collection, making statistical coordination much easier, and in particular for data on MDGs indica

tors to be forwarded to the database manager at Statistics Botswana as soon as they are published.

» Indicators such as net primary enrolment and ratio of boys to girls in primary education are now produced yearly from administrative data sources.

(iv) Challenges

78. The challenges of this criterion have so far included the following:

» Lack of an NSDS, which has been delayed due to census activities and the process of transformation from CSO to Statistics Botswana status.

» Although the Government is willing to fund statistical activities, currently Statistics Bot

swana is unable to conduct other surveys because of staff shortages.

» Inadequate technical skills in analysing survey data has resulted in delays in publishing statistical results.

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» Inability to attract and retain professional staff clue to low pay. There is need for retrain

ing and continuity in applying international statistical standards and concepts.

(v) Way forward

79. Since Statistics Botswana is in the process of transformation to semi-autonomous status, the NSDS needs to be drawn up. This will enable the strengthening of data collection, analysis and dis

semination activities.

Criterion Number 1

Title Strong political commitment

Country Democratic Republic of Congo

National Statistical Office Institut national de la statistique

(i) Background

80. For more than a decade, the NSS of the Democratic Republic of the Congo faced many diffi

culties producing and releasing relevant and reliable statistics to users. This was revealed by a num

ber of evaluations of the organization and its functioning, all of which identified the same obstacles and weaknesses constraining the growth of the system. Those obstacles included: low capacity of the system, partly due to the low supply and demand for statistical data; little use of statistical data at all decision-making levels; and absence of a financial policy for official statistics. As long as those weaknesses existed, the NSS of the DRC could not adequately support the implementation and monitoring of development policies and programmes such as the MDGs, and growth and poverty reduction strategies (GPS). This situation led the Government of Democratic Republic of the Congo to set up strategies and policies for the NSS.

81. There were also Government efforts in different social sectors of the economy, such as pri

mary education, which had in previous years seen a considerable rise in enrolment rates, the health sector, which resulted in a fall in infant mortality, while maternal mortality was reduced. These re

sults were the fruit of strengthened partnership between the Government and the international com

munity.

(ii) Description of good practices

82. The Democratic Republic of the Congo has implemented a number of activities that prove its strong political commitment to achieving the MDGs:

» Promulgation and implementation of Decree No. 10/05 of 11 February 2010 on the NSS, containing statutory instruments relating to sector-based statistical services that strengthened the autonomy of the NSO in its missions.

» Three statistics training institutes.

» Implementation of the second generation of Poverty Reduction Strategy Papers (PRSPs), one of the pillars of which is the production and availability of quality statistics.

» Effectiveness of the subnational statistical and planning offices, as well as the initiative of creating provincial calculation centres.

» Operational sector-based statistical services producing administrative statistics.

» Establishment of websites and databases.

Best practices report on millennium development goals monitoring and reporting at National and Subnational levels in African countires

ECONOMIC COMMISSION FOR AFRICA African Centre for Statistics