Embed Size (px)
Citation preview
November 2004
Contents
Sta
tsN
ew
s
2
3
5
6
7
11
15
16
19
20
22
25
27
31
33
36
38
40
42
45
Editor’s note
Message from the Statistician-General
Transformation
Less red tape challenge
Counting the future
Towards a people development strategy in Stats SA
Quality and methodology
Implementing data governance
Methodological support
Addressing the nation …
Assessing and improving statistical quality
The role of research in census process redesign
Governance, planning and integration
Developing a Management Information System (MIS)
Project management training
The risk management process within Statistics South Africa
Integration changes data processing
Product alignment to meet user needs
Census of Commercial Agriculture 2002
The determination of underlying causes of death
Population statistics is as important as economic statistics for a country
Second round of census products
Meeting national and international obligations
National Statistics System
Towards a harmonised Consumer Price Index (HCPI)
Editor’s note
Connie Muvunyi, Editor
2
Ed
ito
r’s
no
te
The June edition of StatsNews introduced our readers to some of the important activities for the year, such as the National Statistics System (NSS), Consumer Price Index (CPI), Gross Domestic Product (GDP), Causes of death, the organisational responsibility regarding HIV/Aids and a concerted effort to improve the quality of our products in order to meet our stakeholders' needs.
This edition attempts to outline the progress we have made so far with these activities. It also includes other information such as the findings of the 2002 Census of commercial agriculture, the importance and role of research in census process redesign, the vital role that population statistics play in our lives, and many more interesting articles. We also read about the many products from census 2001, which have been in high demand from our users. As you read every article, just remember that it represents many days of hard work during the year for the components involved.
StatsNews congratulates Annette Myburgh, Executive Manager of the Programme Office and Management Information System (MIS), and her team on winning the red tape challenge competition and making us all proud. Read about it in her article on page 5. As always, I also wish to take this opportunity to thank the authors of articles who managed to set time aside to contribute to the publication.
In conclusion, I wish everybody a joyous and restful festive season. Drive safely if you are going away on holiday. We want you alive and refreshed to share opportunities and tackle more challenges next year! Le itshware hantle!
Editor
Message from the Statistician-General
3
Me
ssa
ge
fro
mth
eS
tati
sti
cia
n-G
en
era
l
Pali J Lehohla, Statistician-General
This StatsNews edition bears testimony to the progress we have collectively made in the past four years. Whilst we have in a very frank manner managed to critique ourselves and recognise our shortcomings in a number of our own reports, this edition of StatsNewsserves to concretely highlight what we have been hard at work doing, having reflected on these serious limitations.
I am reminded of what Dr Warren Bennis said in May 2000 when the University of Southern California was celebrating his 75th birthday and his 50 years as a student of organisations and leadership. When asked: “What would you say is the essence of effective leadership?”, he responded: “The enduring quality of leadership is managing relationships.”
So have we managed our relationships much better?
Yes we did. Earlier on, when I was appointed as the Statistician-General, I took over from the Acting Statistician-General, Dr Ros Hirschowitz, who had established such a strong working relationship with the management, provincial and head office staff members and organised labour under the umbrella body called ODeTT. We have seen over the years how we have consolidated our relationships with other stakeholders such as the Minister, the Statistics Council, parliamentarians, legislators, the media, political and administrative leadership in the provinces.
On the international relations side, we have emerged with a better reputation in the international community. Our colleagues out there respect our products which include, among other things, our GIS and poverty mapping. We have co-chaired the Partnership in
stStatistics for Development in the 21 Century (PARIS21) and have smoothly handed over to Ghana. We currently chair the Advisory Board of Statistics in Africa (ABSA).
Of course we do all these against some strained relationships with some of our stakeholders, as alluded to in recent media coverage. The situation is, however, under control and we are working very hard to mend these poor relations. We have also emerged very poor on matters of governance. For this we hold ourselves responsible and will not yield an inch until we have complied completely and successfully with the requirements of good governance. Our collective march towards a clean annual audit report is real, and we will succeed.
We hold ourselves responsible and accountable. Our operating model tells us that we will work smarter. Our MIS tells us that there are still gaps and we need to deal more decisively with our key projects, some of which are elaborated in detail by different contributors in this edition.
Our Management Review last year also showed gaps that existed among ourselves, some of which we have addressed through the operating model and the aligned structure. But some of these are not just structural issues.
When senior managers met last year at Mabalingwe outside Bela-Bela, there were allegations of corruption hanging around the Statistician-General's neck. We have survived that and will not allow ourselves to be dogged by those kinds of allegations among our ranks.
This year, we unveiled an aligned organisational structure. This alignment compels us as managers to engage in a paradigm shift and apply what Ralph Stacey refers to as 'extraordinary management' techniques. Extraordinary management enables leaders to operate in uncertainty and to apply ingenuity and different techniques to the new challenges that they face in the 21st century. It is a process that involves 'questioning and shattering paradigms, and then creating new ones. It is a process which depends critically upon contradiction and tension.' Stacey argues that successful organisations result from leaders who thrive on chaotic situations and who find innovative and creative solutions for these situations.
Organisations in the 21st century need leaders and managers whose challenges include:
• Leading organisations in an environment of dwindling resources and increased demands for quality service, sustainable development and retention of skills for organisational continuity;
• Cultivating a performance management culture with emphasis on results;
• Upholding good governance despite a hostile and ever-changing environment;
• Instituting effective staff retention strategies amid a change fatigue syndrome; and
• Keeping pace with advances in information technology and workforce renewal.
We are statisticians charged with a responsibility for official statistics. There are so many challenges facing us. As the year ends we will be releasing the GDP. The GDP is often a surrogate measure governments use to judge how well they have performed; and society, the media in particular, uses this measure to judge the overall performance of governments. So, we are sometimes required to illuminate issues, not just monitor them. This requires a high level of public credibility, since few in society can verify national statistics.
We have a noble duty to perform, and we better perform it right always. As statisticians we have to take note from former President Mandela, who, when asked by a journalist how he keeps pace with his numerous activities at the age of 82, said: “Do your homework. Watch TV for just an hour, then go back and do your work, especially mathematics and statistics for 30 minutes a day. Don't leave it for two days.”
Lastly, I wish all Stats SA staff the best of luck and peace during your well-deserved break. Sharpen your axe as the task ahead is immense. Don't leave statistics for two days.
Pali Lehohla
4
Me
ssa
ge
fro
mth
eS
tati
sti
cia
n-G
en
era
l
Tra
nsfo
rm
ati
on
Less red tape challenge
5
The Centre of Public Service Innovation (CPSI), in partnership with the Department of Public Service and Administration (DPSA) and the State Information Technology Agency (SITA) released the results of a study on red tape on 18 March 2004 with a key message that the time is right for greater focus on improving the performance of the public service by tackling red tape.
Reform of red tape can take many forms. Some require major regulatory change, vertical and horizontal coordination across government, new technology, and even new types of organisations. Hundreds of improvements can be achieved quickly and simply within a single organisation and even within a unit of an organisation. Addressing these quick-wins is an important pillar of the red tape reform agenda of Government.
Based on the abovementioned issues, the CPSI, DPSA and SITA encouraged public service employees to put forward their ideas to reduce red tape.
The Programme Office and Management Information System (MIS) division, on behalf of Stats SA, submitted a proposal on how to reduce red tape. The proposal was selected as one of the winners of the red tape challenge.
Managers at all levels of an organisation need to make decisions daily. This could have an impact on the performance of their organisation, and the delivery of their products and services on time, within budget and according to quality and other requirements of stakeholders. The process of
Annette Myburgh (front row, right) with the Programme Office and MIS team
gathering integrated information across the department on, such matters as organisational, project or activity performance against plans; human resource distribution; risk management; expenditure against budget; and so forth, is tedious and time consuming due to the number of people involved. This affects the quality and effectiveness of decisions made, governance and service delivery as the decision making process takes long or decisions are based on no or incomplete information or information that is old.
The Programme Office and MIS division proposed an integrated management information system, which will enable decision makers to gain access to real time integrated management information. The integrated management information system would for example focus on collection, storage and retrieval of information according to set standards and guidelines generated as a result of building deliverables and outputs; and the process of managing the organisation, programmes and projects. This system would be accessible to all staff across the entire organisation including staff at provincial and regional offices. An on-line real time integrated management information system, running on the Intranet of the relevant Department, which should be operational via an Internet information server from Microsoft, should facilitate the above-mentioned.
The integrated management information system will enhance decision-making, good governance and service delivery.
Annette MyburghProgramme Office and MIS
Tra
nsfo
rm
ati
on
'Counting the future', the theme of our annual review, reflects where our energies and talents have been channelled this year. Talking about the future in a diverse society like ours makes it hard to ignore the voices of our colleagues who are infected and affected by HIV/Aids.
In March 2003, the HIV/Aids policy was officially signed and adopted by Statistics South Africa's Management. This changing profile led to a review of services, with the appointment of the coordinator of the Stats SA HIV/Aids programme four months later.
The role of the coordinator, amongst other things, is to market and communicate the HIV/Aids policy to staff. The marketing drive made important inroads when Dr Ros Hirschowitz (DDG for Quality and Integration) was appointed as an Aids Champion on 19 November 2003.
The programme strengthened its campaign by providing education and counselling support services. For many colleagues, this drive has built sufficient confidence to move out of the shadow of anonymity and fear into the world of dialogue about disclosure and openness.
Despite these positive strides, the programme has had its share of difficulties. Stigma, feelings of shame and anxiety, prejudices and discrimination are some of the pressures that employees living with HIV/Aids still experience.
In an attempt to avert some of these challenges, we have developed various campaigns and initiatives such as daily email-talk messages, lift news and monthly posters. The daily email-talk messages open a platform for a dialogue about the future, while the lift news and posters empower employees through information and advice on policies and strategies.
The programme also provides peer support, where employees living with HIV/Aids engage in dialogue with their peers at both provincial and national offices.
An HIV/Aids committee was also formed as provided for in the HIV/Aids policy. The committee is committed to increase HIV/Aids awareness and service improvement through stakeholder involvement. It provides an opportunity to those on the programme to speak out and make a difference for those who are still silent.
Finally, the courage and strength of those who broke the silence have contributed immensely to policies and change of attitude. It is tribute to those we have lost to HIV/Aids that we are inspired to work towards ensuring that the future is counted.
Melusi MsimangoHIV/Aids Programme Coordinator
Counting the future
Melusi Msimango, HIV/Aids Programme Coordinator
6
Students and the Statistician-General at the send-off function at Bentley's Country Lodge
A skilled workforce is essential for any organisation. In Stats SA, capacity building has been identified as one of the top priorities. At present the organisation lacks sufficient statistical capacity to deliver on the mandate of producing quality products and services. The absence of adequate and appropriate skills has impeded Stats SA's ability to provide the sustained statistical leadership required for the whole of the national statistics system.
The early initiatives
Training in the organisation, which was conducted by the Human Resource Development (HRD) section and other divisions, has mainly been in the non-core areas, leaving out the core areas like Statistics, Demography, Economics and Geography. The need to addresstraining in the organisation's core areas led to the establishment of a Statistical Capacity Building (SCB) division whose main function was to oversee the necessary training.
Towards a people development strategy in Stats SA
Mbulaheni Nthangeni, Human Capacity Development
Tra
nsfo
rm
ati
on
Shortly after the Statistician-General, Pali Lehohla, was appointed late in 2000, Stats SA, in conjunction with the Statistics Council, launched an urgent search for institutions offering appropriate training in statistics. Two institutions were identified: the East Africa Statistical Training Centre (EASTC) in Tanzania and the Institute of Statistics and Applied Economics (ISAE) at Makerere University, Uganda. The former offers training in official statistics at undergraduate levels (Certificates and Diplomas) and the latter at Bachelors and Masters levels.
EASTC was established in 1965 by the United Nations Economic Commission for Africa, the East African Common Services Organisation (EACSO) and the United Nations Development Programme (UNDP). By 1977, the Tanzanian government had taken over the running of the institution, and expanded the number of African countries participating in the programme.
7
M. Nthangeni (extreme left) and Mr Pali Lehohla (standing, middle) with students at Makerere University
Tra
nsfo
rm
ati
on
The Institute of Statistics and Applied Economics (ISAE) in Uganda was set up in 1969, within the legal framework of Makerere University. Its stated objective was to set up high level training of professionals in statistics and applied economics.
To date, more than 30 Stats SA staff members have graduated from the two institutions. The first group graduated in 2003. They have now rejoined the organisation and are making significant contributions. Stats SA continues to send staff members on an annual basis, to study at the two institutions.
Also, a few years ago Stats SA started sending some staff members to enroll for Honours and Masters degrees in Statistics at the University of Witwatersrand, with specialisation in Data Mining and others in Demography. The first group is expected to graduate at the end of the year.
Integration of training
For some years, training and development in Stats SA has been fragmented, leading to a haphazard and uncoordinated capacity building process. It also led to the duplication of efforts, inefficient application of resources and lack of an integrated development plan. The establishment of the Human Capacity Development (HCD) division, a result of the merging of SCB and the Human Resource Development (HRD) components, has been the first attempt in integrating and centralising all training and development activities in the organisation.
The outcomes-based approach
Since the beginning of the year, the HCD division has been working on the outcomes-based approach to training and development. This will assist the division to align the training with organisational needs. It is also a requirement by the South African Qualifications Authority (SAQA) that all training should be in line with the National Qualifications Framework (NQF) for it to be accredited.
The first step in implementing the outcomes-based approach is to conduct a competency profiling of all the occupations. This is a method for identifying specified skills, knowledge, attitudes and behaviour necessary to fulfil a task or activity. This is followed by the skills audit for each occupational category. Training is aimed at filling the gap between competencies and skills audit.
8
Tra
nsfo
rm
ati
on
HCD has developed a people development framework for training and development in the organisation. This framework is shown in the diagram below. The framework provides for training of external and internal recipients (stakeholders) by internal and external service providers.
Stats SA is also planning to establish a statistical training institute (STI) in the near future, offering training in official statistics for both internal staff and personnel from other departments. Discussions with universities have been going on, with the aim of getting them to start offering programmes in official statistics and also collaboration on the envisaged statistical training institute.
E-learning
The purpose of e-learning in Stats SA is to provide an appropriate, more efficient response to skills needs and to address educational requirements as mandated by legislation.
The need for e-learning in Stats SA stems from several factors. Foremost amongst them are:
• A perceived lack of relevant skills
• Training interventions that are poorly aligned with skills needs
• The lack of skills-tracking instruments
• The increasing cost of face-to-face instruction.
The advantages of e-learning are:
• Blended learning: a combination of face-to-face and electronic media instructions
• Needs-driven: just-in-time learning
• Personalised: Own time, own pace, own place
• Cost-effective: locale-independent
• Enabler for life-long learning.
E-learning will also come in very handy for training of staff members at the provincial Stats SA offices and other government departments.
Future challenges
Lately, the need for statistical information at all tiers of government has increased tremendously. In particular, the need for having people trained in the gathering and use of statistical information at municipal level is critical.
Results of the skills audit conducted by the National Statistics System (NSS) division indicate that there is a very critical skills shortage in most government departments. There is a need for establishing statistics units in these government departments, generating demand for skilled statistical capacity.
These are some of the challenges that HCD will have to address.
Mbulaheni NthangeniHuman Capacity Development
9
Competency ProfilingProcess
People Development in Stats SA
Unit Standards Qualifications
Skills Audit
Workplace Skills Plan
Material Development
Training
E-learning(Workplace
basedlearning)
Classroom andWorkplace based
learning
Individual DevelopmentPlans
PerformanceManagement Internal Stakeholders External Stakeholders
Non-core Core
CareerDevelopment
JournalistsParliamentariansOther Gov Depts
Local Government
ExternalISAE
EASTCWits
In-house/In-serviceBasic Sampling
Advance SamplingShort Courses
Skills ProgrammesLearnerships
Internship
Monitoring &Evaluation /QA
SOSProgramme EvaluationQuality Man. System
CurriculumStatistics
Social StatsEconomics
DemographyGeography
Non-core
EmploymentEquity
StatisticalTrainingInstitute
Tra
nsfo
rm
ati
on
10
The data management and information delivery project (DMID) began as an initiative to construct a stand-alone data warehouse. As research and consultation progressed, however, the project developed a far wider focus.
International visitors
Part of this process involved discussions with other statistical agencies engaged in data warehousing initiatives. Graeme Oakley, a senior manager from the Australian Bureau of Statistics (ABS), visited DMID in June 2004 to assess the project and provide some advice on the way forward. Based on his recommendations and feedback from other statistical agencies, the project has been redesigned. The problems Stats SA is grappling with in designing and building the data warehouse are similar to those experienced by other statistical agencies, and consultation with them has allowed for a rapid learning curve based on experiences elsewhere in the world. Data warehousing is a high-risk and complex activity, and there are no easy solutions available off the shelf!
An old friend of Stats SA, Dr David Beckles, who was involved with the data- processing of Census 2001, has also worked intensely with the DMID team to conceptualise some of the project phases, and provided valuable insight and comments on the work done by the unit.
Qu
ality
an
dm
eth
od
olo
gy
Implementing data governance
Marius Cronje, DMID Project
Left to right: David Beckles, Ashwell Jenneker and Antony Cooper
11
DMID and its five sub-projects
DMID now consists of five interdependent sub-projects: the Metadata Repository,CaRS (Classifications and Related Standards) Implementation, the DataRepository, Standards Development, and Policy. The Metadata Repository and CaRS Implementation will result in a Central Metadata Repository (CMR), which, together with the Data Repository, will form the Data Warehouse itself. The other two sub-projects provide supporting technologies and frameworks for the Data Warehouse and its components.
MetadataRepository
CARSImplementation
Data Repository(1st Iteration)
StandardsDevelopment &Implementation
Policy
Business CaseJuly 04
Project PlanJuly 04
PolicyAugust 04
ContentJanuary 05
Pilot standardOct 04
Technical solutionMarch 05
TrainingMarch 05
Start maintenanceFeb 05
Business CaseJuly 04
Project PlanJuly 04
CARS PolicyAug 04
Technical DeploymentSept 04
ContentOct 04
TrainingDec 04
Start maintenanceJan 05
Business CaseSept o4
Agreement withABS – July 04
Tender: Buss requirementsNov 04
Buss req. gatheringJune 05
Tender: DW developmentAug 05
Design phaseOct 05
Construction phaseFeb 06
Testing phaseMarch 06
Deployment phaseApr 06
MOU: Stats SA &CSIR – Aug 04
Standards SLA: StatsSA & CSIR – Aug 04
Business CaseAug 04
Project PlanAug 04
Skills transfer strategySept 04
List of StandardsSept 04
Best practiceMethodology – Nov 04
Standardsinfrastructure – Nov 04
Match: national & interna-tional standards – Nov 04
Develop relevantprofiles – Nov 04
PolicyJan 05
Business Case
Aug 04
Data GovernancePolicy – Dec 04
Central MetadataRepository (CMR)
DataWarehouse
DMID Project
Su
b-p
roje
cts
De
live
rab
les
Current
Completed
DMID Project Overview
Metadata repository
Metadata (data about data) document data and the processes undertaken to create the data. Metadata should describe assumptions made and limitations in the data, and is crucial for any meaningful use of the data, and in the prevention of misuse. This sub-project aims to develop and implement a repository for the storage and retrieval of statistical metadata in Stats SA. Metadata will be stored and updated according to an approved standard, and according to an approved policy for the governance of metadata.
Central storage of data and metadata has been identified as a key element in improving the quality of, and access to, authoritative and reliable statistical information. Currently, the originating components within Stats SA develop, structure and store metadata in different ways. This limits analysis and comparability of data, and makes it difficult for both internal and external users to access and use metadata in a consistent manner. Adopting a corporate standard for the development, storage and governance of metadata will address this weakness in the statistical cycle.Q
ua
lity
an
dm
eth
od
olo
gy
12
Classifications and Related Standards (CaRS)
The CaRS system has been acquired from Statistics New Zealand for implementation in Stats SA. CaRS provides a central repository for classifications, standards, definitions, concordances and codefiles for use by all components. This will facilitate access to standard classifications and bring about better version control across the organisation.
CaRS will support a number of business outcomes that have been difficult to achieve as a result of having classifications in separate systems and on different platforms.
Data repository
The Data Repository project covers the development and implementation of a data warehouse. A warehouse is a managed data store that aims to service collection activities (including editing and estimation), analysis, research and management needs from initial unit record data capture up to aggregated data used in dissemination. The data repository will therefore support both 'real time' operational (transaction) analytical uses and post-processing analysis. The data repository is a key technological enabler in the development of new organisational arrangements to improve the quality and management of data.
Developing a data warehouse is a significant undertaking. The Data Repository project will involve development of a data warehouse in a number of iterations over several years, with progressively more functions and data being incorporated, and more outcomes realised, over time.
The first iteration will involve building a 'production pilot' warehouse for economic and social statistics with aggregate data used in dissemination. The second iteration will involve census data used in dissemination. The third iteration will convert all confidential unit record files. Following this, incorporation of datasets from other stakeholders in the National Statistics System (NSS) can be considered.
Standards development
For the data warehouse to be viable, data products have to adhere to relevant standards. Common standards, definitions, classifications and methodologies enable statistical data and information to be comparable. These are necessary preconditions for analysis, data conflation, quality assessment and methodological evaluation.
Standards of various descriptions are already in use throughout Stats SA, yet there is no formal process for approving such standards. Work in this area will include identifying both existing standards and additional or different standards required; initiating new national or international standards; embedding standards in the work of Stats SA; evaluating the effectiveness of standards; and revising or retiring standards. This sub-project will also address guidelines, good practices and other methodologies, not just for DMID but for Stats SA as a whole.
Qu
ality
an
dm
eth
od
olo
gy
13
Policy
The outputs and outcomes of the DMID project and its associated sub-projects will fundamentally change the manner in which Stats SA collects, compiles, processes, stores, retrieves, analyses and disseminates statistics.
This will entail changes in the way originating components develop and store metadata (including standards and classifications), and link these to statistical data collected. Implementation of and adherence to standard and centralised procedures for the development and maintenance of a central data and metadata repository will be an essential element of successful completion of the DMID project.
Successful management of these changes, and clear guidelines and procedures for governance of the way data and metadata are to be managed in the future, is a necessary precondition for the implementation of centralised storage and retrieval repositories.
The development of a clear and unambiguous policy will enable all statistical originating components and related support components to understand what is required of them; set governance and adherence benchmarks against which activity can be assessed; link adherence to and compliance with policy to performance management; and identify the broad parameters and direction of intended change in data and metadata management.
Agreements signed with the CSIR
Stats SA has signed a memorandum of understanding with the CSIR relating to cooperation in the National Statistics System. Arising out of this, Stats SA and the CSIR have also signed a service-level agreement, in terms of which Antony Cooper from the CSIR will consult to DMID for six months on the development of standards for Stats SA. He will assist the project in setting policies and procedures for the development of standards, working closely with members of the team as well as with members from other sections in Stats SA. A key part of the relationship will be formal and informal coaching and mentoring of Stats SAstaff.
Marius Cronje and Antony CooperDMID Project
Qu
ality
an
dm
eth
od
olo
gy
14
One of the many challenges the Statistician-General set for Statistics South Africa (Stats SA) was to address statistical units data items, classifications and standards as key to our success. As a result, Quality and Methodology (Q&M) has recently been introduced in the organisation and the support offered by this component is taking a new turn. Over and above its participation in projects, Q&M has realised the need to develop standards, manuals, guidelines and policies to bring about the smooth running of processes as a move towards quality management and the improvement of the quality of statistical products. As a result our operating methods have undergone some changes. For instance, the classifications and metadata team has joined the Data Management and Information Delivery (DMID) project to develop classifications and improve on data governance. The Q&M group has divided itself into two teams to provide maximum support to Social Statistics and Economic Statistics. The latter team has moved to the Economics division to give support to the production of the Consumer Price Index (CPI) and on sampling methodology.
Priorities
There are no commonly shared documented procedures and guidelines in Statistics South Africa, and where they exist they are not accessible to all staff members. The team supporting Social Statistics has therefore developed a Standard Operating Procedure (SOP) to guide Social Statistics on handling work processes. The implementation of this document has been tabled with the Social Statistics component and is expected to be fully operational in March 2005. The team has also participated in the development of survey manuals, and is currently working on the Data Processing Manual.
The team has also identified standards for surveys. These include maintaining confidentiality, developing proposals for ad hoc requests, initial
Methodological support
Margaret Africa and Themba Mohoto, Quality and Methodology
planning of surveys, quality control procedures for new releases, publication and product planning, design of surveys, survey response rate parameters, pre-testing of survey systems, maintaining data series over time, training, coverage for frames and samples, and achieving acceptable response rates. Other standards include documenting a survey system, data editing and imputation of item non-response, evaluation of surveys, non-response bias analysis, statistical analysis, inference and comparison, variance estimation, rounding numbers and percentages for reporting in text and displaying in summary tables and figures, and lastly the review of reports and data products.
Consultations are being held with relevant persons in Stats SA. This is a long process and will take a long time to complete. It also requires commitment and cooperation of production areas.
Definitions
It has been observed that within Stats SA different divisions have been using different concepts and definitions. Concepts and definitions are crucial for products developed at Stats SA in terms of quality,consistency and relevance. Team members are currently identifying duplicates, redundancies, and controversial terms in order to harmonise the various sets of concepts and definitions in use. As a first step, compendia of concepts and definitions in nine subject areas are being compiled, namely: Population statistics, Social statistics, Economic statistics, Macro-economic statistics, Data processing, Post-enumeration surveys, Metadata, Statistical indicators and quality.
Q&M is also working hand in hand with DMID to develop policies on handling requests, the dissemination of data and metadata, and document control.
Margaret Africa and Themba MohotoQuality and Methodology Q
ua
lity
an
dm
eth
od
olo
gy
15
For most of us having an address is taken for granted. However, a large proportion of the country's households do not have addresses. Formal urban areas have long established addressing systems, which form the basis for postal services, emergency response and other services. However, an address system in informal areas such as urban informal and tribal communities of our country is largely non-existent.
The national address system and register is a unified spatial framework that assigns one (and only one) unique identifier to every physical dwelling, site or structure. Our experience thus far shows that attempts were made in the past by various organisations at providing addresses. Statistics South Africa (Stats SA) wanted to provide them for census taking, Eskom for electrification, Telkom for providing telephone communications, and the local authorities for providing a range of services including facilitating elections.
However, this could be seen as a sectarian approach to providing addresses. This approach only led to confusion and duplication that is costly to those who supply these identifiers and not meaningful to recipients. Whilst the identifiers supplied may be unique from the perspective of sectarian suppliers, they are not unique to those who receive them. This fails the test of assigning a unique identifier supplied from a unified spatial framework. Therefore this shows that an address is an important identifier that facilitates interaction between service suppliers and recipients.
Addressing
Sharthi Laldaparsad, Geography
the nation …
Census and local authority numbers spray-painted on houses
Qu
ality
an
dm
eth
od
olo
gy
16
Stats SA is leading a team of intergovernmental role-players in an attempt to coordinate all address initiatives and related activities. Who would have thought that addresses are important for statistics? But they are. This detailed geographic unit will improve our data collections for surveys and censuses and will form the basis for our enumerator area demarcation. The address project is a complex, integrated project with several key role-players trying to meet the requirements for each of their mandates, each for the betterment of the people on the ground. Key role-players are the South African Post Office, a portfolio organisation falling under the Department of Communications, the local municipalities and metropolitan councils, the Departments of Land Affairs, Home Affairs, Housing, Water Affairs and Forestry, Social Development, Treasury, Public Service and Administration, Minerals and Energy, and the State Information TechnologyAgency (SITA).
For the Post Office, the project will help in their rigorous endeavour to move away from cluster boxes towards personal mail delivery. At local government level, addresses are certainly a prerequisite for service delivery. The Department of Land Affairs will use them in the allocation of communal land to the communities in terms of the recently enacted Community Land Rights Act (CLRA). Finally, the Department of Home Affairs provides for the inclusion of addresses in the population register, which would reduce the need for too frequent population censuses.
Accomplishments
The Governance and Administration (G&A) cluster will serve as a steering committee for the project. This is chiefly to engender support at a head of department level and for interdepartmental cooperation. A programme management committee, led by the heads of Stats SA and the Department of Communications, has also been established.
Two technical task teams, comprising various government departments, have been established to further strengthen the project. The first task team, which deals with communications, liaison and legal affairs, is looking at the mandates and policies of the project as well as at the promotion of the project to avoid expensive duplication, hence providing for an effective coordinating of all initiatives.
The second task team, which deals with planning and operations, has developed proposed address concepts and definitions, address standards and addressing methodologies. Addressing standards are now also a work item for StandardsSouth Africa. The proposed standards imply that the system will be dependent on various departments' information, and thus the need for us to agree on data items, the geographic frame, standards and integration of work plans.
In addition, the project team also requests and analyses existing address data from the metropolitan councils and municipalities. Our observation so far is that address information, even in the formal areas where one would expect to have all addresses logged onto a system, has inconsistencies and gaps. Many areas in the country use their property identification numbers as a form of an address.
Addressing pilots have begun in the tribal areas of the eThekwini Metro in KwaZulu-Natal, Mhinga Tribal Authority in Limpopo, Thaba Nchu in Free State, and in Eastern Cape. Consolidation of address data in the eThekwini, Johannesburg and Cape Town Metros has also started.
Qu
ality
an
dm
eth
od
olo
gy
17
The project is intended to create a complete, authoritative set of addresses for all our use.
Sharthi LaldaparsadGeography
Tribal community address points
Qu
ality
an
dm
eth
od
olo
gy
18
One important aspect of statistical quality is the extent to which the Gross Domestic Product (GDP) measures are exhaustive. Over the past few years, substantial progress has been made in the methods to assess and measure the non-observed economy (NOE). Most of these efforts are founded in the Organisation for Economic Co-operation and Development (OECD) handbook Measuringthe Non-observed Economy. In this regard, the OECD, the United Nations Economic and Social Commission for Asian and Pacific (ESCAP) and Asian Development Bank (ADB) jointly held a workshop on this topic in Bangkok on 11-14 May 2004.
The workshop discussed the frameworks and concepts relating to the non-observed economy, assessed the extent to which countries currently employ these techniques to measure the non-observed economy, and practical aspects for improving the exhaustiveness of GDP measures. The NOE covers five areas namely, underground economy, illegal production, informal sector,household production for own final use and production missed due to deficiencies in data collection programmes. The importance of an up-to-date single list business register was emphasised.
1Marietha Gouws attended the workshop and made a presentation on how Stats SA deals with this aspect of the economy.
Draft recommendations specific to National Statistical Offices (NSOs) in the ESCAP region were to:
• Take actions towards measuring the NOE problem areas that are important in individual participating countries to improve the quality of coverage of the GDP estimates;
Assessing and improving statistical quality
Marietha Gouws, National Accounts
• Ensure that national plans for improving national accounts and economic statistics included specific reference to the NOE;
• Apply as far as possible the standard framework and methods proposed in the handbook on measuring the non-observed economy, with the appropriate adaptation for national data sources and level of economic development;
• Raise awareness of the importance of the NOE among the main users of the national accounts data and include it in analysis and policy considerations;
• Allocate adequate budgetary resources for preparing national accounts statistics based on a complete coverage of the economy. Such expenditure should be seen as an investment rather than a cost;
• Where statistical legislation is missing or insufficient, work towards a legislative framework that would offer support and budgetary sustainability for statistical work; and
• Work towards transparent data dissemination practices and advance release calendars so that the public could have confidence in the integrity of the information released.
Participants noted that it was vital for a statistical agency to cover all economic activities defined in the production boundary of a country, as well as the enormous task of a national accountant to ensure that estimates of national accounts statistics are comprehensive.
Marietha GouwsNational Accounts
1 Thanks to the OECD who financially sponsored M Gouws's participation at the workshop. Qu
ality
an
dm
eth
od
olo
gy
19
The experiences of Census '96 and Census 2001 have highlighted the need for undertaking comprehensive research to inform the effective planning of the next census. This research is linked to the following focus areas:
• Enumerator area validation
• Piloting of operations
• Content research
• Research on the effects of layout and format of questionnaires
• Calibration of quality control instruments
• Business process redesign
• Performance measurement
• Measurement of stakeholder needs
• Measurement of level of stakeholder satisfaction
• Measurement of perceptions and attitudes of stakeholders and respondents
• Operational research.
Three main streams of research can be discerned, namely:
• Content research and research on the effects of layout and format of questionnaires
• Measurement of perceptions, attitudes and satisfaction of stakeholders and respondents
• Business process redesign, piloting of operations and performance measurement.
The third stream can be seen as a very basic approach in ensuring quality. It is a major concern of the management of Statistics South Africa to constantly revise and improve the business processes pertaining to population censuses. This revision can follow several different approaches. It may focus on improving the design or implementation of activities. Management may structure and reconfigure process flows and roles around such processes. Information flow or knowledge management around processes may also be changed. All such innovations are bound to have an effect on the quality of the resultsand the cost-effectiveness of the operations. It is the aim of census research to ensure rational decision-making in this regard.
Process modelling
In order to improve or redesign census-taking processes, census managers must not only understand the objectives and purposes of existing processes, but also the expectations of the customers. Process performance measurement is imperative in understanding the problems and credits related to existing processes. The existing processes must be benchmarked and modelled. Based on this, understanding innovations may be tested and
The role of research in census process redesign
John Kekovole, Census Inputs and Outputs
Qu
ality
an
dm
eth
od
olo
gy
20
introduced. Process modelling is thus the core method for ensuring sound census methodologies.
Census research starts with information gathering by census managers. In partnership with these managers, processes are documented in order to organise for process redesign, select processes for redesign, understand the existing processes and identify process enablers. A process vision is created through benchmarking and the identification of best practices and analysis of stakeholder needs.
Testing
Through these discussions research projects that would inform decisions on methodology are identified. Census managers, as domain experts and owners of the processes, take responsibility for the finalisation of methodology, establishment of measurements, setting targets for the processes and implementation. The design of research projects and the interpretation of research results are thus the output of close collaboration.
Evaluation framework
Measurement quantifies process performance. It helps evaluate the effects of change and enables systematic improvement. It is therefore a fundamental requirement for process improvement and redesign. Indicators that measure process efficiency, effectiveness and outcomes are designed to monitor performance and are incorporated in an overall evaluation framework.
These indicators must be designed to offer minimal opportunity for manipulation. They must be acceptable to the people concerned. Although their technical design is a research function, census managers are therefore fully involved. Indicators that are complex and difficult to understand must be avoided, as must those that may cause employees not to act in the best interest of quality and cost-effectiveness. The availability of data and the cost of their collection must be taken into consideration during their design.
Cost-effectiveness
The UNFPA categorises research into census costs and operational methods, along with advocacy, capacity building and strategies as conceptual approaches for improving the efficiency and cost-effectiveness of census taking. Decisions on the scope of the census, the level of field control and timelines impact vastly on census budgets, and implications of alternative scenarios should therefore be investigated. Cost, timeliness and accuracy are essential trade-offs. Census research informs management decisions on what level of quality is acceptable and fitting to the purpose of census stakeholders.
In March 2004, Cabinet decided that population censuses will be conducted in a ten-year cycle. The next population census is therefore scheduled to take place in 2011. Stats SA will utilise the years leading up to this census to undertake appropriate research to improve the planning and implementation of various activities.
John KekovoleCensus Inputs and Outputs
Qu
ality
an
dm
eth
od
olo
gy
21
Annette Myburgh, Programme Office and MIS
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
22
Coordinated and integrated management information on all operations and projects is very important for all managers at all levels in an organisation in order to improve service delivery; ensure relevant, accessible, interpretable, documented, timely, high quality programme and project results; and enhance good and effective governance of resources.
The MIS at Stats SA focuses on collection, storage and retrieval of information according to set standards and guidelines generated as a result of building deliverables and outputs; the process of managing change; the process of managing programmes and projects; programme and project communication; deliverable reuse; knowledge transfer; etc.
The Programme Office and MIS division within Stats SA is responsible for developing a MIS for the organisation. The development work started in 2003. The MIS will include all relevant management information across all operations of Stats SA. The aim is first to develop three main components of the MIS i.e. business management, project management and financial management (refer to figure 1) before addressing the other components of the MIS.
Developing a Management Information System (MIS)
MIS
Project &operational
managementsystem
Financialmanagement
system
Businessmanagement
system
Figure 1: The Management Information System Framework: Three maincomponents
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
23The business management component of the MIS currently consists of the business planning and performance monitoring, emergency personnel contact information, leave planning and tracking, and audit report monitoring systems (refer to figure 2).
The business planning and performance monitoring system was finalised during June 2004. Once fully operational, it will contain five-year business plans of executive management divisions within Stats SA as well as quarterly performance information measured against the business plans.
Businessplanningsystem
Personnelemergency
contact system
Leaveplanning &
tracking system
Audit reportmonitoring
system
Budgetingsystem
IntegratedStatisticsSchedule
Businessperformance
reportingsystem
Project &operational
planning system
Project &operationalprogress
reporting system
Personneltracking system
MIS
Figure 2: Developed systems within the Management Information System
The emergency personnel contact information system has been developed and is awaiting approval for implementation. This system contains personnel information such as the residential and postal addresses, telephone and cellular phone numbers, work station number, email address, parking bay number, mode of transport to and from work, vehicle registration number, year in which initial driver's licence was obtained, licence code, average departure time from home, average arrival time at work, average departure time from work, average arrival time at home and any disabilities of each employee.
The leave planning and tracking system has been developed. As the components within MIS are all linked to each other and integrated, this system will only be implemented as soon as the emergency personnel contact information system is operational. This system will contain information on leave planned for the year by staff across Stats SA, which will then be utilised by staff to apply for leave and seek approval, and by the Human Resource Management division to track leave taking.
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
24The audit report monitoring system has been developed and is fully operational. This system contains progress information on the implementation of audit report recommendations. Monthly progress reports in this regard are submitted to the SG, DDGs and Executive Managers of Stats SA in order to monitor the implementation of audit report recommendations, to take corrective actions and improve governance.
Future systems within the business management component of the MIS that will be developed include, inter alia, a risk management system, and a training and development of personnel tracking system.
The project management component of the MIS currently consists of the project and operational planning and performance monitoring, project progress reporting and personnel tracking systems, as well as an integrated statistics schedule (refer to figure 2).
The project and operational planning and performance monitoring, and personnel tracking systems have already gone through two development phases and currently contain one-year project and operational plans of Stats SA, personnel location information, as well as real time information on performance measured against the project and operational plans. Further improvements to this system are currently underway and will be implemented at the end of 2004 when the next project and operational planning cycle for 2005/2006 is implemented across Stats SA.
Based on the functionality and availability of real time information in the project management component of the MIS, management of Stats SA is currently able to monitor the governance structures, human resource distribution between projects, and the progress, status and variations of the different projects of StatsSA against plans. Furthermore, management is also able to monitor expenditure against budget of each Stats SA component and each large project.
The stakeholder needs and requirements regarding the development of an online project progress reporting system have been determined. The development of the initial framework is complete and further stakeholder requirements are being discussed, finalised and applied within the framework. The requirements that have been finalised and implemented in the system are now being tested to verify the accuracy of the data generated.
The integrated statistics schedule has been developed and is fully operational. This schedule links outputs of Stats SA to external requirements and events of government and aligns outputs and events within Stats SA. This schedule contains, inter alia, dates of all publications (statistical releases, reports and bulletins), press conferences, executive management team monthly meetings, project steering committee meetings, user training events, internal training seminar series, presentations, workshops, advisory committee meetings of StatsSA.
The financial management component of the MIS currently consists of the one-year project and operational budgets based on the Medium Term Expenditure Framework budgets of divisions within Stats SA (refer to figure 2).
Annette MyburghProgramme Office and MIS
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
25
Project management training
The Programme Office and MIS developed a practical project management course for Stats SA based on international best practices prescribed by the Project Management Body of Knowledge (PMBOK) and the unit standards for project management within the National Qualifications Framework (NQF).
This project management course includes, inter alia, the following:
• Introduction to the project management framework of Stats SA and the project life cycle
• Project stakeholder needs, requirements and expectations determination phase (phase 1)
• Development of a project proposal/ business case based on stakeholder needs and business requirements
• Project initiation, definition and conceptualisation phase (phase 2)
• Development of a project charter
• Project planning, scheduling and budgeting phase (phase 3)
• Development of a work breakdown structure, project plan, schedule and budget, network diagram, critical path, human resources plan, communication plan, quality plan, risk management plan, procurement and contract management plan etc
• Project execution, control and monitoring phase (phase 4), which includes, inter alia, project control system, quality control, change control, cost control, risk response control, performance reporting, document management, project administration process etc
• Development of a project control system; document management system; progress, status, expenditure and variance reports; change requests; risk and issue logs; decision logs etc
• Project assessment, lessons learnt and closure phase (phase 5)
• Development of a project closure report etc
• Project management tools and techniques such as trade-off analysis, brainstorming, work breakdown structure, process flowcharts, network diagrams including the Programme Evaluation and Review Technique (PERT) and the Critical Path Method (CPM), Gantt charts, cause and effect (fishbone) diagrams, Delphi technique etc
• Nine knowledge areas of project management, namely project integration management, project scope management, project time (schedule) and milestone management, project financial management, project quality management, project human resources management, project communication management, project risk management, project procurement management, and MIS and IT management
• Practical examples, case studies, demonstrations, and formative and summative assessment exercises e.g. on the development, implementation and monitoring of a project, chosen by each participant to work on during this course.
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
26
1.Integration
managementProject plan development
Project plan executionOverall change control
2.Scope
managementInitiation
Scope planningScope definition
Scope verificationScope change control
3.Time
managementActivity definition
Activity sequencingActivity duration estimating
Schedule developmentSchedule control
4.Financial
managementResource planning
Cost estimatingCost budgeting
Cost control
10.MIS and IT
managementRequirements
PlanningUtilisationReporting
Maintenance
5.Quality
managementQuality planning
Quality assuranceQuality control
9.Procurement &
contractmanagement
Procurement planningTender planning, tendering
Source selectionContract administration
Contract close out
8.Risk
managementRisk identificationRisk assessment
Risk response action planRisk response control
7.Communication
managementCommunication
managementInformation distributionPerformance reportingAdministrative closure
6.Human
resourcesmanagement
Organisational planningGovernance structure
Staff acquisitionTeam development
ProjectManagement
Figure 1: Project management knowledge areas
Figure 1 reflects the content addressed within each of the project management knowledge areas during this course.
We are currently conducting the third project management course within Stats SA. Approximately 30 staff members from across Stats SA are attending this course.
Annette MyburghProgramme Office and MIS
The previous issues of StatsNews outlined the role of the audit function and more specifically the role of Internal Audit within the management process of Stats SA. In thisissue we concentrate on the risk management process within Stats SA.
Risk management is broadly defined as the policy, structure, process and infrastructure applied to mitigate threats, reduce uncertainty and capture lost opportunities.
These elements are illustrated in the diagram below (Source: PricewaterhouseCoopers, 2004).
The risk management process within Statistics South Africa
Naas Du Plessis, Internal Audit
RiskManagement
Processes
Report
Identify
Analyse
Monitor Treat
Strategy
Policy
OrganisationStructures
HumanResource
Mechanisms
AssuranceManagement
EducationPerformanceMeasurement
Communications
Risk: any potential event, scenario or happening that could prevent the achievement of an objective. Risks can be thought of in three distinct senses – as threat, uncertainty or lost opportunity.
Threats or hazards: the risk of loss or a bad thing happening – typical examples include system failure, fraud, financial loss or a lawsuit.Uncertainty: the distribution of all possible outcomes, both positive and negative. In this context, risk management seeks to reduce the variance between anticipated outcomes and actual results.Lost opportunity: the risk that an opportunity is missed or of something good not happening.
The international standard for a risk management framework, formulated by the Committee of Sponsorship Organisations of the Treadway Commission (COSO), indicates that the activities applied in a risk management process should include the identification, assessment, management, monitoring and reporting of risk.
Stats SA recognises that risk management is not a single event or ''happening'', but is the summation of many management practices and actions that permeate through and are inherent in the way the organisation's business is conducted, leading Stats SA to follow an
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
27
approach of embedding the core elements of the risk management process into current management activities and processes where risk type reporting and decisions are made.
The process of implementing a risk management strategy for Stats SA, as required in terms of Regulation 3.2.1 of the Treasury Regulations issued in terms of the Public Finance Management Act, started during the latter half of 2003. PricewaterhouseCoopers (PwC) assisted Stats SA by facilitating various workshops during the risk assessment process, which was done in three phases.
Phase 1: Identification of risks, voting and rating of risks
The SG, DDGs, executive managers and the Chair of the Audit Committee held two work sessions in November 2003 where they gave their inputs on what they perceived as risks for the organisation. The identified risks were then rated following a voting process, which was done anonymously. Risks were classified according to their impact on StatsSA and their likelihood of occurrence without controls.
Phase 2: Identification of control objectives
The facilitators compiled a spreadsheet reflecting suggested controls against the risks identified. Stats SA management then held three work sessions during April and May 2004 to consider the following:
• The controls identified and currently in place to address the identified risks
• How controls can/should be improved to reduce the residual risk to levels acceptable to Stats SA by identifying responsible managers.
Phase 3: Development of a risk management plan or strategy
Stats SA is currently in the final phase of developing a risk management plan. This is done by implementing the governance structures mentioned below with the relevant responsibilities.
Risk and control steering committee
This committee is composed of the Statistician-General and Deputy Directors General. Its purpose was to act as an oversight committee to ensure that Stats SA takes appropriate actions to address the risks identified during the risk identification workshop.
The stated responsibilities of the committee were to:
• delegate risk management responsibilities to appropriate core risk focus area committees or persons;
• provide guidance on risk management;
• monitor progress of the risk mitigation strategies including status of action plans;
• maintain an enterprise-wide view of risk;
• review of the extent to which management has established effective controls over risks identified; review of the status of action plans;
• be apprised of the most significant risks and whether management is responding appropriately;
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
28
• support management in the monitoring of risks across Stats SA; and
• report to the Audit Committee on the adequacy of Stats SA's systems and controls for managing risk.
Sub-committee
This sub-committee is comprised of elected senior managers. The purpose of the committee is to review the control objectives that were identified during the control mapping workshop, expand on them as necessary and ensure that corrective action is taken where control deficiencies are identified.
The main responsibilities of the sub-committee are to:
• review generic control objectives to determine the appropriateness thereof;
• identify underlying controls and actions which support control objectives;
• identify any areas where there are control deficiencies which require further action;
• appoint individuals responsible for taking corrective actions to mitigate risks identified; and
• report to the Risk and Control Steering Committee on the status of the control environment and action plans taken to implement appropriate controls to mitigate the risks identified.
Internal audit committee
The responsibilities of this committee are to assist senior management and the Audit Committee to fulfil responsibilities to monitor systems of internal control and risks, ensure that the risk management policy has been fully complied with and implemented, review the appropriateness of the Risk Matrix, and facilitate and support the risk management function.
During Phase 3, PwC assisted Stats SA in the identification of the members of the abovementioned committees, their roles and responsibilities, as well as the development of a risk management policy for Stats SA. The Chief Financial Officer of Stats SA was tasked to drive this process further with the participation of the above structures.
Achievements
To date the following has been completed:
• Compilation of a risk management strategy policy document and approved by Exco. The policy document reflects Stats SA's commitment and approach to risk management.
• A plan to manage the risks is now being developed.
• The Internal Finance Control Section within the Finance Division has been reconstituted into a risk management unit, which will coordinate the risk management process under the control of the CFO.
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
29
The risk management policy defines the overall goals and acceptable procedures for risk management at Stats SA, and is supported by the following underlying principles:
• Acceptance that risk is an essential element of strategy.
• There are regulatory and user expectations of a demonstrable system of risk management to recognise and manage risk.
• Effective risk management provides greater assurance that the vision and strategy of Stats SA will be achieved without surprises.
• Each executive manager of a division is expressly responsible for managing the risks associated with its operational objectives.
• All material risks are to be identified, analysed, treated, monitored and reported in accordance with these policies and procedures.
• All divisions are to adhere to prudent risk management practices and minimum control standards.
• Risk is to be managed within the context of a consistent organisation-wide approach as outlined in the policy guidelines.
• Risk management action is to be tailored to the particular business circumstances.
• Management should regularly assess compliance with policies and practices and the state of risk management and control.
Stats SA will formally apply these processes in the following management activities:
• Strategic planning
• Business planning
• Project planning and management
• Monthly management and Exco reporting
• Assurance and internal audit reviews
• New contracts / supply chain management
• Major change initiatives or large business projects.
Although it is the responsibility of management to develop and drive the risk management process, Internal Audit will add value to the process by advising management during all phases of the process, as well as supplying a systematic and objective evaluation of the process on an ongoing basis to assist management in ensuring that risks to the organisation are well managed and controlled.
In the next issue of StatsNews the various types and objectives of audits will be outlined.
Naas Du PlessisInternal Audit
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
30
The Data Processing team in the Social Statistics division consists of three professionals – two who manage the processing phases and one programmer – and twenty-five data processors. Social Statistics conducts the labour force survey twice a year, and the general household survey once a year, both with a sample size of 30 000 dwelling units. There are also surveys that are conducted less frequently such as the yearly income and expenditure survey.
During the planning of the 2002 General household survey, it was decided that the data processors, because of their knowledge of the different household surveys, would be trained to do quality assurance of fieldwork during the enumeration period. This process also fitted in with the skills training programme. The data processors have since become a very important part of quality assurance, and are helpful in training fieldworkers.
Sybase database is used as the central database for these surveys. The software used for data processing is Visual Basic for capturing and updating, Eyes and Hands for scanning, and SAS for data editing. The programmer develops and maintains data capturing and updating systems. Scanning was introduced in 2000 and has been used since then as the primary means of capturing survey data. The scanning systems are developed and maintained by two data processors, together with the programmer.
Integration with Social Statistics: Development and output
The Data Processing team is actively involved in the design and development of the questionnaires in order to ensure that they are suitable for scanning. This team is also responsible for checking the 'blue print' (final proof) before printing starts.
Integration with Social Statistics: Field operations
Ten of the data processors attended the training of the national trainers for the labour force survey of September 2004. They then assisted with the training of fieldworkers in the nine provinces, and during the two weeks of fieldwork they conducted quality assurance. This involves checking completed questionnaires, and verifying that the correct dwelling units have been enumerated by means of control visits.
Integration changes data processing
Susan Steyn, Social Statistics
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
31
The involvement of the data processors in training and quality assurance for the survey has increased their understanding of the questions and the types of responses that are given. This has both empowered the data processors and contributed towards the quality of the final processed data.
Ongoing activities
The data processing for the various surveys has to be tightly managed if all the component's responsibilities are to be met. At present the LFS is released six months after fieldwork, while the GHS, which is conducted in July, is released early the following year. Work on the two surveys overlaps and meeting deadlines is critical. For example, this year, the fieldwork for the general household survey was conducted in July. Data processing commenced on 11 August 2004, and was then put on hold on 23 September 2004, when data processing of the September labour force survey started. This is planned to be completed by the end of November, after which the team will resume work on the GHS data. There is never a quiet moment in this crucial component in Stats SA.
Susan SteynSocial Statistics
Go
ve
rn
an
ce
,p
lan
nin
ga
nd
inte
gra
tio
n
32
The Primary Sector Statistics section, which is responsible for the collection and dissemination of agriculture, forestry, fishing, mining and quarrying statistics within Statistics South Africa (Stats SA), conducted a Census of Commercial Agriculture in 2002. This census was commissioned by the Department of Agriculture (DoA). The preliminary results were released in September this year and a comprehensive report is expected before the end of the year. The authority to collect and disseminate these statistics is conferred upon Stats SA through the Statistics Act of 1999.
Formal agriculture contributed roughly 4% to the Gross Domestic Product (GDP) in South Africa in 2002. However, its importance in terms of alleviating poverty is of great significance. Data in respect of surveys conducted in the agricultural sector are used by various stakeholders, and are designed to meet local and international standards and needs. Adherence to these standards requires that the information provided be consistent with internationally accepted definitions and conforms to specified timelines.
The two major objectives of the census were to evaluate the current situation in the formal agricultural sector and identify the needs of the industry. To do this information was collected from all commercial farming units on type of ownership, particulars of the farmer/ farm operator, financial statistics, products statistics and employment statistics.
For the purpose of the census, a commercial farm was defined as a farm that is registered for value-added tax (VAT).
Scope of the census
The census was designed to cover 80 718 VAT-registered farming units that were live and active when the snapshot was drawn from the Stats SA business frame. This business sampling frame covers all formal businesses registered for VAT,including those farming units that were VAT-registered at the time. Investigations on the initial 80 718 VAT-registered farming units indicated that 34 900 of these units were in fact inactive during the reference period. This was for various reasons, including liquidations, closures, take-overs, mergers, and farming units with zero gross farming income. This led to the original sample being adjusted to reflect the 45 818 active farming units within the country during the reference period.
Census of Commercial Agriculture 2002
Moses Mnyaka, Census of Agriculture
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
33
For the purposes of the census, farming means:
• the growing of field crops, fruit, grapes, nuts, seeds, trees in a nursery, bulbs, vegetables and flowers in the open air or under cover;
• operations at nurseries excluding those predominantly engaged in reselling;
• the operation of tea, coffee and sugar plantations;
• the raising of livestock, game, poultry, rabbits, freshwater fish, furbearing and other animals; and
• the production of milk, wool, pelts/fur, eggs and honey for commercial purposes.
Forestry, e.g. timber, wattle, wood and other plantations, ocean fishing and agricultural services were excluded from the census. Data in respect of agricultural smallholdings such as nurseries, vegetable gardens, flower farms and dairy farms are included in this report only in those cases where the products were intended for sale.
Challenges
Many and varied challenges were experienced during the project, which made collection of data difficult. Some farmers were reluctant to release financial information. Others were suspicious of government's intentions, in the light of land redistribution activities in neighbouring countries, and in the light of new laws affecting farmers and their employees. A long period had elapsed since the last census of agriculture (conducted in 1993) and overall, the purpose of the census and the potential benefit to farmers were not understood.
In addition to these attitudinal issues, a large proportion of questionnaires (55%) were sent to the offices of bookkeepers or accountants instead of to farmers themselves, and moreover it transpired that the census was not adequately publicised.
Stats SA then embarked on a campaign to inform the farmers about the census and its purpose, and to allay their fears. Delegates from Stats SA attended annual general meetings organised by agricultural unions at national and provincial level, and were given the opportunity to address audiences about the census. In addition, publicity was stepped up, in the form of media statements, pamphlets and brochures distributed to farmers, and radio interviews on talk shows and the like.
These efforts greatly enhanced the ability of fieldworkers to gain access to farms and gather the required information. As a result of the active support of organised agriculture, Stats SA was able to obtain an overall response rate of 62,7% of the adjusted frame of 45 818 farming units.
Key findings
According to the results of the Census of Agriculture there were 45 818 active commercial farming units in South Africa during the reference period 1 March 2001 to 28 February 2002. This is a decrease of 12 162 farming units since the last Census of Agriculture in 1993. As noted earlier, 34 900 commercial farming units were inactive during the period.
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
34
Even though there were fewer farming units, the gross farming income generated by these units was R53 billion compared with R39 billion in 1993 at 2002 constant prices.
The number of paid employees in formal agriculture decreased by 13,9% from 1 093 265 to 940 815 over the nine-year period. Of the nine provinces, Western Cape employed the largest number of paid employees (22,5% of the total), followed by Free State (13,1%), Mpumalanga (12,8%) and KwaZulu-Natal (12,4%).
Of the R53 billion generated from the formal agricultural sector, over R21 billion (40,1%) was generated from commercial farming in animals and animal products. In 1993, the share of gross farming income generated by formal agriculture from animals and animal products was 49,8%. Field crops and horticultural products were the second and third largest sources of income in the sector, with contributions of 31,1% and 26,8% respectively. The percentage contribution of field crops and horticultural products increased at the expense of the animals and animal products.
Total expenditure by the formal agricultural sector amounted to R42 billion. The ratio of farming debt to the market value of farming assets rose from 22,9% in 1993 to 31,3% in 2002.
Farming debt amounted to R31 billion. Western Cape was responsible for 23,7% of the farming debt, followed by Free State (16,9%) and Northern Cape (11,2%),while Gauteng had the lowest proportion of debt (3,4%).
Gross farming income and farming debt are illustrated in the figure shown here. Additional details of the results can be found in Statistical Release P1101 which is available on the Stats SA website.
0 2 000 4 000 6 000 8 000 10 000 12 000
Western Cape
Northern Cape
North West
Mpumalanga
Limpopo
KwaZulu-Natal
Gauteng
Free State
Eastern Cape
R million
Please note: This statistical release is a summary of selected findings. A detailed report will be published in December 2004.
Gross farming income
Farming debt
Gross farming income and farming debt by province for the financial year ended February 2002
Moses MnyakaPrimary Sector
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
35
In the last issue of StatsNews we reported on the Causes of Death project, which was undertaken to address the backlog in processing death notification forms (death certificates) received from the Department of Home Affairs. Forms for three of the six years, namely 1997, 1999 and 2001, have now been captured as part of a contingency plan to make the preliminary results available for analysts earlier. The remaining years were processed at the end of October 2004. Dividing the work in this way has provided a more useful interim picture of the data in terms of pattern distribution as well as trends over time.
Stats SA is now in the process of preparing an interim report based on the processed forms for 1997, 1999 and 2001. It is hoped to present the report to the government before the end of this year. Meanwhile this article provides more detail about the various processes involved in the determination of the underlying causes of death, which is the target information.
The first step in determining the underlying causes of death process is coding. The purpose of coding is to convert the diagnosis written on the death
The determination of underlying causes of death
Jean-Marie Hakimana and Naeema Jeewa, Causes of Death Project
notification form to a code as specified in the International Classification of Diseases (ICD10) coding book, which complies with the international standards as required by the WorldHealth Organisation (WHO).
In some developed countries, coding the (immediate) causes of death from the information provided on death certificates has evolved into a fully automated process. In South Africa, however,coding has always been manual. In this project, all codes allocated by coders are verified by a second person.
Once the codes have been captured and verified, the post-capture processes are implemented in sequence of operational events. However, the management of the flow ensures that any change in the database is centrally controlled through one master database (see diagram).
In addition to data capturing and data quality assurance managed through the “Causes of Death Capturing System”, an ad hoc module was developed to cater for the updating of inconsistencies detected by the editing system as
ICD10 coding bookCoders busy at work
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
36
Post-capture process flow
Datacapturing
Masterdatabase
SQI Export to ASCII
AutomatedEditing
Automatedallocation of unresolved
cases to unsp.
Automatedunderlyingcause of
deathderivation
Allocation of underlyingcause to
unit recordOutput
database
Manualverification
QA sampled batch
comparison
Boxcomparisonverification
Testtabulation
Errordecision to
correct
Manualediting
Manualcomparison
Manualcoding
Systemupdate
MetadataSX build
SX tables
CSpro/IMPStables
Derived variables or othercalculated indicators
(on request)
Report writing/analysis
Reject
Data QA
1st failure Pass
2nd failure
No
Yes
Yes
No No
Yes
Passed
Failed
Patternvariables
QA test
well as correction of cases rejected during the determination of the underlying causes of death.
The post-capture processes are as follows:
• Editing sub-process
• Determination of underlying causes of death
• Building an output database
• Tabulation and preliminary analysis.
The underlying causes of death are the most important variables for tabulation of causes of death. The reason for determining underlying causes of death is that mortality statistics should be seen as health information contributing to the prevention of death. That is why the immediate cause of death is not considered important for decision-making.
The procedures for selection of the underlying causes of death are based on the selection rules stipulated in the ICD10 books. These rules were
automatically derived using one module of the Mortality Medical Data System (MMDS), developed by the United States National Centre for Health Statistics. The module applies WorldHealth Organisation (WHO) rules to the ICD10 codes determined by coders and selects an underlying cause of death. The system has been successful for 98% of records. Only 2% of records are subject to manual determination of underlying causes of death because (a) they are unique South African codes (not in ICD10); (b) they are rare causes of death such as cholera and malaria; or (c) they are problematic due to the way the death notification form was filled in.
Once the underlying causes of death have been determined, these are uploaded into the output database and used for the tabulation of the results.
Jean-Marie Hakizimana and Naeema JeewaCauses of Death Project
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
37
Markets and the media often focus on economic statistics released by Statistics South Africa (Stats SA). While most media coverage focus is on economic indicators such as PPI, CPI and CPIX, published by Stats SA, population statistics are no less important in what they indicate and are used for. This is particularly important in the years between censuses and will become even more important now that South Africa has a ten-year census interval.
As part of its adherence to the specification of the International Monetory Fund's Special Data Dissemination Standard (SDDS), Stats SA publishes population estimates for the country as a whole and the nine provinces annually. Stats SA released the 2004 mid-year population estimates covering all the residents of South Africa in July 2004.
The mid-2004 population is estimated at approximately 46,6 million, an increase of approximately 1,8 million people from the October 2001 census figures. Fifty-one per cent (approximately 23,6 million) of the population are female. The provincial estimates show that KwaZulu-Natal has the largest proportion of the population (20,7%), followed by Gauteng (19,0%) and Eastern Cape (15,2%). The Northern Cape remains the province with the lowest proportion of the country's population (1,9%).
Although there are crude estimation methods such as inflating the total population at one date by an assumed overall annual growth rate, most serious estimation efforts use the cohort-component approach. In this approach, the population at the start date is disaggregated by sex and age. The initial population, in this case the census data, are projected forward through the application of age specific fertility, mortality and migration schedules. There are separate mortality schedules for males and females which are applied to each age group. The result is the population estimated by sex and age at some point in time, in this case the South African population at mid-2004.
From this estimate a number of useful things can be calculated, including the number of working people in the working ages. The estimates could also be used as the denominator to calculate the per capita GDP. The estimates imply that the per capita GDP should be slightly adjusted upwards as the population growth rate has declined slightly.
The estimates also suggest that while KwaZulu-Natal still remains the province with the largest proportion of the total population, positive net migration (the difference between those moving into a province and those leaving a province) will lead to substantial growth in Gauteng and Western
Population statistics is as important as economic statistics for a country
Eileen Phoshoko and Diego Iturralde, Demographic Analysis
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
38
Cape in the short term. For Gauteng, approximately 850 000 people are estimated to move to the province and approximately 350 000 are estimated to move out of the province between 2001 and 2006 resulting in net migration of approximately 495 000. For Western Cape, the estimated net migration for the same period is 225 000 people. KwaZulu-Natal is estimated to have a slightly negative net migration. These interprovincial shifts in the population imply that Gauteng might become the most populous province by 2011, when the next census is scheduled to takeplace. Furthermore, this shift in the population will have implications for parliamentary representation and other sectors of society.
There has also been much concern about the effect of HIV on fertility in South Africa. Evidence indicates that being HIV positive lowers the biological ability of women to have children. There are also arguments that behavioural changes among HIV-positive persons could lead to either lower or higher fertility. In South Africa fertility has been declining since the 1970s. Given that fertility in South Africa declined between the mid-1980s and the mid-1990s by an average of 15% per year, and further declined between 1996 and 2001, the estimates suggest that the overall impact of HIV on the level of fertility is unlikely to be larger than other factors influencing fertility in South Africa.
There has also been concern about the impact of HIV on mortality. It is estimated that the HIV-positive population in 2004 is approximately3,8 million, which relates to an HIV-prevalence rate of about 15% of the adult population. The accumulated Aids deaths up to 2004 were estimated at 1,5 million. It should also be noted that Stats SA's assumptions in relation to HIV/Aids may change with the completion of the study on the causes of death currently underway.
Diego Iturralde and Eileen PhoshokoDemographic Analysis
Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
39
In July 2003 the results of Census 2001 were launched with great ceremony. This entailed considerable hard work behind the scenes to prepare publications as quickly as possible
1once data processing was complete. Despite the anticipated release date being postponed more than once, the launch took place less than two years after the date of the census. This may seem quite a long time, but is in fact a considerable achievement by international standards.
A particular feature this time round has been the range of electronic census products, which enable users at all levels to access census data via the Stats SA website. Twoproducts in particular enable users to produce maps to illustrate particular findings, namely the Digital census atlas and My constituency: Ward profiles. The digital atlas was available on CD at the time of the launch, and a bound printed copy, running to 18 volumes, was presented to President Mbeki. The flagship printed product Census in brief was also available at the launch. Since then close on 15 000 copies have been distributed,
2and over 28 000 copies have been downloaded from the census website.
Other popular products on the website have been the urban/rural discussion document, which has been downloaded in full almost 2000 times, and in part another 1600 times, and the metadata section on the census questions relating to individuals, the 'Persons' section, which has been downloaded over 2000 times.
What many people do not realise is that work on census products and publications is ongoing. This year, 2004, has seen the publication of the Community profiles and the 10%sample, both electronic products. User-friendly software is disseminated with the Community profiles to allow users to do their own cross-tabulations at different geographical levels down to sub-place level. The 10% sample on the other hand is targeted at researchers who need to do in-depth analyses on the data.
On the methodological side, the report of the Post-enumeration survey was published early in the year (hard copy and web), and the Computer editing specifications wasfinalised and released on the web.
Meanwhile publication of the Primary tables began in the middle of this year. The national volume and three provincial volumes are already available, and it is planned to release one volume a month until all provinces are completed. This product contains Census 1996 tables, Census 2001 tables, a comparative graph, and comparative text, for
Second round of census products
Mary Hazelton, Publishing
1 A description of the data processing phase of the census may be found in Census 2001: How the count
was done (Report no. 03-03-02 (2001)) (hard copy or via the web), and full details of the computer edits applied to the raw data are available in Census 2001: Computer editing specifications (Report no. 03-02-43 (2001) (web only). 2 All figures for products downloaded from the census website refer to the period 1 October 2003 to 31 August 2004.P
rod
uc
ta
lig
nm
en
tto
me
et
use
rn
ee
ds
40
Census products released subsequent to the launch
each of 23 variables, broken down by population group and sex. The process involves four software packages (Supercross, Excel, Word, and Harvard Graphics) and considerable logistical management to ensure the correct data are used for each comparison, and that everything goes through the stipulated checks.
Another useful printed product published recently is the Key municipal data. This volume contains one page of tables for each of the 284 municipalities in the country (metros, district municipalities, local municipalities and district management areas). This quick reference volume gives population figures, and education, labour market, transport and household services data for each municipality.
Both the Primary tables and the Key municipal data are primarily designed as reference volumes for those without the means to use the interactive web products. Moreover the Key municipal data volume has spiral binding, to facilitate, indeed encourage, the copying of individual pages. Copies will be distributed to libraries and other central places.
The following products are also in the pipeline:
• a comprehensive analytical report of the census results, with numerous cross-tabulations and breakdowns not as yet published;
• a full-colour report on disability with graphs and photographs;
• four monographs on individual topics: mortality, fertility, migration and gender;
• a profile on each province, currently being prepared by authors in the provinces; and
• a revised version of the Community Profiles.
Finally a book is in preparation in collaboration with the Human Sciences Research Council, entitled Census users' handbook. Modelled on a British publication, this book comprises chapters from different authors on various aspects of the census-taking process and the census results.
Who thought Census 2001 was behind us? Not me.
Mary HazeltonPublishing Pro
du
ct
alig
nm
en
tto
me
et
use
rn
ee
ds
41
The National Statistics System (NSS) has come a long way since its inception in 2002. Since then most energy has been spent on developing the system, which involved building concepts and compiling concept documents such as the founding document on the role of the NSS as an integrative framework for information, government policy and planning, submitted to the January 2002 Cabinet Lekgotla. System development has also involved developing a template for monitoring and evaluation for individual departments, formulating a certification framework for departments that wish to have their statistics designated as official by the Statistician-General, and mapping out an institutional framework for the NSS. Roles, responsibilities and deliverables have also been identified. Another major aspect of development has been the advocacy programme on the use of statistics for measuring performance, planning and decision-making; importance of the NSS; and the role of Statistics South Africa (Stats SA) as the leader of official statistics. Advocacy has also been achieved through provision of technical support to departments by Stats SA.
During 2004, the NSS has made the transition from system development to system implementation. The approach has been to start small, with three or so departments, and incrementally expand the system over time. This has already happened, with the Department of Science and Technology and the Council for Scientific and Industrial Research (CSIR) formally signing memoranda of understanding with the Statistician-General.
System implementation has been strongly supported by the World Bank's Trust Fund for Statistical Capacity Building (TFSCB), which has enabled visits by World Bank missions to support statistical capacity in selected departments and to develop recommendations for organisational development of the NSS countrywide. The mission report on capacity assessment by Gösta Guteland and Mary Strode, World Bank consultants, has been finalised and is available at Stats SA. The next mission to advise on organisational development is currently underway. Mary Strode, Gösta Guteland and Bill McLennan (also a World Bank consultant) are undertaking it.
Because Stats SA has the mandate for official statistics, it is also responsible for the Millennium Development Goals (MDGs). Associated with the 8 MDGs are 18 targets and 48 indicators. These form part of the Millennium Declaration agreed to in 2000 by the United Nations General Assembly. In fact, South Africa is a signatory of the Millennium Declaration.
The Social Cluster, to which progress made on the MDGs is reported, has constituted the MDG Country Team which held its maiden meeting on 22 September 2004. During the meeting the Country Team was provided with information on the MDG process. The MDG process was activated in earnest
National Statistics System
Akiiki Kahimbaara, NSS
Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
42
Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
Cabinet
Social Cluster
Foreign Affairs
UN
Stats SA
Task team
Country team
Leadagencies
Govtdepts
Parastatals
CSOs
Privatesector
MDG process in South Africa
towards the end of 2003. The Department of Foreign Affairs has overall responsibility for the process. Stats SA, the statutory agency responsible for official statistics, is the lead agency responsible for managing the rollout of the process as well as for providing a secretariat for MDG activities. Stats SA reports to the Social Cluster in the Department of the Presidency. The Social Cluster in turn reports to Cabinet. Cabinet will give final approval to the MDG Country Report for the Department of Foreign Affairs to submit to the United Nations. The process is schematically presented below.
A Country Team has been constituted and its composition demonstrates the seriousness with which the government regards MDGs. The Country Teamconsists of representatives from all government departments and parastatalorganisations, a selection of civil society organisations and the private sector. Institutions directly nominate their own representatives. The UN agencies, and especially the UNDP, provide technical support, advice on the process, and may also help out financially. However, technically they are not part of the Country Team, the reason being emphasis on country ownership of the process. Considering the rather large size of the Country Team, the meeting constituted a smaller task team to do the actual spadework involving such activities as sourcing data from the diverse producers of national statistics in the country. The task team is to meet in the first half of October to put together a work programme for the MDG process.
The MDG process is expected to yield four products. The main one is the MDG Country Report. The second product is an MDG Stakeholder ConsultationsReport, elements of which will feed into the country report. This report will largely be informed by civil society organisations. The third product will be a Synthesis Report of the MDG Challenge Strategy, which will result from a synthesis of eight research competitions by academics on the best way to achieve the MDGs. The last product will be a compilation of selected illustrations from an art competition by primary school learners, the best of which will illustrate the cover of the country report.
43
The NSS provides a perfect framework for MDGs. The relevance of the NSS to the MDG process is best illustrated by outlining the key deliverables of the NSS. One of products of the NSS is a compendium of national development indicators to enable government to monitor its own performance, to plan, to evaluate its plans and to make evidence-based decisions. MDG indicators fit well into the compendium. Specifically the NSS is tasked with creating a strong information base for good governance and service delivery in a developmental (or interventionist) state. In practice this involves providing support to the government-wide M&E framework with development indicators, including MDGs.
The second product is a Management System for Statistical Information (MSSI) whose rationale is for the NSS to provide a one-stop shop for development indicators. At the moment Stats SA has successfully piloted an indicator dissemination tool, the Second Generation Live Database, sponsored by the World Bank. The same one-stop shop will serve dissemination of MDG indicators.
The third product is development of standards within the framework of international good practice. Standards consist of concepts, definitions, classifications, frames for surveys (geography and registers), methodologies, protocols, codes of conduct (rules of engagement), etc. The NSS Division has driven the development phase of statistical capacity building in the country with the support of the TFSCB of the World Bank. The sub-phases of the development phase consist of a statistical audit of selected departments, organisational development, a Statistical Master Plan, and a capacity building framework, which includes MDGs.
The MDG process has an integrative function. In this respect the first meeting of the Country Team recommended pooling together other national processes of a similar nature into a single process. Accordingly, there is to be one combined process for MDGs, World Summit for Social Development (Copenhagen plus Ten) and the World Summit for Sustainable Development (Johannesburg plus Two). The Country Team will serve as one reference group for all these processes.
Implementation of the MDG process has taken off in earnest, in terms of building capacity, holding stakeholder consultations to feed into the process, and processing indicator values. Capacity building has already been implemented. It is of two types – empowering individuals to undertake data processing to generate values for indicators for the Country Report, and empowering communities to interrogate and at the same time inform the MDG process. In the case of the empowerment of individuals, UNICEF has facilitated training on the DevInfo software (for generating MDG and other indicators from databases) both regionally (in Nairobi last August) and nationally (in Pretoria early September).
Empowering communities both to interrogate and to inform the MDG process involves stakeholder consultations, and especially civil society organisations. The first consultative meeting was held at the end of July 2003, even before the Country Team had formally met.
Akiiki J KahimbaaraNational Statistics System
Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
44
Integration in SADC depends largely on the information systems they use. The first project towards harmonisation was the population census, in particular the 2000 round of censuses where South Africa surved as a focal point. More work has been done since and recently Statistics South Africa hosted a seminar between 27 September and 1 October 2004 to discusss progress on the harmonisation of the consumer price indices of SADC member states. This seminar followed from workshops and seminars held in the past. The main aim of the seminar was for member states to share experiences on project activities and to develop methods for the compilation of a harmonised CPI to be applied by all member states in future.
The Stats SA delegation, headed by Desmond Booysen, comprised mainly staff from Prices.
Towards a harmonised Consumer Price Index (HCPI)
Harry Thema, Prices
Left to right: Glorinah Thoabala, Harry Thema, Eugene Kgantsi, Desmond Booysen and Sebentile Dlamini
The following points were discussed:
• Presentation by each country of a correspondence table of headings and products in their national CPI to the United Nations' classification of individual consumption by purpose (UN-COICOP)
• Adjusting/adopting weights from national CPIs to SADC HCPI
• Harmonised CPI computation procedures at both elementary and higher levels
• Possibilities of items substitutions and inclusion of new products in the harmonised CPI
• Quality control measures in harmonised CPI compilation
• Computer laboratory exercises for harmonised CPI compilation – covering all the above issues Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
45
• Review on HCPI price data collection experience gained by member states
• Discussion and finalisation of content of data that will be required by the SADC Secretariat for the compilation of consolidated indices for the region
• Discussion on work programme and timetable
• Agreement on deadlines for submission of required data to the SADC Secretariat.
The process started in the late 1990s when member states decided to harmonise their consumer price indices. The harmonisation of methods in the compilation of these methodologies will enable a comparison across member countries. This programme is part of a number of political, economic and financial initiatives aimed at contributing to an accelerated regional integration of SADC member countries and their integration into the world economy. Reliable national and regional statistical information systems providing a minimum necessary set of internationally comparable statistics in major fields are a substantial part of the process. Harmonised instruments based on best methods and practices for statistical production will contribute to improved monitoring of the trade protocol, inflation management and poverty assessments within the region. The assistance programme for the construction of a sustainable regional CPI based on reliable national comparable CPIs is a key element not only within the development of national statistics systems, but also in the regional capacity for compilation of relevant statistical information.
The project was planned to be undertaken in three phases. Phase one involved holding a sensitisation workshop in January 1998 in Gaborone, Botswana to inform SADC about the UEMOA project (EDF funded regional programme for harmonisation of CPI within the member countries of the Union Economique et Monétaire Ouest Africaine) and determine whether SADC wished to harmonise price indices and to what extent and whether member states would benefit from the UEMOA experience.
Phase two was a formal assessment of member states' prevailing price index systems. This assessment was undertaken to determine existing methodologies and the preparation of a working plan leading to the production of regionally comparable indices. This phase was concluded by an end-of-phase workshop bringing together all SADC member states (except the Democratic Republic of Congo), the SADC Secretariat, the regional advisor at the EU delegation in Botswana, Eurostat and the consultant entrusted with the assessment for validation of the findings of the study. The study was undertaken in 1999, the end-of-phase workshop was held in March 2000.
The final phase is the implementation of the project, and was planned to run for three years. It was aimed at developing and implementing capacity for compilation of harmonised regional price indices. The overall objective of this phase was to accelerate the process of regional integration within SADC, to improve the quality and comparability of relevant statistical instruments at both national and regional levels for planning and monitoring of inflation, and poverty alleviation.
The activities of the project involve:
• Supporting national statistics offices (NSOs) in the design and analysis of national Household Budget Surveys (HBS) or Household Income and Expenditure Surveys (HIES) to ensure that agreed areas of comparability are fully implemented
Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
46
• Supporting NSOs in the implementation of methods and practices of the CPI designed to meet the requirements of comparability of the national indices for the SADC region, according to the recommendations accepted by member states
• Advising and assisting NSOs in compiling national CPIs in accordance with the recommendations as accepted by the countries
• Reviewing the quality and compatibility of other price indices in member states
• Identifying the needs of the member states at the different stages of the project so as to request the relevant expertise to be mobilised
• Supporting NSOs in the calculation of Purchasing Power Parities (PPPs) and real volumes of household expenditures, based on inter-spatial comparisons
• Designing and implementing at the SADC Secretariat a sustainable information system for the compilation, analysis and dissemination of monthly national and regional SADC CPIs, periodic calculation of PPPs, and other economic statistics as decided by member states
• Preparing analytical papers and assisting with the organisation, conducting and management of workshops and seminars to be organised during the life of the project
• Supporting NSOs in appropriate price collections to enable calculation of national PPPs as a basis for calculating real household incomes and the real GDP
• Providing and/or mobilising appropriate technical support for NSOs, to strengthen and make more comparable a range of economic statistics such as industrial statistics, the informal sector and national accounts.
• Provide advice to NSOs on household surveys and their use in the measurement of poverty and estimation of the GDP.
The main resolution of the seminar was determining the SADC HCPI basket, which encompasses the needs of most users for our intended product. Some products and services still need to be assigned the units of measure and proposition of computational methodologies. The basket will be applied as from January 2005, for the interim HCPI compilation after the slight tuning of the units.
It was a great honour for the seminar participants to have in their midst the Statistician-general, Mr Pali Lehohla, who officially closed the ceremony.Amongst important issues he raised was the importance of having a relevant, reliable and timeous CPI and HCPI, and a link between this project and other projects in the SADC region.
Dr Zakayo Msokwa (SADC Project Manager) and Pali Lehohla
Harry ThemaPrices Me
eti
ng
na
tio
na
la
nd
inte
rn
ati
on
alo
blig
ati
on
s
47
season's greetings
The Statistician-General, Pali Lehohla,
and the StatsNews team
wish you a happy festive season
and a fruitful and prosperous 2005.
48
![ROBUST ADAPTIVE BEAMFORMER WITH · PDF filebust adaptive beamforming, ... strained adaptive beamformer is studied in [5, 6] and widely used thereafter. Recently some interesting robust](https://img.pdfslide.us/doc/110x75/5ab383fc7f8b9ad9788e2684/robust-adaptive-beamformer-with-adaptive-beamforming-strained-adaptive.jpg)
