STATISTICAL ORGANIZATION FOR POVERTY MONITORING Prof. Ben Kiregyera

1. WORKSHOP ON MONITORING DEVELOPMENT AND INDICATORS CAPE TOWN, 3 – 6 APRIL, 2002 STATISTICAL ORGANIZATION FOR POVERTY MONITORING Prof. Ben Kiregyera PARIS21 Consultant and Chairman, Uganda Bureau of Statistics

2. 0. Scope 1 1. The Scourge of Poverty 2. The need for Information to Inform Poverty Monitoring Processes 3. Statistical Organization for Poverty Monitoring

3. 2 I. Introduction • Scourge of Poverty • Globally: *** 1 in 5 live on < $ 1 a day • *** 1 in 7 suffers chronic hunger • *** 150 million underweight children • Africa (All): *** 44 % of pop. live on <$39 p.m • North Africa: *** 22 % of pop. live on < $54 p.m • Sub-Saharan Africa: *** 51% of pop. live on <$34 p.m • Millennium Development Goals (MDGs) • 8 goals • Eradication of extreme poverty and hunger is greatest development challenge

4. 2. Information for Poverty Monitoring Processes 3 • Need for wide range of Information • Profile of the poor • who are the poor? • where are they? • how many are they? • whatis severity of poverty? • Causes of poverty • factors that cause poverty • relations among the factors • Which policy, strategy or decision? • alternatives • Changes in levels/ depth of poverty • Are policies/actions having effect? • Planners • Policy makers • Decision-makers • Others

5. 2. Information for Poverty Monitoring Processes (ctd) 4 • Demand versus Supply of • Information Supply of good information Demand for good information • Taxonomy of Information • quantitative • qualitative • combination – take advantage of complementarities

6. 2. Information for Poverty Monitoring Processes (ctd) 6 • Main sources of information • Management Information Systems • Health • Education • Agriculture • Other • Sample surveys • Household Budget Survey • Demographic and Health Survey • Agricultural Survey • Censuses • Population and Housing Census • Agricultural Census • School Census • Participatory poverty assessments

7. 3. Statistical Organization for Poverty Monitoring 7 • Statistical Organization Main Stakeholders Govt., researchers, public & private sector, NGOs, donors, international organizations, press, public Data Users Data Collectors NSO, line Ministries, public sector, NGOs, etc. Data suppliers Households, farmers, establishments, institutions, etc. • Enabling legislation

8. 8 • SomeWeaknesses of the National Statistical Systems in Africa • limited political commitment • promoting use of data • demanding and using data • funding data production (data production expensive) • insufficient data user/producer dialogue • usually one-off workshops • informal, ad hoc and not institutionalised • supply and/or donor driven systems • priorities for data production not determined • paradox of data gaps/over-supply of some data • limited coordination • user/producer • producer-producer • producer/research/training institutions • data quality problems (inconsistency, incompleteness, inaccuracy, lack of timeliness, insufficient disaggregation)

9. 9 • Enhancing of relevance and effectiveness of • statistical organization for poverty monitoring • Advocacy for statistics • raise awareness about and create demand • raise profile of statistics • resource mobilization • Keeping policy, decision makers and other stakeholders • in loop • create partnerships for statistics • stakeholders to take ownership • increase relevance and funding for NSS • make national statistics demand-driven • mechanism of User-producer Committees

10. 10 • Enhancing of relevance and effectiveness of • statistical organization for poverty monitoring (ctd) • Designing National Statistical Master Plans • paradigm shift – ad hoc/piece-meal to holistic approach • National Statistical Master Plan • road map for coordinating and developing a NSS • mechanism for harnessing critical mass of resources • basis for the Plan • critical assessment of existing data gaps • identification and prioritisation of data needs • identification of required resources • activities to be undertaken • outputs to be produced • expected outcomes and effects • user focus, synergy, efficiency and effectiveness • SMART ( Specific, Measurable, Achievable, Relevant and • Time bound)

11. WINDOW I 11 Quick fix ad hoc surveys /censuses APPROACH Largely donor-driven Limited govt. contribution and ownership INPUTS • data which are inadequate • serious data gaps • multiple databases • unsustainable agric. Stat. systems OUTPUTS

12. 12 WINDOW II • Identify Partners • Integrated Framework – Strategic • Plan Coordinated System • user driven • long-term • partnerships • prioritized Main Feature • government • Donor (optional) Inputs • adequate data • networked databases • sustainable system Outputs

13. 13 • Improving Coordination, Collaboration and Networking • for Statistics National Statistical System DATA PRODUCERS Main producers DATA USERS Other data producers • government (s) • public/private sector • NGOs • research/training orgs. • donors/international orgs. • press • wider public Statssa Research/Training Organs. Partnerships

14. 14 • Enhancement of data quality • Consistency - improved coordination • - system-wide adoption/standardization of • concepts, definitions, classifications • (Uganda’s Example - Compendium) • Completeness - comprehensive programme (Master Plan) • Accuracy - use of “best methods” • - human resources development • - proper handling of data in post-enumeration • period • - need for adaptation/research/experimentation • - UNSD’s Web site on “Good Practices in Official • Statistics” • Timeliness - release calendar and sticking to it

15. 15 • disaggregated data • - increase sample size (not viable option) • - combine data from surveys with data from Pop. & • Housing Census • - community-based information systems (community • owned, managed and used) • Improved data analysis • - data cycle • planning, collection, processing/analysis/dissemination • - need to improve analytical capabilities (NSOs) • - relying on other institutions/experts • Examples • Institute of Economic and Social Research (Zambia) – agricultural sector performance analysis • Economic Policy Research Centre, Poverty Analysis and Monitoring Unit, Department of Gender (Uganda)

16. 16 Data and Information End Data Users Data Producers Intermediate User Raw Data (low level information) Data Analysis Information Add value to data

17. 17 Data analysis Policy Policy-related Analysis Basic Analysis Tables Raw Data

18. 18 • New analytical products using Geographical Information System (GIS) • functionality (vulnerability and poverty maps)/ Statistics South Africa • Improved dissemination and data access • - information has no value unless it: • ** reaches those who need it • ** is easily understood • ** is actually used • - dissemination programme • ** provide needed information, form and frequency • ** user-friendly manner (users should understand the story) • ** provide metadata • - dissemination media • ** publication of statistical reports • ** press releases • ** circulation of tables (in advance of reports) • ** electronic media, including internet

19. 19 • networking and sharing of information • ** better data management including building • >>> electronic database • >>> data warehousing • >>> data mining • ** cutting-edge World Bank Live Database • ** National Databank in Uganda

20. 20 UGANDA’S NATIONAL DATABANK --------- Health Agric. Education Other Data Users Sub-systems The Internet District Databanks National Databank Censuses and surveys

21. Thank You END