STATISTICAL ORGANIZATION FOR POVERTY MONITORINGSTATISTICAL ORGANIZATION FOR POVERTY MONITORING

Prof. Ben KiregyeraProf. Ben KiregyeraPARIS21 Consultant and Chairman, Uganda Bureau of PARIS21 Consultant and Chairman, Uganda Bureau of

StatisticsStatistics

WORKSHOP ON WORKSHOP ON MONITORING MONITORING

DEVELOPMENT AND INDICATORS DEVELOPMENT AND INDICATORS

CAPE TOWN, 3 – 6 APRIL,CAPE TOWN, 3 – 6 APRIL, 2002 2002

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1. The Scourge of Poverty1. The Scourge of Poverty

2.2. The need for Information to The need for Information to

Inform Inform Poverty Poverty

Monitoring ProcessesMonitoring Processes

3.3. Statistical Organization for Statistical Organization for

Poverty Poverty MonitoringMonitoring

1. The Scourge of Poverty1. The Scourge of Poverty

2.2. The need for Information to The need for Information to

Inform Inform Poverty Poverty

Monitoring ProcessesMonitoring Processes

3.3. Statistical Organization for Statistical Organization for

Poverty Poverty MonitoringMonitoring

00.. ScopeScope

• Scourge of PovertyScourge of Poverty

Globally: *** 1 in 5 live on < $ 1 a dayGlobally: *** 1 in 5 live on < $ 1 a day

*** 1 in 7 suffers chronic hunger*** 1 in 7 suffers chronic hunger

*** 150 million underweight *** 150 million underweight childrenchildren

Africa (All): *** 44 % of pop. live on <$39 p.mAfrica (All): *** 44 % of pop. live on <$39 p.m

North Africa: *** 22 % of pop. live on < North Africa: *** 22 % of pop. live on < $54 p.m$54 p.m

Sub-Saharan Africa: *** 51% of pop. live on Sub-Saharan Africa: *** 51% of pop. live on <$34 p.m<$34 p.m

• Scourge of PovertyScourge of Poverty

Globally: *** 1 in 5 live on < $ 1 a dayGlobally: *** 1 in 5 live on < $ 1 a day

*** 1 in 7 suffers chronic hunger*** 1 in 7 suffers chronic hunger

*** 150 million underweight *** 150 million underweight childrenchildren

Africa (All): *** 44 % of pop. live on <$39 p.mAfrica (All): *** 44 % of pop. live on <$39 p.m

North Africa: *** 22 % of pop. live on < North Africa: *** 22 % of pop. live on < $54 p.m$54 p.m

Sub-Saharan Africa: *** 51% of pop. live on Sub-Saharan Africa: *** 51% of pop. live on <$34 p.m<$34 p.m

I.I. IntroductionIntroduction2

• Millennium Development Goals (MDGs)Millennium Development Goals (MDGs) 8 goals 8 goals Eradication of extreme poverty and hunger is greatestEradication of extreme poverty and hunger is greatest development challengedevelopment challenge

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• Need for wide range of InformationNeed for wide range of Information

Profile of the poor who are the poor? where are they? how many are they? what is 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?

• PlannersPlanners• Policy makersPolicy makers• Decision-makersDecision-makers• OthersOthers

2. 2. Information for Poverty Monitoring Information for Poverty Monitoring ProcessesProcesses

42. 2. Information for Poverty Monitoring Information for Poverty Monitoring Processes (ctd)Processes (ctd)

SupplySupplyof good of good

informationinformationDemand for good

information

• Demand versus Supply ofDemand versus Supply of

InformationInformation

• Taxonomy of InformationTaxonomy of Information

quantitativequantitative

qualitativequalitative

combination – take advantage of complementaritiescombination – take advantage of complementarities

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• Main sources of informationMain sources of information

Management Information SystemsManagement Information Systems

HealthHealth EducationEducation AgricultureAgriculture OtherOther

Sample surveysSample surveys

Household Budget SurveyHousehold Budget Survey Demographic and Health SurveyDemographic and Health Survey Agricultural SurveyAgricultural Survey

CensusesCensuses

Population and Housing CensusPopulation and Housing Census Agricultural CensusAgricultural Census School CensusSchool Census

Participatory poverty assessmentsParticipatory poverty assessments

2. 2. Information for Poverty Monitoring Information for Poverty Monitoring Processes (ctd)Processes (ctd)

3. 3. Statistical Organization for Poverty Statistical Organization for Poverty MonitoringMonitoring

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Main Stakeholders

Data UsersData Users

Data Collectors

Data Collectors

Data suppliers

Data suppliers

Govt., researchers, public & private sector, NGOs, donors, international organizations, press, public

Govt., researchers, public & private sector, NGOs, donors, international organizations, press, public

NSO, line Ministries, public sector, NGOs, etc.NSO, line Ministries, public sector, NGOs, etc.

Households, farmers, establishments, institutions, etc.Households, farmers, establishments, institutions, etc.

• Statistical Organization

• Enabling legislation

• Some Weaknesses of the National Statistical Systems in AfricaWeaknesses of the National Statistical Systems in Africa

limited political commitment limited political commitment o promoting use of datapromoting use of data

o demanding and using datademanding and using datao funding data production (data production expensive)funding data production (data production expensive)

insufficient data user/producer dialogueinsufficient data user/producer dialogueo usually one-off workshopsusually one-off workshops

o informal, ad hoc and not institutionalisedinformal, ad hoc and not institutionalisedo supply and/or donor driven systemssupply and/or donor driven systemso priorities for data production not determinedpriorities for data production not determinedo paradox of data gaps/over-supply of some dataparadox of data gaps/over-supply of some data

limited coordination limited coordination o user/produceruser/producer

o producer-producerproducer-producero producer/research/training institutionsproducer/research/training institutions

data quality problems (data quality problems (inconsistency, incompleteness,inconsistency, incompleteness,

inaccuracy, lack of timeliness, insufficient disaggregationinaccuracy, lack of timeliness, insufficient disaggregation))

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3.3. Enhancing of relevance and Enhancing of relevance and

effectiveness of effectiveness of statistical organization for poverty statistical organization for poverty

monitoringmonitoring• Advocacy for statistics Advocacy for statistics

raise awareness about and create demandraise awareness about and create demand raise profile of statisticsraise profile of statistics resource mobilizationresource mobilization

• Keeping policy, decision makers and other Keeping policy, decision makers and other stakeholdersstakeholders in loopin 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

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3.3. Enhancing of relevance and Enhancing of relevance and

effectiveness of effectiveness of statistical organization for poverty statistical organization for poverty

monitoring (ctd)monitoring (ctd)

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• Designing National Statistical Master Plans Designing National Statistical Master Plans paradigm shift – ad hoc/piece-meal to paradigm shift – ad hoc/piece-meal to holistic approachholistic approach National Statistical Master PlanNational Statistical Master Plano road map for coordinating and developing road map for coordinating and developing a NSSa NSSo mechanism for harnessing critical mass of mechanism for harnessing critical mass of resourcesresourceso basis for the Planbasis for the Plan

critical assessment of existing data critical assessment of existing data gapsgaps identification and prioritisation of identification and prioritisation of data needsdata needs identification of required resourcesidentification of required resources activities to be undertakenactivities to be undertaken outputs to be producedoutputs to be produced expected outcomes and effectsexpected outcomes and effects

o user focus, synergy, efficiency and user focus, synergy, efficiency and effectivenesseffectivenesso SMART ( SMART ( SSpecific, pecific, MMeasurable, easurable, AAchievable, chievable, RRelevant and elevant and TTime bound)ime bound)

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WINDOW IWINDOW I

APPROACHQuick fixad hoc surveys /censuses

INPUTS

OUTPUTS

Largely donor-driven

Limited govt. contribution and ownership• data which are inadequate

• serious data gaps

• multiple databases

• unsustainable agric. Stat. systems

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WINDOW II

Coordinated System

• Identify Partners

• Integrated Framework – Strategic

Plan

Main Feature

user drivenlong-termpartnershipsprioritized

Inputs

Outputs

governmentDonor (optional)

adequate data networked databases sustainable system

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Statssa

Other data producers

Research/Training Organs.

Main producers

• government (s)• public/private sector• NGOs• research/training orgs.• donors/international orgs.• press• wider public

Partnerships

• Improving Coordination, Collaboration and Networking for Statistics

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• Enhancement of data qualityEnhancement of data quality

ConsistencyConsistency - improved coordination - improved coordination

- system-wide adoption/standardization - system-wide adoption/standardization of of

concepts, definitions, classifications concepts, definitions, classifications ((Uganda’s Example - CompendiumUganda’s Example - Compendium))

Completeness Completeness - comprehensive programme (Master - comprehensive programme (Master Plan)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

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disaggregated datadisaggregated data

- increase sample size (not viable option)- increase sample size (not viable option)- combine data from surveys with data from Pop. & - combine data from surveys with data from Pop. &

Housing CensusHousing Census

- community-based information systems - community-based information systems (community(community owned, managed and used)owned, managed and used)

• Improved data analysisImproved data analysis- data cycle - data cycle

planning, collection, planning, collection, processing/analysis/disseminationprocessing/analysis/dissemination

- need to improve analytical capabilities (NSOs)- need to improve analytical capabilities (NSOs)- relying on other institutions/experts- relying on other institutions/experts

Examples Examples

Institute of Economic and Social Research (Institute of Economic and Social Research (ZambiaZambia) ) – agricultural – agricultural sector performance analysissector performance analysis

Economic Policy Research Centre, Poverty Analysis Economic Policy Research Centre, Poverty Analysis and Monitoring and Monitoring Unit, Department of Gender (Unit, Department of Gender (Uganda)Uganda)

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Data Producers

End Data Users

Data and Information Data and Information

Raw Data(low level

information)

DataAnalysis

Information

IntermediateUser

Add value to data

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Raw Data

Tables

Basic Analysis

Policy-related Analysis Policy

Data analysis Data analysis

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- New analytical products using Geographical Information System (GIS) New analytical products using Geographical Information System (GIS)

functionality (functionality (vulnerability and poverty mapsvulnerability and poverty maps)/ Statistics South Africa)/ Statistics South Africa

• Improved dissemination and data accessImproved dissemination and data access

- information has no value unless it:- information has no value unless it:** reaches those who need it** reaches those who need it** is easily understood** is easily understood** is actually used** is actually used

- dissemination programme- dissemination programme** provide needed information, form and frequency** provide needed information, form and frequency** user-friendly manner (users should understand the story)** user-friendly manner (users should understand the story)** provide ** provide metadatametadata

- dissemination media- dissemination media** publication of statistical reports** publication of statistical reports** press releases** press releases** circulation of tables (in advance of reports) ** circulation of tables (in advance of reports) ** electronic media, including internet** electronic media, including internet

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- 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

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UGANDA’S NATIONAL DATABANK

National Databank

The Internet

Health EducationAgric.

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Data Users Sub-systems

Other

Censuses and surveys

District Databanks

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Thank YouThank You

END