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Census Micro Data A Discussion Prof. Ben Kiregyera Director African Centre for Statistics

This discussion led by Prof. Ben Kiregyera highlights the importance of data and information in development, emphasizing the need for detailed analysis to inform policy decisions. The presentation also addresses challenges in data management and recommends actions for improving data quality and accessibility.

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Census Micro Data A Discussion Prof. Ben Kiregyera Director African Centre for Statistics

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  1. Census Micro Data A Discussion Prof. Ben Kiregyera Director African Centre for Statistics August 2007

  2. COVERAGE • COVERAGE • Value of data and information • Issues arising from presentations • Conclusion

  3. I. VALUE OF DATA AND INFORMATION • data and information now universally recognized as: • part of the enabling environment for • development • a priority for results-based agenda • (PRSPs, MDGs, etc) (e.g. Marrakech • Roundtable, 2004) • data and information have nointrinsic value – they are not gold or silver. They have extrinsic valuewhich lies in their power to inform processes e.g. policy debate & design, planning, monitoring, etc. • their value lies in fact that: • they can reach those who need them, • can be easily understood • are usable and actually used

  4. LIS-based research has catalyzed changes in national policies (LIS paper)

  5. DATA CYCLE Planning Stage 1 Stage 2 Implementation Dissemination Feedback Stage 3 Reporting Processing Analysis/Interpretation

  6. More and more data • Need information from this • these data

  7. information Policy/ decision- maker Policy-related Analysis Basic Analysis Tables Raw Data Policy-related information

  8. Data, Information, Knowledge, Actions Informed Actions/ decisions Knowledge Value addition Information Data

  9. Who should do data analysis? • Preliminary or general analysis • statisticians/data producers (NSO) • Definitive or in-depth or detailed analysis • subject-matter specialists • researchers • Importance of involving subject-matter specialists and • researchers in analysis • enriches analysis by adding subject-matter perspectives • possibility to link policy variables to micro-level outcomes (paper on LIS) • enhances collaboration (advancing from coordination to collaboration) • spreads ownership of statistical products • feedback and advocacy

  10. Basic principle of data analysis Once Data Collection Feedback Many times Data Analysis • NSO • Policy analysts • Researchers • Students • etc.

  11. II. ISSUES ARISING FROM PRESENTATIONS All presentations make a case for enhancing data management including: Country level – we see great need for: • data archiving (IPUMS-International) • creation of user-friendly and accessible databases (documentation crucial - metadata) – Benin’s “Jupiter” • ensuring that databases are not empty boxes or have “garbage” • encouraging researchers to do more detailed data analysis (Benin example shows need for this) • a lot of value in doing broad range of analysis (through space and time)

  12. ISSUES ARISING FROM PRESENTATIONS (ctd) Challenges: i) appreciation of value of data (by data producers and users) Users: use data for evidence-based policy (debate, design) & decision-making; invest more to build statistical capacity & development Producers: ensure data quality & relevance; better data management inconsistencies through time caused by changes in definitions(1992 & 2002 Benin censuses) - special attention to harmonization to permit comparability technical capacity (Benin paper – mutually beneficial TA) Statistics Acts which are not congenial Other laws (e.g. designating areas as rural or urban)

  13. International level – we see increasingly: • construction of cross-national micro databases to enhance research infrastructure • Integrated European Census Microdata (IECM) • Luxembourg Income Study (LIS) • Redatam Software for micro data dissemination • & analysis • IPUMS-International initiative – archiving, • integration and disseminate high-density census • micro data samples • defining features of these databases include: • research centres not only involved in data • analysis but also in “coordination, dissemination • and harmonization activities” (IECM) • interest and cooperation of NSOs • emphasis on integration & documentation • - IECM major part of project • - IPUMS – International (post-harmonization)

  14. Creating databases become a driver for data harmonization and integration

  15. Challenges • reliability and comparability of data sources across countries • lack of consistency, terminology, classifications, question numbers and wording (all papers) • confidentiality (strict conditions set by countries & more) • ownership (IPUMS - International) • dissemination • Redatam developed by CELADE, Popn. Division • of ECLAS (UN) allows on-line processing of any • database over Internet • LIS uses remote-access system (primary means • to access data) • IPUMS posts census documents on Internet, etc

  16. Other issues • improved analytical capacity at NSOs • training institutions should strike a productive balance between “data demand” and “data supply” in training programmes • bigger dose of data analysis in training centres

  17. III. CONCLUSION • Case made by all papers about need to add value to data • through more detailed analysis • Papers urge NSOs to move away from “risk avoidance” to • “risk management” (European statisticians) • Enhance research infrastructure by creating user-friendly • databases including micro databases • Issues to address include integration, harmonization, • confidentiality

  18. Thank You

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