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Statistical Requirements For Poverty Monitoring In Pakistan
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Statistical Requirements For Poverty Monitoring In Pakistan

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    • 1. Statistical Requirements for Poverty Monitoring in Pakistan Tara Vishwanath Ambar Narayan (World Bank) Workshop in Dubai – Towards a Monitoring Framework for the Full PRSP for Pakistan, August 5-7, 2002
    • 2. Ensuring Compatibility Across Statistical Databases in Pakistan
      • Pakistan’s statistical base
        • Multiple data sources: Population Census, Agricultural Census, Census of Private Schools, PIHS, Labor force survey
      • Issues of compatibility across databases
        • Using most recent census information for sample design of household surveys
        • Using census information to extrapolate from household survey findings
      • Potential benefits of compatibility
        • Poverty map exercise
        • Poverty monitoring
        • Establishing a school database of private and public schools
    • 3. Poverty Map for Pakistan
      • Poverty maps are spatial descriptions of the distribution of poverty in a country
        • Most useful when they represent small geographic units for use by policymakers for targeting public investments or poverty programs
      • Household surveys – not representative at such fine levels of disaggregation; census data – lack poverty information
        • Solution: combine sample survey data with census data to predict consumption poverty indicators using all households in the census
        • Statistical underpinnings of the methodology make such maps more credible than the more commonly found maps based on ad-hoc methods
      • Methodology developed in the Bank have now been piloted in several countries, e.g. Ecuador, South Africa, and Nicaragua
      • For Pakistan – important for Census and PIHS to be compatible
        • E.g. sampling frame of PIHS must be based on the latest census information
    • 4. GIS School Database
      • Already immense GIS progress in Pakistan (NADRA): Next Step: GIS School Database?
      • Why a GIS School Database?
        • What school choices does a child have?
          • Private/Public/NGO
        • In village: Merge data from Census/Private School Census/EMIS
      • BUT
        • Schools may be close to village: NO INFORMATION CURRENTLY AVAILABLE
        • Educational Policy: Upgrading schools, school construction, school improvement
        • GIS will provide village catchment areas for each village
    • 5. Example: School Catchments in Zambia
      • Polygon around each dot is the area closest to X school
      • BUT: no information on villages
      • PAKISTAN: Both information on villages and schools
    • 6. GIS: A reality?
      • Problems
        • Compatibility
          • Different Administrative categories across data sets (Census: Land-based; EMIS: Political Units that change with time)
          • No centralized consistent village list
      • Where are the Current Users?
        • Difficult to use school data below district-wide aggregates
        • Large amounts of data collected: but poor use of available information
      • GIS
        • Very user-friendly database: information on all villages and schools
        • Leads to consistent demand for new and updated information
        • Improves monitoring and efficiency
    • 7. Poverty Monitoring
      • Monitoring important in the context of MDGs
        • Developing baselines; setting targets
      • For measuring long-term impacts, PIHS is primary source
        • Certain issues regarding improvement of PIHS important to consider
      • Intermediate indicators: Monitor indicators that show changes over shorter time horizon
        • Proposed CWIQ-style rotating module should be able to track such indicators
    • 8. Why a Monitoring Tool Like CWIQ (Core Welfare Indicators Questionnaire) ?
      • Urgent need for district level data
        • To inform provincial planners’ decisions to allocate resources to districts
        • To monitor the I-PRSP targets
      • Various sources of information need to be tapped
        • Not just administrative systems, but information directly from households, communities and facilities
      • Why information from households in addition to administrative records (e.g. MIS)?
        • Tells us how key indicators vary across household characteristics: useful for targeting or policy planning
        • Check reliability of administrative data
    • 9. What is CWIQ ?
      • Primarily a household survey used to monitor outcomes of development outcomes (such as PRSPs)…….
      • …… through the use of leading indicators, such as access, use and satisfaction
        • Simple, small set of indicators monitored regularly
        • Indicators are “signals” for broad-based impact of development programs
      • CWIQ also helps strengthen the capacity of countries to use such indicators to design and monitor programs and projects more efficiently
    • 10. Innovative Features in CWIQ
      • Standardized, mostly pre-packaged questionnaire and analytical tools
      • Large sample size
        • Data can be representative at district level
      • Simple and thin questionnaire
        • With multiple choice questions for easy and rapid data collection
      • Quick data entry, validation and result reporting
        • The use of machine-readable questionnaires and optical scanners
        • Pre-programmed validation procedures to ensure high built-in data quality levels
        • “ Push-button” standardized outputs to provide quick feedback to policy-makers
    • 11. A Typical CWIQ Survey
      • Typical CWIQ questionnaire for African countries
        • Basic household roster; education; health; household assets; household amenities; child characteristics
        • Not more than a page for each module
        • Includes questions on satisfaction with public services, e.g. schools, health centers
      • Sample CWIQ outputs – Ghana
        • School enrollment ratios by public/private, rural/urban, regions
        • Reasons for not attending schools
        • Reasons for not satisfied with school/health services
        • Access to school/health facilities
      • Flexible modules
        • E.g. gender module (Nigeria); community CWIQ (Tanzania)
    • 12. Typical Timeline for CWIQs Implemented So Far
      • 1-month pilot survey: small sample of ~1000 households
      • Evaluation workshop, involving data users and suppliers, to assess pilot experience
      • Period of around 6 months to prepare for final survey
      • Full national survey taking 3 months
        • Implemented with close technical support and training from donors
      • Preliminary results available within a few weeks
        • National seminar to discuss survey results
      • Second round to be carried out 1 year after the first
        • the National Statistical Organization expected to implement fully, using institutional capacity developed during previous round, with necessary technical support from donors
    • 13. Specific Recommendations for CWIQ-style Survey in Pakistan
      • Household survey should focus on key indicators related to service delivery & poverty programs
      • District level representation
      • Survey of schooling and health facilities to complement the household survey
      • Coordination with PIHS
        • Integrate with the PIHS time cycle
        • Combine key questions from CWIQ, MICS and PIHS
    • 14. Integrating CWIQ into the Survey Framework
      • PIHS has the important role of measuring a large set of indicators that show changes in the long-term
      • CWIQ will monitor a set of key indicators that will reflect more short-term changes
      • One possible way to integrate
        • Conduct PIHS on a 3-year cycle
        • Conduct CWIQ every year
        • Align the 2 surveys such that PIHS and CWIQ data can be combined to generate a yearly time-series for a small set of key indicators
      • Most importantly, such issues need detailed discussion to arrive at a consensus
    • 15. Policy-Related Benefits from District Level Data
      • Improving geographic targeting of poverty programs
        • E.g. Khushal Pakistan; Food Support program
      • Facilitating fiscal transfers from the national/provincial govt. to the district level
      • Inducing competition among districts for federal and provincial funds
    • 16. Challenges
      • Institutions and capacity building
        • Imperative to ensure that the survey is institutionalized, and becomes a part of the regular statistical monitoring process
      • Ensuring data flow from the bottom up to the national decision-making process
      • Linking policy decisions and budget allocation with feedback from monitoring

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