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

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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