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