New Model Introduced to Improve Pakistan’s Human Development Index Assessment
A data-driven approach from Research and Development Solutions promises more consistent, district-level health insights by aligning local survey data with global benchmarks.
Research and Development Solutions (RADS) has unveiled a simplified, evidence-based model aimed at improving how Pakistan measures the Human Development Index (HDI), particularly its health component. By marrying local survey data with long-term international trends, the model seeks to replace irregular, hard-to-compare estimates with a reliable, repeatable system that can inform policy over the long run.
Why this matters
HDI aggregates three pillars of human development—health, education, and income—to provide a high-level snapshot of well-being. In Pakistan, the health dimension has historically leaned on demographic surveys that focused heavily on child mortality. Because those surveys are not always conducted on a regular cadence, the resulting data often lacked consistency across time and regions.
RADS’ new model addresses this by centering the calculation on life expectancy and using Pakistan Social and Living Standards Measurement (PSLM) survey data, which are gathered routinely. This change enables district-level estimates that can be compared year over year, improving clarity for policymakers and development partners.
Built on 41 years of global evidence
According to experts associated with the effort, the model draws on 41 years of international data from a range of countries. That historical breadth helps anchor Pakistan’s indicators to global benchmarks and best practices. Where local data gaps exist, the researchers introduced additional indicators to strengthen robustness, then cross-checked the outputs against international datasets to validate accuracy.
What’s new in the approach
- Life expectancy at the district level: PSLM data are used to estimate life expectancy for each district, providing more granular and dependable insights than ad hoc surveys.
- Regular, comparable data: Because PSLM is collected consistently, it supports time-series analysis and reduces reliance on one-off demographic snapshots.
- Global alignment and validation: Results are harmonized with international standards and cross-referenced with global datasets to ensure credibility.
- Gap-bridging indicators: Where PSLM or other sources fall short, the model introduces additional indicators to maintain reliability without overreliance on a single metric.
Policy and planning implications
The move from irregular child mortality estimates to systematic life expectancy calculations is more than a methodology tweak—it’s a foundation for better decision-making. With a clearer picture of health outcomes at the district level, governments and development institutions can:
- Identify underperforming regions and target interventions more precisely.
- Track progress consistently to see what policies are working—and where course corrections are needed.
- Align programs with national development strategies and global benchmarks for human development.
Method in brief
While the technical details are still being unpacked publicly, the process can be summarized as follows:
- Compile multi-decade international datasets to establish a robust reference frame for HDI health measurement.
- Leverage PSLM indicators to estimate life expectancy consistently across districts.
- Introduce supplementary indicators where needed to address missing or irregular data.
- Cross-validate results with global datasets to ensure accuracy and comparability.
A step toward a more resilient HDI
Experts see the RADS model as a long-term solution to the data challenges that have complicated Pakistan’s HDI assessments. By prioritizing regular, locally collected data and validating against international records, the approach lowers the risk of gaps and one-off anomalies skewing national performance snapshots.
As the model is adopted, it could streamline how Pakistan’s institutions monitor health outcomes and, ultimately, how they design policies to improve human development. With more dependable metrics at hand, stakeholders will be better positioned to allocate resources, evaluate programs, and pursue development goals with greater confidence.
