From leadership research to district action: The health production function in South Asia: panel evidence on education, health expenditure, governance, and longevity

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract This study examines the association of education, government effectiveness, health expenditure, economic growth, and urbanization with life expectancy in South Asia within Grossman’s health capital framework. Using annual panel data for seven South Asian countries—Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—over 2000–2025, the analysis investigates both long-run and short-run relationships. The empirical strategy combines panel unit root tests, panel ARDL estimation, Driscoll–Kraay standard errors, PCSE, FGLS, System GMM, and bootstrap panel causality tests. The results indicate a stable long-run relationship among the variables. Education, measured by school enrollment and adult literacy, is positively and significantly associated with life expectancy. GDP per capita also shows a positive and significant association. Health expenditure has a positive but model-sensitive association, suggesting that the effectiveness of spending may matter more than its level alone. By contrast, government effectiveness and urbanization are significantly associated with life expectancy but with negative coefficients in the estimated models. Bootstrap causality results indicate dynamic links between life expectancy and several explanatory variables. Overall, the findings highlight the importance of education, inclusive growth, efficient health spending, and better-managed urban development in shaping longevity across South Asia. Keywords: education, government effectiveness, health expenditure, life expectancy, urbanization

Study methods

Table 1 presents a detailed description of the variables employed in the empirical analysis for South Asia. The dependent variable is life expectancy at birth (LE), measured in years. In the empirical model, LE is expressed in natural logarithmic form (LLE) to facilitate interpretation and estimation. The primary explanatory variables capture two key dimensions: education and governance. Education is proxied by two widely used indicators, gross school enrollment rate (SE) and adult literacy rate (LR), reflecting both access to schooling and the stock of human capital. More specifically, school enrollment captures current participation in formal education and therefore reflects the flow dimension of educational expansion, while adult literacy rate captures the accumulated educational capability of the adult population and therefore reflects a stock dimension. Although related, these indicators are not conceptually identical. A country may expand enrollment more rapidly than improvements appear in adult literacy, especially where historical educational deficits remain important. Including both measures therefore allows the empirical model to distinguish between contemporaneous educational participation and the broader educational base already embodied in the population. Likewise, Government effectiveness (GE), drawn from the Worldwide Governance Indicators, serves as the main measure of institutional quality and public sector performance. The model also incorporates three important control variables: current health expenditure (HE), GDP per capita (GPC), and the urban population share (UP). The sample consists of seven South Asian countries: Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Afghanistan is excluded from the analysis because of substantial missing values across several key variables over the study period. Including a country with extensive data gaps could reduce the consistency and reliability of the panel estimations. Therefore, to maintain a balanced panel and ensure the robustness of the econometric analysis, only countries with sufficiently consistent observations are retained. This approach is also in line with existing studies on South Asia that exclude Afghanistan because of data limitations (50, 51).

Limitations

Several limitations should be taken into account when interpreting the findings of this study. The analysis is based on a relatively small macro-panel consisting of seven South Asian countries—Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—over the period 2000–2025, with Afghanistan excluded because of substantial and persistent data gaps across key variables that would have prevented the construction of a balanced panel. Although this regional focus is policy-relevant, the small cross-sectional dimension places clear constraints on econometric inference. In particular, the Driscoll–Kraay estimator is generally more suitable in panels with a larger number of cross-sectional units, so its statistical properties are not fully guaranteed in a sample of this size. For this reason, the Driscoll–Kraay results should not be interpreted in isolation, but rather alongside the PCSE and FGLS estimates, which are treated in this study as co-primary static-panel evidence for assessing the robustness of coefficient signs and statistical significance. Dynamic panel estimators such as System GMM must likewise be interpreted cautiously in small-country settings, even when instrument reduction is applied. In addition, the relatively high R-squared reported in the baseline static model should not be overinterpreted. In log-level macro-panel settings with trending variables, high R-squared values are common and may partly reflect shared persistence and time-trending behavior rather than only strong explanatory power. Accordingly, greater weight is placed on coefficient signs, statistical significance, consistency across estimators, and the long-run and short-run adjustment patterns identified by the panel ARDL framework. In addition, the dataset for 2024 and 2025 includes imputed extension values rather than fully observed source data, since these terminal-year observations were not completely available in the original databases at the time of compilation. Future research could strengthen the empirical base by updating the analysis once fully observed data for the terminal years become available, expanding country coverage where feasible, and comparing the present findings with results from alternative estimators that are better suited to small macro-panels. Related work could also assess whether the conclusions remain stable in carefully specified unbalanced panels that allow the inclusion of countries otherwise excluded because of missing observations.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Mohammad Ridwan; Zulfiquar Ali Antor; Jeremy Ko; Md Emran Hossain; Chun Kai Leung; Wai-Kit Ming; Mohd Ziaur Rehman. The health production function in South Asia: panel evidence on education, health expenditure, governance, and longevity. Frontiers in Public Health (2026-09-16). https://doi.org/10.3389/fpubh.2026.1866937

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.