This paper examines the interaction between health expenditure and health outcomes with due consideration for government effectiveness across developing African economies. The rich data for this study draw from forty-five Sub-Saharan African (SSA) countries covering the period 1960 to 2022. The analysis follows a country-specific comparative manner using the Autoregressive Distributed Lag (ARDL) model as the major estimation technique. The results indicate that poor health outcomes are not due to inadequate budgetary allocations alone. Specifically, this study found a cointegrating relationship and strong adjustment of health outcomes deriving from the shocks and dynamics of not just health expenditures, but also government effectiveness. It is therefore recommended that strong institutions and safety nets be created to guard against corruption and leakages that derail the beneficial impact of public health spending. Also, government expenditures should be focused more on cottage and primary health dimensions to better mitigate adverse health conditions in SSA countries.

Purpose: This study investigates the efficiency of hospital services production in Afghanistan, a conflict-affected and resource-constrained setting, to identify key drivers of hospital productivity and provide actionable insights for healthcare policy and management. Design/methodology/approach: We use 2010–2020 panel data from 29 national public hospitals in Afghanistan. To account for endogeneity and unobserved heterogeneity, we estimate hospital production functions using Olley–Pakes (OP) and OP-generalized method of moments (OP-GMM) control function estimators. We also implement an instrumental variables strategy (geographic distance) to address the endogeneity of bed occupancy. Findings: This study demonstrates increasing returns to labor and material inputs, suggesting gains through improved staffing, while capital inputs show diminishing marginal productivity. Productivity-enhancing bed occupancy reflects the importance of capacity utilization. Conversely, higher mortality rates highlight quality inefficiencies in care delivery. This study advances the performance management literature by applying control function estimators in a fragile context. It integrates methodological rigor with practical relevance, providing new insights into how hospitals convert limited resources into effective service and identifying resource combinations that maximize efficiency and accountability. Originality/value: To our knowledge, this is the first study to apply OP and OP-GMM methods to healthcare in Afghanistan. By addressing endogeneity in input selection, we provide more accurate productivity estimates, offering critical insights for hospital administrators and policymakers. Findings guide investments in labor and materials, improving service utilization and linking productivity to quality metrics. These results have wider implications for fragile health systems in other low-income countries.

This paper analyzes “prompt engineering” through the economic lens of self-insurance against the risk of errors from noisy AI systems. To formalize this approach, we model an agent under cognitive load, allocating effort between working unassisted and prompting an AI assistant. The theoretical model demonstrates that an agent’s optimal prompting effort is driven by the agent’s attitude toward risk. Specifically, the model proves that risk-averse agents rationally “over-invest” in prompting effort, while risk-seeking agents “under-invest” relative to the risk-neutral benchmark. This outcome stems from the covariance between the marginal utility of performance and the marginal product of prompting. This alignment is positive for risk-averse agents, effectively boosting the AI’s perceived productivity. The novel implication is that prompting effort is an economically meaningful behavior that can be informative about an individual’s underlying attitude toward downside AI risk. These results offer a new perspective for understanding heterogeneity in AI adoption and oversight. They also suggest that, under comparable task conditions and controlling for prompting ability, observed prompting effort may be informative about attitudes toward downside AI risk. The framework therefore provides a risk-management perspective for understanding heterogeneity in AI governance in high-stakes settings such as healthcare and finance.