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Beyond Outcomes: A proposal for a Human Capability Metric for Global Health and Development

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05 August 2026

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06 August 2026

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Abstract
Global health and development monitoring relies on a narrow set of outcome-based indicators. While these metrics enable cross-country comparisons, facilitate public policy debates, enrich academic enquiry, and guide investment decisions, they are anchored in achieved states and, by design, capture only a limited set of outcomes rather than whether individuals have the freedom to live the lives they value. By shifting the focus from outcomes alone to both functioning and capabilities, this paper proposes a Human Capability Metric (HCM) comprising four interrelated pillars: life-span, productive, reproductive, and agency capabilities. All four pillars are expressed using a common life-course accounting architecture comprising a capability gap, a decomposable loss metric, and a capability-adjusted life expectancy, thereby extending the logic of the Global Burden of Disease (GBD) into a multidimensional capability framework. This provides a more comprehensive and policy-relevant metric for monitoring global health and development.
Keywords: 
human capability metric
;  
life-course accounting
;  
human capability gaps
;  
life-span capability gap
;  
productive capability gap
;  
reproductive capability gap
;  
agency capability gap
;  
global burden of disease (GBD)
;  
human development index (HDI)
;  
socio-demographic index (SDI)
;  
human capital index (HCI)
;  
poverty-free life expectancy (PFLE)
;  
productivity-adjusted life years (PALY)
Subject: 
Public Health and Healthcare  -   Other

Introduction

GDP per capita, life expectancy, years of schooling, and composite indices such as the Human Development Index (HDI), Socio-demographic Index (SDI), Human Capital Index (HCI), Poverty-free Life Expectancy (PFLE), and Productivity-Adjusted Life Years (PALY) have advanced understanding of human conditions, informed policy debates, shaped global development assistance, and simplified cross-national comparisons [1,2,3,4,5]. These metrics, or the components they capture, are not, however, without limitations.
Demographic indicators such as total or partial fertility rates, used to construct the SDI, do not distinguish among constrained outcomes, coercively shaped fertility patterns, or settings where reproductive outcomes align with individual preferences [6,7]. The distinction is critical and is well illustrated by Nielsen’s analogy between a “fasting person and a starving person”: similar outcomes may arise under very different conditions of autonomy and constraint [8]. Furthermore, by implicitly treating secular fertility decline without a lower limit as normatively desirable, the SDI imposes a theoretical minimum of zero fertility as an ideal outcome—a position that may neither align with individuals’ substantive freedoms nor with divergent demographic priorities across the world.
Similarly, per capita income, embedded in the HDI, SDI, and HCI in various forms, does not distinguish material security achieved through meaningful, stable, and productive engagement from that secured through transfers, insecure employment, or underemployment. Nor do these composite indices capture time lost to unemployment, discouraged participation, or poor-quality work, including jobs that make limited use of skills. PFLE, which combines poverty and survival, fails to acknowledge material deprivation as a consequence of constrained capabilities—such as ill health, limited education, or gaps in productive capabilities arising from involuntary exclusion or skill mismatch. As a result, the core indices used in health and development literature remain, by design, centred on achieved states, capture only a limited set of outcomes, and overlook productive capability gaps that have implications for individual well-being, social participation, and economic resilience. Recent work has also questioned whether the HDI’s treatment of income is compatible with the capability theory that ostensibly underpins it [9].
Capabilities capture opportunities, whereas functionings represent realised achievements [10]. This distinction is often blurred in existing metrics—a point widely recognised, including by the Stiglitz–Sen–Fitoussi Commission [11], and one that has spurred efforts to develop beyond-GDP frameworks [12]. A 2025 International Science Council report concluded that the HDI requires revision to better reflect autonomy, inequality, sustainability, and agency—dimensions largely absent from mainstream development metrics [13,14]. A systematic review of 66 beyond-GDP indicators found that health (86%) and material well-being (77%) are well covered, whereas critical domains such as care services appear in only 17% of indicators [15].
Recent work has proposed an extended capability-based pluralist framework comprising eight pillars—Basic Needs, Health, Education, Meaningful Work, Political Equality, Community, Social Status, and Reasonable Autonomy—while an alternative framework organises human capacities into four domains: vitality, relationality, productivity, and sustainability [8,16]. Both contributions illustrate the growing scholarly momentum towards multidimensional, capability-based measurement. These advances have broadened the scope of monitoring, yet they remain at the construct or philosophical level and have not integrated the various domains into a single quantitative framework with a common capability-gap framework.
This paper addresses these gaps by proposing a unified, capability-based metric—the Human Capability Metric (HCM)—that shifts the evaluative space in global health and development from outcomes alone to both functionings and capabilities, and that capitalises on the logic of the Global Burden of Disease (GBD), which has fundamentally transformed how global health is measured and compared across settings [17]. The HCM is structured around four interrelated pillars: life-span, productive, reproductive, and agency capabilities. Each pillar is defined by a capability gap—a higher-order construct analogous to the GBD framework—within which life-course measures of total loss and full capability are defined. As a dual-level framework, it treats each pillar as important for both individual flourishing and societal well-being. The following sections define each pillar, describe its operationalisation using existing data systems, discuss the framework’s scope and limitations, and outline implications for policy and measurement. The rationale for pillar selection, a phased implementation roadmap, and the formal measurement architecture are provided in the Appendices.

The Four Pillars of Human Capability

The HCM’s conceptual basis is rooted in the capability approach [10,18,19,20,21]. Sen’s work provides the evaluative foundation; Nussbaum’s and Nielsen’s work offer the conceptual breadth; and the HCM provides the measurement framework and a parsimonious set of capability-focused metrics directly relevant to population health, demographic sustainability, and economic resilience. The HCM is structured around four interrelated domains—life-span, productive, reproductive, and agency capabilities—aimed at measuring how long, how well, and how fully people can exercise their freedom as individuals and as societies.
All four pillars share a common conceptual and measurement architecture. Each is defined by a capability gap—the shortfall between full and realised capability—and operationalised through a life-course accounting framework comprising a decomposable loss metric and a capability-adjusted life expectancy. This structure extends the logic of disability-adjusted life years (DALYs) and health-adjusted life expectancy (HALE) beyond health to other domains of human capability, shifting the focus of global monitoring from achieved outcomes to the capabilities that underpin them.
As a dual-level framework, the four pillars are important for both individual flourishing and societal well-being. At the individual level, the pillars represent core dimensions of capability in health, productive engagement, reproductive autonomy, and agency. At the societal level, they reflect the extent to which institutional and structural conditions enable these capabilities to be realised across populations.
Collectively, the four pillars present a structured account of the conditions required for human flourishing. First, flourishing presupposes existence: the capacity to live a long, healthy life, captured by the life-span pillar. Second, sustaining that life requires the ability to engage in productive activity that secures material well-being and enables participation in society, as reflected in the productive capability pillar. Third, the continuity of individuals and societies over time depends on the ability to realise reproductive preferences, making reproductive capability essential for demographic sustainability. Finally, these capabilities can only be meaningfully exercised when individuals have the freedom to form preferences, make choices, and act on them—as captured by the agency pillar. These domains are interdependent yet conceptually distinct, and their ordering reflects a progression from the conditions that make life possible to the freedoms that make life meaningful. A summary of these dual interpretations is provided in Appendix 2 (Table A2), with the formal measurement approach and cross-pillar alignment presented in Appendix 1.

Life-Span Capability

The philosophical case for treating health as a capability rather than an outcome is well established. Sen argues that health is a constitutive dimension of human freedom rather than merely an instrument of welfare, and that it should not be evaluated by examining outcomes alone, without reference to the conditions of freedom under which those outcomes are produced [10,22]. Nussbaum similarly identifies life and bodily health as central capabilities whose threshold realisation is required for human dignity [21]. Venkatapuram extends this view by defining health as a meta-capability—the enabling condition for all other dimensions of a flourishing life [23,24]. Adopting a sufficientarian perspective, Nielsen has further argued that being below a threshold of health capability deprives one of the very conditions that make the pursuit of other central capabilities possible [20,25].
None of these frameworks, however, quantifies the capability gap at the population level. The omission is deliberate, reflecting the authors’ preoccupation with philosophy, justice, and morality, as well as their pursuit of the best possible substantive account of capability [20,26]. The GBD framework, by contrast, provides a comprehensive empirical system for measuring health loss but does so from the perspective of an outcome rather than a foundational capability, and does not explicitly connect the construct to the capability approach. What sets the HCM apart from both strands is that it integrates the philosophical foundation of health capability with the GBD’s empirical measurement architecture. In so doing, it serves as GBD+, in that it combines health and non-health capability gaps, with the health gap, known as the Life-span Capability Gap (LCG), as one of its four constituent pillars.
Specifically, LCG is conceived as the shortfall between full and realised health capability over the life course. Consistent with the decomposition applied throughout the four pillars, LCG arises from three sources: Health resource constraints (HRC), Involuntary health exclusion (IHE) and Health capability mismatch (HMM). HRC represents the enabling layer of the gap: the life-span capability shortfall that arises not from disease or death directly but from the inadequacy of the conditions under which health capability must be achieved. This source maps onto the GBD’s risk factor attribution architecture and aligns with Sen’s insistence that health capability cannot be evaluated in isolation from the social conditions under which it is produced [10,22]. Hence, HRC captures the upstream conditions required to realise health capability—namely, access to healthcare, environmental quality, food security, safe water and sanitation, and the structural social determinants of health, including poverty, education, and housing —that shape health outcomes. IHE is the most severe form of the gap: total foreclosure of health capability rather than its diminishment. It captures the complete loss of life-span capability through premature mortality—years of life cut short before a normative benchmark lifespan. IHE corresponds directly to years of life lost (YLL) in the GBD framework, reinterpreted here not as a health state but as the complete and involuntary termination of the capacity to live a healthy life. HMM captures a partial loss of life-span capability through years lived with illness, disability, or functional limitation, reducing the quality of health capability without ending it entirely. A person in this state is alive but unable to exercise health capability in full—a gap between the life-span capability nominally present and that which can actually be realised. HMM corresponds to years lived with disability (YLD) in the GBD, recast as a capability shortfall rather than a health burden.
Together, IHE and HMM constitute the aggregate life-course loss—LCG—which is operationalised using two established GBD measures repositioned within this capability framework: DALYs, which combine YLL and YLD into a measure of total health loss, are reinterpreted as the gap between full life-span capability and its realisation [17]; and HALE, which captures expected longevity net of morbidity, is reinterpreted as the expected years of life in full health capability [17]. Integrating these measures allows LCG to reflect both the length and quality of life lived, rather than relying on life expectancy alone. HRC, as the upstream source, informs the causal attribution of LCG rather than its direct quantification—precisely as risk factors do in the GBD. Critically, this decomposition does not require new health data: it recasts what the GBD already measures within the evaluative space that Sen, Nussbaum, Venkatapuram, and Nielsen have argued is appropriate, while adding a structural source that makes the determinants of the gap visible alongside its manifestations. As shown in Appendix 1, this same three-source logic underpins the life-course accounting for the productive, reproductive, and agency pillars.

Productive Capability

Conventional welfare and development metrics capture material security and deprivation through income, poverty, and unemployment rates, but these measures obscure the conditions under which income is earned or deprivation is avoided. Research demonstrates that employment quality—security, autonomy, fair pay, and skill match—is as important as employment status or income level itself [28,29], consistent with the capability approach’s shift of focus from means to what individuals are able to do and be [10].
The Productive Capability Gap (PCG) is conceived as the shortfall between full and realised productive capability over the life course. PCG arises from three sources: Material insecurity (MIS), which captures shortfalls in disposable income and transfers; Involuntary exclusion (IEX), which captures time lost to unemployment or constrained non-participation driven by lack of opportunity; and degraded job quality and capability mismatch (QCM), which captures deficits in security, autonomy, reward, or skill match. These measures can be aggregated to define a person’s Disutility-Adjusted Productive Life (DAPL), yielding two life-course measures that mirror DALYs and HALE: Capability-Adjusted Productive Years Lost (CAPY) and Capability-Adjusted Productive Life Expectancy (CAPLE).
CAPY can be further decomposed into Productive Years Lost to Exclusion (YLE), representing complete loss, and Years Lived with Reduced Productive Capability (YLR), representing partial loss due to job-quality and capability mismatches. As the GBD metric requires disability weights to quantify DALYs and construct HALE, Employment Disutility Weights (EDWs)—survey-based weights on a 0–1 scale analogous to disability weights in population health—are required to implement CAPY and CAPLE (See Appendix 1 for technical details). Unlike PALYs, which use wage-based weights, CAPY and CAPLE reflect welfare loss based on social preferences, thereby avoiding the circularity of linking capability to market prices. A recent scoping review of 41 PALY studies found that most originate in high-income settings and that estimates vary widely depending on whether unpaid and informal work are included, underscoring the limitations of wage-based productivity weights and reinforcing the need for capability-based alternatives such as EDWs [30].

Reproductive Capability

Drawing on the ICPD-94, reproductive capability is conceived as the ability to achieve desired fertility outcomes safely and autonomously. [31,32] Unlike conventional fertility indicators, this pillar assesses whether individuals can realise their preferences for the number and timing of children. The distinction is crucial, since similar fertility levels can arise under very different conditions—including limited access to services, voluntary choice, gender inequity, economic constraints, or coercion [33,34,35]. A capability-based approach addresses this by focusing on the relationship between desired and realised fertility [36].
The Reproductive Capability Gap (RCG)—the shortfall between full and realised reproductive capability—arises from three sources: Reproductive Resource Constraints (RRC), which capture limitations in access to contraception and fertility services, and to the material, legal, social, and institutional conditions required to realise fertility preferences; Involuntary Reproductive Exclusion (IRE), which captures the complete failure to achieve desired fertility, including involuntary childlessness; and Reproductive Mismatch (RMM), which captures partial deviations between desired and achieved fertility in terms of the timing, spacing, number, and composition of children.
RCG can be summarised by Capability-Adjusted Reproductive Years Lost (CARY), comprising Reproductive Years Lost (RYL) from complete exclusion and Years Lived with Reduced Reproductive Capability (YRR) from resource constraints and mismatch. A complementary metric, Capability-Adjusted Reproductive Life Expectancy (CARLE), measures the expected years lived in full alignment with fertility preferences (See Appendix 1 for technical details). In principle, these components could be weighted using Reproductive Disutility Weights (RDWs) to reflect the varying welfare losses associated with different reproductive states, acknowledging that a gap arising from coercion is not equivalent to one arising from a minor timing mismatch.
Data for most of these components are available through established national and international family, fertility, gender, and health surveys, enabling immediate partial implementation. Unmet need for contraception—already measured in Demographic and Health Surveys—can, for example, be directly mapped to RRC, IRE, and RMM, thereby enabling an immediate partial estimate of RCG. However, research consistently shows that reproductive preferences shift over the life course, are shaped by economic circumstances and partnership status, and are susceptible to adaptive preference formation—a process in which individuals adjust aspirations in response to structural constraints rather than forming them freely [37,38,39]. Survey-based fertility intentions, therefore, should not be treated as uncontested benchmarks of autonomous preference. Implementing RCG consequently requires conservative adaptation: where longitudinal intention data are unavailable, the framework could use a combination of period-specific fertility intentions and demographic indicators of unmet reproductive need as approximations of constrained capability, acknowledging that these proxies underestimate the full depth of reproductive capability gaps in high-constraint settings. In addition, certain settings may hinder the collection of data relevant to developing RDWs, because the conditions that produce the most severe reproductive disutility—coercion, suppressed autonomy, or legal and cultural barriers—are precisely those least likely to yield reliable data. In the subsequent section, approaches to addressing these challenges are discussed, including estimating core indicators without weights during the first-tier implementation stage (see Appendix 3).

Agency Capability

The theoretical foundation for treating agency as a distinct fourth pillar rests on a distinction Sen draws explicitly in his own work and that the other three pillars do not capture: the difference between well-being freedom—the freedom to achieve states of personal flourishing—and agency freedom—the freedom to pursue goals and values one has reason to value, regardless of their contribution to personal well-being [10,26]. The first three pillars of the HCM are concerned with well-being freedom: the capacity to live long and healthy, to engage productively, and to realise reproductive preferences. Agency freedom is categorically different. A person may score well across those three dimensions yet remain profoundly constrained in their ability to participate in public life, challenge decisions that affect them, or act on commitments that extend beyond personal welfare. Agency capability matters, therefore, not primarily as an enabling condition for the other pillars—though it is that—but as an intrinsically valuable dimension of freedom in its own right. Nussbaum similarly treats control over one’s environment as a central capability distinct from health, productive activity, and bodily integrity, and Robeyns identifies political and social participation as a core component of the capability approach that cannot be absorbed into other domains [18,21]. Grounding the pillar in this theoretical distinction establishes agency capability as an independent evaluative space, not a residual category.
The decomposition of the Agency Capability Gap (ACG)—the shortfall between full and realised agency capability—is structured around Sen’s and Robeyns’s concept of conversion factors: the personal, social, and environmental conditions that determine how effectively individuals can translate resources and rights into the actual exercise of freedom [10,18]. This yields three analytically distinct components. Agency endowment gaps (AEG) capture shortfalls in the personal capacities required to convert rights and resources into agentic action—principally literacy, numeracy, critical reasoning, and digital literacy. Structural agency gaps (SAG) capture limitations imposed by the social and institutional environment—such as civil liberties, political rights, media independence, internet access, and digital rights—that constrain or preclude the conversion of agency, regardless of individual endowments. Where AEG operates at the level of the person, SAG operates at the level of the system: it is the difference between a context that enables and one that forecloses the exercise of freedom. Realised agency gaps (RAG) capture the residual shortfall between formally adequate conditions and actual participation and voice—arising from internalised constraints, social norms, adaptive preference formation, and other mechanisms that suppress agency even when individual endowments and structural conditions are nominally sufficient. This third component is theoretically the most distinctive and the most demanding to measure: it requires not only data on participation but also an assessment of whether observed non-participation reflects a genuine preference or constrained capability. This decomposition—AEG, SAG, RAG—follows the same internal logic as the other three pillars: a progression from the conditions that make capability conversion possible, through the structural environment that enables or restricts it, to the residual gap between conditions and realised freedom.
The ACG is expressed using the same life-course accounting logic as the other pillars, though the temporal dimension requires explicit justification here. Unlike health loss or productive exclusion, agency deprivation is not naturally measured in discrete units of time. The rationale for a life-course expression rests on the observation that agency deprivation, like chronic morbidity in the GBD framework, is a condition that operates continuously across extended periods of a person’s life: years lived under authoritarian governance, prolonged legal disenfranchisement, or persistent exclusion from civic life are meaningfully described as agency-diminished years, in the same sense that years lived with a chronic condition are health-diminished years. The ACG is accordingly summarised by Capability-Adjusted Agency Years Lost (CAAY), comprising Agency Years Lost (AYL) from complete exclusion—such as disenfranchisement or severe political repression—and Years with Reduced Agency (YRA) from endowment and structural gaps that partially constrain but do not wholly prevent the exercise of agency. A complementary metric, Capability-Adjusted Agency Life Expectancy (CAALE), captures the expected number of years lived under conditions of full agency capability. The analogy with the GBD disability weight framework is directly applicable: just as disability weights are applied continuously to years lived with a chronic condition, Agency Disutility Weights (ADWs) would be applied to years lived under varying degrees of agency constraint.
Data for partial implementation of the ACG are available from multiple established sources. V-Dem and Freedom House provide country-level indicators of civil liberties, political rights, and media independence that map directly to SAG [40,41]. The World Values Survey and Afrobarometer provide individual-level data on civic participation, political efficacy, and trust in institutions, enabling partial estimation of RAG [42,43]. Educational attainment and literacy data from national censuses and household surveys, including those collected through the UNESCO Institute for Statistics and the DHS Programme, support estimation of AEG. The Women’s Empowerment in Agriculture Index (WEAI) and its derivatives offer validated instruments for measuring constrained agency at the individual level, including the realised agency gap arising from internalised constraints and social norms, and provide a methodological template for survey-based AEG and RAG estimation beyond agricultural contexts [44]. Ibrahim and Alkire’s internationally comparable agency and empowerment indicators, developed within an explicit capability framework, offer a further operational reference point for cross-country implementation [45]. Together, these sources enable an initial tier of ACG estimation using existing data, without requiring new survey infrastructure.
The development of Agency Disutility Weights (ADWs) may face a measurement constraint analogous to, and in some respects more acute than, that noted for RDWs: the institutional conditions that produce the most severe agency deprivation are precisely those that most severely restrict the data collection needed to estimate ADWs. This is not merely a practical inconvenience but a structural feature of the measurement problem. In highly repressive settings, both survey responses and administrative data are systematically distorted by the very conditions the weights are meant to capture. Two strategies are proposed to address this. In settings where data conditions permit, a modified vignette approach—used successfully in the GBD disability weight studies—can be deployed to elicit population preferences across agency states, drawing on the methodological precedent of cross-cultural elicitation in health [26]. In settings where direct elicitation is infeasible, a transferability approach can be applied by adapting weights estimated in comparable settings, using observable structural covariates—governance quality, gender equity indices, poverty rates—to generate bounded estimates with explicit uncertainty intervals. Expert opinion may also serve as a starting point in such settings, consistent with the earliest phase of GBD disability weight development. A structured two-tier implementation strategy—outlined in Appendix 3—distinguishes between settings where ADWs can be estimated empirically and those where indicator-based proxies serve as substitutes pending methodological development, ensuring the framework remains actionable across data environments rather than only in settings where agency deprivation is least severe.

Why These Four Pillars: Selection Rationale and Scope

The selection of the four pillars—life-span, productive, reproductive, and agency capabilities—is guided by three criteria: conceptual importance (each pillar must represent a domain of substantive freedom that is central to human flourishing and not reducible to another pillar); measurability (each must be quantifiable using existing or plausibly extendable data systems); and distinctness (each must represent an evaluative space not adequately covered by existing global indicators).
Several additional candidate domains were considered and excluded from further consideration. Relational and care capabilities—the ability to engage in social relationships and to provide or receive care—are conceptually important but raise substantive measurement challenges at the population level, particularly for cross-country comparability, and are partially captured within the productive and agency pillars through the quality-of-work and participation dimensions. Environmental sustainability was considered as a potential fifth pillar. Environmental conditions, however, are considered as cross-cutting and primarily operate as a structural determinant that shapes the realisation of all four existing pillars—climate shocks affect health (life-span), labour markets (productive), fertility decisions (reproductive), and political participation (agency)—rather than constituting a capability domain in its own right at the individual level. For this reason, environmental sustainability is acknowledged as a cross-cutting modifier of all four capability gaps rather than as a standalone pillar, consistent with how the GBD framework treats social determinants. This design choice is revisited in the Future Directions section.
The four-pillar structure deliberately balances completeness and measurability, providing a manageable foundation for capability-based monitoring. Each pillar addresses a specific limitation in existing metrics: life-span capability reframes established health measures as foundational freedoms rather than mere outcomes; productive capability replaces outcome-focused material security indicators with welfare-loss gaps; reproductive capability shifts from demographic accounting to preference-realisation monitoring; and agency capability directly measures individuals’ ability to participate and act, rather than relying on indirect proxies such as educational attainment.
In principle, the four domains can be normalised and aggregated into a composite index using a geometric mean without explicit weights, consistent with approaches used in the HDI, SDI, the Multidimensional Poverty Index, and the Productive Capacity Index. The framework’s primary purpose is, however, diagnostic rather than aggregative, with aggregation remaining an optional second-order analysis once domain-level estimates are established.

Implications for Policy and Monitoring

The HCM provides policymakers with a diagnostic framework that goes beyond tracking outcomes to identify the underlying constraints that produce them across domains. By decomposing capability gaps into resource limitations, exclusion, and mismatch, the framework enables more precise targeting of interventions. Employment rates, per capita income, or fertility outcomes—even when identical across settings—can arise under very different conditions, requiring fundamentally different policy responses. By making these distinctions explicit, the HCM supports more efficient and context-sensitive policy design.
The framework is also designed to be policy-relevant in a direct institutional sense. Each pillar aligns with a corresponding institutional domain—health systems, labour markets, reproductive and gender policies, and governance structures—enabling capability gaps to be linked directly to policy levers. This supports a more precise diagnosis of constraints and a more targeted intervention design.
The framework is compatible with existing monitoring architectures. It extends life-course measurement beyond health while retaining the principles of standardisation, comparability, and scalability that underpin global metrics [45]. Most components can be implemented using existing data sources—including GBD estimates, labour force and fertility surveys, and governance indicators—enabling immediate partial application. Over time, integration into routine monitoring systems would enable more systematic tracking of capability gaps across domains.
A key design feature of the HCM is its explicit recognition of heterogeneity in data availability across country contexts. The framework does not assume uniform implementability. In high-income OECD settings, where rich administrative data, panel surveys, and well-funded statistical systems exist, most HCM components can be estimated with relatively high fidelity. In low- and middle-income countries (LMICs)—precisely those where capability gaps are most acute—data systems are frequently incomplete, particularly for job quality, fertility intentions, and agency indicators; a modelling exercise akin to the GBD may be necessary. The HCM is therefore designed with a phased implementation structure (Appendix 3) that distinguishes between what can be estimated now using existing global survey data, what requires survey instrument expansion in the medium term, and what depends on novel data-collection approaches or methodological development in the long term. This graduated approach mirrors the evolution of the GBD framework, which expanded incrementally as data systems matured.

Disutility Weights: Operationalisation Strategy

The disutility weights required for the non-health pillars—Employment Disutility Weights (EDWs), Reproductive Disutility Weights (RDWs), and Agency Disutility Weights (ADWs)—are central to fully quantifying capability losses and represent an important empirical gap. Their development is analogous to the disability weight estimation programme underpinning the GBD framework, which evolved over multiple study cycles from expert elicitation to population-based valuation surveys [26].
For EDWs, the most tractable of the three, a structured elicitation approach is proposed, using vignette-based preference surveys and discrete choice experiments administered within national labour force survey contexts. This approach draws on established methodologies in health economics and quality-of-life research and would enable estimation of welfare-loss weights across productive states—full participation, underemployment, involuntary unemployment, and informal and precarious work—that are comparable across countries. Piloting in a small number of countries with diverse labour market structures would provide an initial empirical basis for EDW estimation.
For RDWs and ADWs, the methodological pathway is more constrained. As noted above, the conditions that generate the most severe reproductive and agency deprivation are frequently those that simultaneously restrict the data collection required to estimate their welfare-loss weights. Two strategies are proposed to address this. First, in settings with sufficient data freedom, a modified vignette approach—used successfully in cross-cultural GBD disability weight studies—can be deployed to elicit population preferences over reproductive and agency states. Second, in settings where direct elicitation is infeasible, a transferability framework can be applied: disutility weights estimated in comparable settings can be adapted using observable structural covariates—such as institutional quality, poverty rates, and gender equity indices—to generate bounded estimates. This approach is explicitly imperfect and produces wider uncertainty intervals, but it avoids the alternative of leaving capability gaps entirely unquantified in the settings where they are most acute. In such settings, expert opinion may also serve as a starting point, as was the case in the initial GBD exercise.
Until disutility weights are developed, a two-tier framework is proposed (Appendix 3): a Tier 1 indicator-based implementation that uses unweighted component indicators to construct partial capability-gap profiles without aggregation, and a Tier 2 weighted implementation applicable where disutility weights have been validated. This structure ensures the framework is immediately usable in its diagnostic function while providing a clear roadmap for progressive quantification.

Future Directions and Measurement Priorities

Several areas require future development. Existing data systems do not fully capture key dimensions of capability, particularly in the productive, reproductive, and agency domains. While measures such as unmet contraceptive need, labour force participation, and governance indicators provide partial coverage, more systematic data collection is needed on fertility preferences, reproductive constraints, job quality, and realised participation. Expanding survey instruments—including Demographic and Health Surveys, labour force surveys, and governance datasets—to include these dimensions would enable more complete implementation of the proposed metric.
The construction of disutility weights for non-health domains—EDWs, RDWs, and ADWs—remains an important empirical priority. As discussed above, a phased elicitation programme, beginning with EDWs and proceeding to RDWs and ADWs as methodological and data conditions permit, offers the most feasible pathway. The development of validated disutility weights should be treated as a research programme in its own right, analogous to the multi-decade GBD disability weight estimation effort.
Further work is needed to refine the aggregation and comparability of capability measures across contexts. This includes developing standardised definitions, improving cross-country harmonisation of indicators, and testing sensitivity to alternative weighting approaches. Simulation studies using existing datasets should be prioritised to assess the framework’s empirical behaviour under different parameterisations before full-scale implementation.
The treatment of environmental sustainability as a cross-cutting determinant warrants further elaboration. While this paper argues against a standalone environmental pillar on the grounds that environmental conditions operate through existing capability domains, the mechanisms by which climate shocks and ecological degradation compound capability gaps—particularly for already-disadvantaged populations—merit formal modelling within the HCM structure. Future work could consider developing explicit environmental adjustment factors for each pillar’s life-course accounting.
Finally, the relationship between the HCM and existing global monitoring frameworks—particularly the SDGs, GBD, and the HDI—should be clarified through a formal mapping exercise. Such an exercise would identify where the HCM adds non-redundant measurement value, where it can draw on existing indicator infrastructure, and where indicator conflicts or definitional tensions arise. This would facilitate smoother integration with established international statistical systems and reduce the burden of new data collection.

Conclusion

Current global health and development metrics focus primarily on outcomes achieved rather than on the freedoms that enable individuals to achieve them. The HCM addresses this limitation by integrating outcomes and capabilities across four domains—life-span, productive, reproductive, and agency—and by extending the life-course logic of DALYs into a multidimensional capability framework. The result is a unified metric for measuring both realised achievements and the conditions that underpin them.
The HCM operates as a dual-level framework. At the individual level, each pillar represents a domain of substantive freedom. At the societal level, the distribution of these capabilities shapes broader outcomes, including population health, economic resilience, demographic sustainability, and institutional legitimacy. This approach enables the identification of capability deficits that conventional indicators overlook—for example, the coexistence of high employment with poor job quality, fertility outcomes that mask unmet preferences, or formal rights without meaningful participation.
The framework’s phased implementation structure ensures it is practical across diverse data environments, not merely aspirational. By being explicit about what can be estimated now, what requires instrument expansion, and what depends on novel methodological development, the HCM invites a cumulative research programme rather than prescribing an all-or-nothing measurement transformation. Environmental sustainability, while treated as a cross-cutting determinant in this framework, remains an open design question that future iterations should address more formally.
As global monitoring systems evolve beyond GDP and aggregate indicators, the HCM provides a practical foundation for next-generation measurement—one that shifts the focus from what is achieved to what is possible. By making the conditions of freedom as visible as their outcomes, it offers a more complete and more honest account of human development.

Appendix 1: Formal Measurement Architecture

A1. Overview and Structure

This appendix formalises the Human Capability Metric (HCM) by extending the DALY and HALE framework to three additional domains: productive, reproductive, and agency capability. For each pillar, we define: (i) a conceptual capability gap (higher-order construct); (ii) a total loss metric, decomposed into complete loss (analogous to YLL) and partial loss (analogous to YLD); and (iii) a capability-adjusted life expectancy, derived using a Sullivan-type method. All notation follows the main text: Life Span: HRC, IHE, CMM; Productive: MIS, IEX, QCM; Reproductive: RRC, IRE, RMM; Agency: ARC, AEX, ASM. Lowercase symbols (e.g., p x , q x , s x ) denote age-specific aggregate loss fractions derived from these components.

A2. Canonical Health Reference: DALYs and HALE

A2. Life-Span Capability

A2.1 Conceptual gap
L C G ⇒ { D A L Y s , H A L E }
The Life-span Capability Gap (LCG) captures the shortfall between full and realised health capability over the life course. Unlike the productive, reproductive, and agency pillars, the LCG is directly operationalised through the established GBD framework — DALYs and HALE — which the HCM reinterprets within a capability-evaluative space. The three-source decomposition below maps onto this existing architecture rather than replacing it.
A2.2 Total loss
D A L Y s = Y L L + Y L D
Where DALYs is total life-span capability loss; YLL is complete loss (analogous to RYL and YLE), and YLD is partial loss (analogous to YRR and YLR).
Complete loss
Y L L = ∑ a D a   n a
where D a denotes individuals experiencing involuntary health exclusion (IHE) — premature mortality before a normative benchmark lifespan.
Partial loss
H W i a = α   H R C i a + β   I H E i a + γ ( 1 − I H E i a ) H M M i a Y L D = ∑ a ∑ i H W i a   n a
where HRC captures upstream health resource constraints, IHE captures the residual morbidity burden among survivors, and HMM captures health capability mismatch — years lived with illness, disability, or functional limitation. The 1 I H E i a term ensures mismatch losses apply only to years lived, avoiding double-counting with YLL.
A2.3 Expectancy
h x = α   H R C x + β   I H E x + γ ( 1 − I H E x ) H M M x H A L E 0 = 1 l 0 ∑ x L x ( 1 − h x )
Where HALE is the expected years of life in full health capability — the life-span analogue of CAPLE, CARLE, and CAALE. Note, in the GBD framework, disability weights serve as the empirical basis for YLD and HALE. Within the HCM, these correspond to the health-domain instantiation of the disutility weights applied in the productive, reproductive, and agency pillars (EDWs, RDWs, ADWs), providing cross-pillar methodological consistency.

A3. Productive Capability

A3.1 Conceptual Gap

The Productive Capability Gap (PCG) captures the shortfall between full and realised productive capability:
P C G ⇒ { C A P Y , C A P L E }

A3.2 Total Loss (DALY Analogue)

C A P Y = Y L E + Y L R
Where, CAPY is the total productive capability loss; Y L E represents a complete loss to productive capability (analogous to YLL) and Y L R is partial loss (analogous to YLD)
Complete loss
Let E a denote individuals involuntarily excluded from productive participation:
Y L E = ∑ a E a n a
Partial loss
Let M I S i a , I E X i a , Q C M i a denote material insecurity, exclusion fraction, and job-quality mismatch for the individual i .
P W i a = α M I S i a + β I E X i a + γ ( 1 − I E X i a ) Q C M i a Y L R = ∑ a ∑ i P W i a n a
The term 1 I E X i a ensures that job-quality loss applies only to time in participation, avoiding double-counting.
A3.3 Expectancy (HALE Analogue)
Let p x be the age-specific lost productive capability fraction:
p x = α M I S x + β I E X x + γ ( 1 − I E X x ) Q C M x C A P L E 0 = 1 l 0 ∑ x L x ( 1 − p x )
Where, CAPLE is the expected years in full productive capability.

A4. Reproductive Capability

A4.1 Conceptual Gap

R C G ⇒ { C A R Y , C A R L E }
The Reproductive Capability Gap (RCG) captures the deviation between desired and realised fertility trajectories.

A4.2 Total Loss

C A R Y = R Y L + Y R R
Where CARY is total reproductive capability loss; R Y L is a complete loss (analogous to YLL) and Y R R is partial loss (analogous to YLD).
Complete loss
R Y L = ∑ a R a n a
where R a denotes individuals experiencing involuntary reproductive exclusion.
Partial loss
R W i a = δ R R C i a + η I R E i a + θ ( 1 − I R E i a ) R M M i a Y R R = ∑ a ∑ i R W i a n a

A4.3 Expectancy

q x = δ R R C x + η I R E x + θ ( 1 − I R E x ) R M M x C A R L E 0 = 1 l 0 ∑ x L x ( 1 − q x )
Where CARLE is the expected years aligned with fertility preferences

A5. Agency Capability

A5.1 Conceptual Gap

A C G ⇒ { C A A Y , C A A L E }
The Agency Capability Gap (ACG) captures the shortfall between full and realised agency.

A5.2 Total Loss

C A A Y = A Y L + Y R A
Where CAAY is the total agency capability loss; A Y L represents complete loss (analogous to YLL) and Y R A is partial loss (analogous to YLD)
Complete loss
A Y L = ∑ a A a n a
Partial loss
A W i a = λ A E G i a + μ S A G i a + ν ( 1 − S A G i a ) R A G i a Y R A = ∑ a ∑ i A W i a n a

A5.3 Expectancy

s x = λ A E G x + μ S A G x + ν ( 1 − S A G x ) R A G x C A A L E 0 = 1 l 0 ∑ x L x ( 1 − s x )
Where CAALE is expected years lived with full agency

A6. Parameter Interpretation

Parameters α, β, γ; δ, η, θ; and λ, μ, ν weight the relative contribution of each domain component (0 ≤ W ≤ 1). They may be estimated using survey-based valuations analogous to disability weights, calibrated with normative assumptions, and standardised for cross-country comparability.

A7. Unified HCM Structure

For each capability domain k ∈ {L, P, R, A}: Gk ⟹ {kₖ,kEₖ}k Yₖ = Yᵏcomplete + Yᵏpartia0; E₀ᵏ =0(1xlx) Σₓ xₓ(1 − zₓᵏ)
Table A1. Unified Structure of the Human Capability Metric (HCM).
Table A1. Unified Structure of the Human Capability Metric (HCM).
Pillar Capability Definition Gap Concept Components Total Loss Metric Loss Decomposition Full Capability Metric Weights
Life-span Long and healthy life Life span gap HRC,IHE, CMM DALYs YLL + YLD HALE Disability weights
Productive Meaningful economic engagement Productive capability gap MIS, IEX, QCM CAPY YLE + YLR CAPLE Employment Disutility Weights (EDW)
Reproductive Achieve desired fertility Reproductive capability gap RRC, IRE, RMM CARY RYL + YRR CARLE Reproductive Disutility Weights (RDW) — conceptual
Agency Form and act on preferences Agency capability gap AEG, SAG, RAG CAAY AYL + YRA CAALE Agency Disutility Weights (ADW) — conceptual

Appendix 2: Dual Interpretation of Capability Pillars

Table A2. Dual interpretation of capability pillars across individual and societal levels.
Table A2. Dual interpretation of capability pillars across individual and societal levels.
Pillar Individual-level interpretation Societal-level interpretation
Life-span capability (LCG) Ability to live a long and healthy life, free from premature mortality and avoidable morbidity. Reflected in total health loss (DALYs) and HALE. Population health as a foundational condition for social and economic development, reflected in the distribution of survival and HALE across the life course.
Productive capability (PCG) Ability to engage in meaningful, dignified, and appropriately matched productive activity providing income, purpose, and self-respect. Losses captured in CAPY (YLE + YLR) and CAPLE. Labour-market performance and enabling institutional conditions supporting broad productive engagement, social cohesion, and long-term economic sustainability.
Reproductive capability (RCG) Freedom to achieve desired fertility outcomes under conditions of safety, autonomy, and informed choice. Losses captured in CARY (RYL + YRR) and CARLE. Demographic sustainability as an emergent property of realised reproductive aspirations, without treating fertility as a target. Captured in aggregate CARY and CARLE.
Agency capability (ACG) Ability to form preferences, make informed choices, and act on them in personal, social, and political life. Losses captured in CAAY (AYL + YRA) and CAALE. Democratic participation, vibrant civil society, and institutional accountability enabling collective decision-making and sustained social progress. Captured in aggregate CAAY and CAALE.

Appendix 3: Phased Implementation Roadmap

The HCM is designed for progressive implementation across three tiers, recognising that data systems vary substantially across country contexts. The framework’s diagnostic value does not require full implementation to be realised.
Table A3. HCM Phased Implementation Roadmap.
Table A3. HCM Phased Implementation Roadmap.
Tier Implementation Stage What Is Estimated Data Sources Required Disutility Weights Applicable Context
Tier 1 Immediate partial implementation Unweighted component indicators for each pillar; capability-gap profiles without aggregation GBD estimates, DHS surveys, ILO labour force surveys, V-Dem / Freedom House, World Values Survey Not required All country contexts; low data burden
Tier 2 Medium-term weighted implementation Weighted capability-gap metrics (CAPY, CARY, CAAY) using empirically estimated disutility weights Above plus: national job quality surveys, longitudinal fertility intention surveys, expanded governance modules EDWs validated; RDWs and ADWs estimated where feasible; transferability weights elsewhere Countries with expanded survey infrastructure
Tier 3 Full life-course implementation Complete CAPLE, CARLE, CAALE metrics with cross-nationally harmonised disutility weights Integrated longitudinal cohort data, harmonised cross-national survey programmes, and expanded administrative data All disutility weights validated cross-nationally High-income and upper-middle-income countries initially; global rollout as data systems mature
Note: Environmental sustainability operates as a cross-cutting modifier at all tiers. Future iterations of the HCM should develop explicit environmental adjustment factors that capture how climate shocks and ecological degradation compound capability gaps within each pillar, particularly for disadvantaged populations in high-vulnerability settings.

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