Global Wellbeing Economics Lab

Measuring Well-Being Beyond GDP: From Lifetime Income to Comprehensive Adjusted Indicators

The measurement of economic progress and human well-being has long been dominated by per capita gross domestic product (GDP). While GDP provides a convenient and widely available proxy for material living standards, it fails to capture many essential dimensions of a "good life," including health, longevity, inequality, education, and environmental quality. In a series of contributions (Fan et al., 2018; Bloom et al., 2021; Zhang et al., 2023, 2025, 2026), we proposed a family of indicators that retain the crucial advantages of per capita GDP, such as its clear economic interpretation and its availability over time and across countries, while incorporating several additional important dimensions of well-being. These indicators allow for a much more comprehensive assessment of economic well-being both over time and across countries.

In this database, we provide the datasets for all of these indicators such that other researchers, policymakers, and the wider public have easy access and can download and use the indicators for their own projects.

In the following, we provide a brief description of the indicators in ascending order of complexity.

Indicator Framework

  1. 1
    Lifetime IncomeLI

    LI = GDPpc × LEXP

    The simplest indicator in this framework is Lifetime Income (LI). It is defined as the product of per capita income – typically GDP per capita adjusted for purchasing power (GDPpc) – and life expectancy (LEXP) such that LI = GDPpc × LEXP. This measure captures the intuitive idea that well-being depends not only on how much income individuals earn in a given year, but also on how long they live. A country with high income but low life expectancy may offer fewer lifetime resources than a country with slightly lower income but significantly longer lives.

    The key advantage of LI as compared, for example, to the Human Development Index (HDI) lies in its simplicity and economic interpretability. It is expressed in monetary units and can be directly understood as the expected lifetime earnings of an average individual given current income and life expectancy. Moreover, it avoids arbitrary weighting schemes because the relationship between income and life expectancy follows directly from its definition. Compared to GDP, LI improves the measurement of well-being by incorporating longevity. Compared to composite indices such as the HDI, LI does not require combining heterogeneous dimensions using arbitrary weights, and it is not bounded between 0 and 1, allowing for clearer comparisons over time and across countries.

  2. 2
    Healthy Lifetime IncomeHLI

    HLI = GDPpc × HALE

    While LI accounts for the length of life, it does not distinguish between years lived in good health and years affected by illness or disability. This limitation is addressed by Healthy Lifetime Income (HLI), which replaces life expectancy with healthy life expectancy (HALE): HLI = GDPpc × HALE. HLI can be interpreted as the income earned during the years in which individuals are in good health.

    By incorporating health as an intrinsic dimension of well-being, HLI captures a critical aspect that GDP and even LI neglect. Empirical analyses show that countries with better population health tend to rank higher under HLI than under GDP-based measures, highlighting the importance of health investments for overall well-being.

  3. 3
    Inequality-Adjusted Healthy Lifetime IncomeIHLI

    IHLI = GDPpc × HALE × (1 − Gini)

    The next step in the progression addresses income inequality. Both LI and HLI rely on average income and therefore ignore how income is distributed within a society. To address this issue, Inequality-Adjusted Healthy Lifetime Income (IHLI) extends the HLI by incorporating a measure of income equality, typically derived from the Gini coefficient such that IHLI = GDPpc × HALE × (1 − Gini). Since the Gini is 0 in the case of perfect equality (everybody earning the same) and 1 in the case of perfect inequality (one person earning all income and the others none), IHLI weights down the well-being in a very unequal society.

    This reflects the idea that well-being depends not only on average outcomes but also on how evenly resources are distributed. A society with high average income but extreme inequality may provide lower well-being for the typical individual than a more equal society. Here, we refer to (1 − Gini) as the Equality Index. The main advantage of IHLI is that it captures three fundamental dimensions of well-being simultaneously: income, health, and equality. At the same time, it retains the desirable properties of earlier indicators: a clear economic interpretation, no reliance on arbitrary weights, and relatively low data requirements.

  4. 4
    Pollution-Adjusted Healthy Lifetime IncomePHLI

    PHLI = GDPpc × HALE × PollutionAdjustment

    While IHLI incorporates inequality, it does not account for environmental conditions. However, environmental quality plays a crucial role in determining both health outcomes and overall well-being. To address this, Pollution-Adjusted Healthy Lifetime Income (PHLI) modifies HLI by incorporating a measure of environmental quality, such as pollution intensity. The corresponding indicator is then calculated as PHLI = GDPpc × HALE × PollutionAdjustment. Conceptually, PHLI adjusts HLI downward in countries with high pollution levels, reflecting the negative impact of environmental degradation on well-being. This adjustment captures the idea that income earned in polluted environments may be much less enjoyable than income earned in cleaner settings.

    It is important to note in this context that a meaningful pollution adjustment should not be based on a pollutant that has mainly global effects and therefore impacts all countries irrespective of where emissions occur (such as CO₂ emissions); rather, it should be based on pollutants with more localized effects such as SO₂ emissions or soil sealing measures. For this purpose, we introduce and apply the Zhang et al. (2026) pollution adjustment factor, which is based on SO₂ emissions standardized by land area and mapped into the (0, 1] interval to ensure clear interpretability.

    The main advantage of PHLI is that it integrates environmental sustainability into the measurement of well-being while maintaining the parsimonious structure of the framework. Unlike many sustainability indices, which rely on large dashboards of indicators, PHLI incorporates environmental factors into a single, interpretable measure.

  5. 5
    Education-Adjusted Healthy Lifetime IncomeEHLI

    EHLI = GDPpc × HALE × EducationAdjustment

    Another important dimension of well-being is education, which influences individuals' opportunities, productivity, and quality of life. Education-Adjusted Healthy Lifetime Income (EHLI) therefore extends HLI by incorporating measures of educational attainment or human capital and is expressed as EHLI = GDPpc × HALE × EducationAdjustment.

    EHLI incorporates the idea that higher levels of education enhance the effective value of lifetime income by increasing individuals' capabilities and opportunities as it has been argued by Amartya Sen (1999) in his book Development and Freedom.

  6. 6
    Pollution- and Education-Adjusted Healthy Lifetime IncomePEHLI

    PEHLI = GDPpc × HALE × EducationAdjustment × PollutionAdjustment

    The next most complex indicator in this framework is Pollution- and Education-Adjusted Healthy Lifetime Income (PEHLI). This measure combines all previously discussed dimensions – income, health, longevity, education, and environmental quality – into a single metric: PEHLI = GDPpc × HALE × EducationAdjustment × PollutionAdjustment. PEHLI can be interpreted as the income earned over the years of healthy life, adjusted for both the quality of the environment and the level of education. It represents a holistic measure of well-being that incorporates key aspects of human development in a unified framework.

    The main advantage of PEHLI is its comprehensiveness combined with parsimony. Despite incorporating multiple dimensions, it remains relatively easy to compute and interpret, relying only on commonly available data. It avoids the pitfalls of multi-indicator dashboards, such as high data requirements, duplication of information, and lack of clear trade-offs between components. Empirical applications show that PEHLI can substantially alter country rankings compared to GDP. Countries with high pollution levels tend to rank lower, while those with strong education systems and cleaner environments rank higher.

  7. 7
    Pollution- and Education- and Inequality-Adjusted Healthy Lifetime IncomePEIHLI

    PEIHLI = GDPpc × HALE × EducationAdjustment × PollutionAdjustment × (1 − Gini)

    The most comprehensive indicator in this framework is Pollution- and Education- and Inequality-Adjusted Healthy Lifetime Income (PEIHLI), which is calculated as PEIHLI = GDPpc × HALE × EducationAdjustment × PollutionAdjustment × (1 − Gini). While this indicator incorporates all of the dimensions of well-being that we discussed above, it is also the most data intensive indicator such that it is most prone to problems with missing data.

Advantages Over Traditional Indicators

Across all levels of complexity, the indicators we propose share the following key advantages over traditional measures such as GDP and the HDI:

Economic Interpretability

All indicators are expressed in monetary units, making them directly comparable to other economic variables. This contrasts with the HDI, which produces a unit-free index that lacks a clear economic meaning.

No Arbitrary Weights

The structure of the indicators follows directly from their definitions, eliminating the need for subjective weighting schemes that are common in composite indices.

Multidimensional Coverage

The framework progressively incorporates key dimensions of well-being – health, inequality, education, and environment – while maintaining coherence and interpretability.

Comparability and Scalability

Unlike bounded indices such as the HDI, these indicators are not restricted to a fixed range, allowing for meaningful comparisons across countries and over time.

Low Data Requirements

Despite their richness, the indicators rely on widely available macroeconomic and demographic data, making their calculations feasible even for statistical agencies with limited resources.

Policy Relevance

By integrating multiple dimensions into a single measure, these indicators provide clearer guidance for policymakers than fragmented dashboards or purely income-based metrics.

References

  1. [1]Zhang, J., Prettner, K., Bloom, D. E., Chen, S., & Lutz, W. (2026). Measuring well-being beyond GDP: Pollution– and education-adjusted healthy lifetime income estimates for 175 countries. SSRN Working Paper.
  2. [2]Zhang, J., Chen, S., & Prettner, K. (2025). Measuring "high-quality development" and progress toward "common prosperity" in China. Social Indicators Research, 179(1), 153–200.
  3. [3]Zhang, J., Prettner, K., Chen, S., & Bloom, D. E. (2023). Beyond GDP: Using healthy lifetime income to trace well-being over time with estimates for 193 countries. Social Science & Medicine, 320, 115674.
  4. [4]Bloom, D. E., Fan, V. Y., Kufenko, V., Ogbuoji, O., Prettner, K., & Yamey, G. (2021). Going beyond GDP with a parsimonious indicator. Vienna Yearbook of Population Research, 19, 127–140.
  5. [5]Fan, V. Y., Bloom, D. E., Ogbuoji, O., Prettner, K., & Yamey, G. (2018). Valuing health as development: going beyond gross domestic product. BMJ, 363, k4371.

Data retrieved from the Global Wellbeing Economics Lab, globalwellbeingeconomicslab.org

Insightful introduction video of Prof. Dr. Klaus Prettner: https://youtu.be/j6p-k2ifv7I