3 Oct 2025
Gross Domestic Product (GDP), a traditional measure of economic advancement since World War II, is increasingly recognized as insufficient for assessing true societal well-being due to its failure to account for rising inequality, environmental degradation, and various forms of unhealthy growth. A novel methodology proposes a consolidated framework for social welfare by analyzing 213 existing indicators, identifying approximately 20 core conceptual components that can form the basis of a global consensus for a more comprehensive and sustainable measure of progress.
GDP, defined as the total value of goods and services produced in a country annually, became the primary economic progress indicator after World War II, based on the assumption that increased production leads to more jobs, higher living standards, and increased welfare. However, in the current Anthropocene era, GDP growth alone is insufficient and carries significant negative side effects.
An overreliance on GDP growth exacerbates income inequality, enriching the wealthy while leaving the poor behind, which in turn diminishes social trust and contributes to social and health crises. It also leads to environmental degradation, including climate change, biodiversity loss, and air and water pollution.
The United Nations has identified several types of dangerous growth, such as growth without employment, voiceless growth suppressing freedoms, ruthless growth increasing inequality, futureless growth depleting natural resources, and rootless growth escalating tensions and conflicts.
Researchers have proposed numerous alternative indicators over decades, including the Human Development Index (HDI), happiness indices, Genuine Progress Indicator (GPI), and quality of life frameworks, often as multi-component social, environmental, and economic indices. A major issue is the vast number and fragmentation of these indicators, with each country, organization, or researcher proposing their own, which hinders widespread adoption.
Despite its recognized shortcomings, GDP remains the dominant metric because of its simplicity, single numerical value, and universal familiarity among countries.
A research paper aimed to identify commonalities and shared concepts among diverse welfare indicators. The authors compiled a large database of 213 welfare indicators and analyzed their constituent components using a natural language processing model called SpaCy, which converts words and sentences into vectors in a multi-dimensional space to measure semantic similarity.
Using SpaCy, components with semantic similarities were clustered together (e.g., social participation, civic engagement, community involvement). A summarizing component was then algorithmically or manually defined for each cluster.
The study found that approximately 20 components are sufficient to cover a substantial portion of the shared content and similarities among existing indicators. Increasing the number beyond 20 yields diminishing returns, as the added value does not justify the increased complexity and cost.
Based on these findings, a 20-component index was designed as a potential foundation for global consensus on welfare measurement. Five high-performing existing indicators, such as Canada's Quality of Life framework and Iceland's welfare indicators, were also identified.
The research revealed that the popularity of an indicator does not necessarily correlate with its comprehensiveness; for example, the Human Development Index and Sustainable Development Goals are well-known but are not always the best at integrating welfare concepts.
Although there is a wide variety of indicators, analysis shows a broad conceptual similarity, indicating a general agreement on key aspects like education, health, inequality, social participation, environmental quality, and living standards.
There is a need to balance comprehensiveness with practicality; a 20-component index is manageable while still offering a good interpretation of welfare.
The ongoing debate questions whether a single, simple indicator or a multi-indicator dashboard is preferable. A single indicator simplifies policy-making, while a dashboard offers more detail to pinpoint problem areas. A combination of both approaches is likely the most effective solution.
Moving beyond GDP necessitates a paradigm shift, requiring a shared understanding of what constitutes a good life, including human well-being, environmental health, equality, social justice, and sustainability for future generations. GDP, a simple economic output measure, fails to capture these dimensions.
A new system is required, encompassing indicators, models, and policies, all aligned towards the overarching goal of sustainable and inclusive welfare. The current research is a significant step in this direction, offering a roadmap for global consensus by identifying commonalities among existing indicators.
GDP is likened to measuring only a car's speed without knowing road safety, fuel levels, or the destination. A comprehensive dashboard is needed to show speed, fuel, engine health, road conditions, and passenger welfare to ensure true progress.
The methodological problems of data collection and representation are not unique to GDP, extending to other economic data like US inflation figures, which have recently undergone controversial revisions due to low corporate response rates in surveys, affecting their accuracy and representativeness.
To address data collection and timeliness issues, the US Department of Commerce and the US government are exploring bringing macroeconomic data onto blockchain technology. This initiative aims to securely provide critical information from the Bureau of Economic Analysis (BEA) on-chain.
Key data points such as GDP (annualized change), PCE price index (monthly and annual), and real final sales to private domestic purchasers (and their percentage change) are being released on blockchain platforms. This innovation offers advantages in data collection and reduced delays, with initial support across ten blockchain ecosystems and growing user-driven network support.
Moving beyond GDP is not merely about creating a new indicator; it represents a paradigm shift towards a shared understanding of what constitutes a good life, encompassing human well-being, environmental health, equality, social justice, and sustainability for future generations.
| InsightCategory | KeyInsight |
|---|---|
| Critique of GDP | GDP solely measures economic output, failing to account for income inequality, environmental degradation, and overall societal well-being. |
| Challenges of Alternative Metrics | The proliferation and fragmentation of alternative welfare indicators hinder global consensus and widespread adoption, leaving GDP dominant. |
| Consensus Methodology | A method using NLP (SpaCy) on 213 welfare indicators identified shared components by clustering semantic similarities. |
| Optimal Welfare Framework | Approximately 20 core conceptual components are sufficient to construct a comprehensive yet manageable index for global welfare measurement. |
| Paradigm Shift Requirement | Moving beyond GDP demands a fundamental shift towards a shared understanding of a 'good life' encompassing well-being, equality, and sustainability. |
| Macroeconomic Data Challenges | Methodological issues, such as low survey response rates, lead to inaccuracies and frequent revisions in critical macroeconomic data like inflation. |
| Blockchain Solution for Data | The US government is deploying macroeconomic data (e.g., GDP, PCE) on blockchain to enhance security, transparency, accuracy, and timeliness of data collection. |
