Dynamic Human Potential Index (DHPI) and Predictive Development Economics (PDE). Toward a New Metrics System for Measuring Development and Guiding Public and Private Investments

1. Why the World Needs a New Development Lens

What gets measured gets managed, and what gets predicted gets transformed. Development measurement has always shaped development policy and guided how public and private policy decisions are made. What societies choose to measure becomes what governments care about and budget for, what investors price, what donors prioritize, and what citizens use to judge progress. The warning from the Commission on the Measurement of Economic Performance and Social Progress remains central: what we measure affects what we do, and flawed measurement can distort choices between growth, distribution, sustainability, and well-being (Stiglitz, Sen, & Fitoussi, 2009).

Today, the problem is not that GDP or HDI are playing a key role in enlightening development experts for informed decisions; they certainly remain useful. GDP tells us about the production, fiscal capacity, market size, and macroeconomic performance of an economy in a given time, building on the past/cent data. Complementarily, HDI reminds us that development is about people, not commodities alone. But both metrics are insufficient for today’s investment sequencing or simultaneity questions. A country can grow without creating enough jobs; a child can attend school without learning; a household can receive a transfer yet remain exposed to climate shocks, violence, poor roads, weak markets, and digital exclusion (UNDP, 2024; World Bank, 2024).

A new development metric should therefore ask a different question: not only how developed a country is today, but what human potential exists, where it is constrained, and which investments are most likely to unlock it over time. That is the purpose of the proposed Dynamic Human Potential Index (DHPI), supported by Predictive Development Economics (PDE).

2. A Reminder on the Birth of GDP: From Kuznets to Global Macroeconomic Governance

The modern statistical architecture behind GDP began in the crisis atmosphere of the Great Depression. A Belarusian-American economist, Simon Kuznets, working with the National Bureau of Economic Research (NBER) and the U.S. Department of Commerce, is widely credited with developing the first modern national income accounts that became the foundation for Gross National Product (GNP) and later Gross Domestic Product (GDP). In the 1934 report National Income, 1929–1932, submitted to the U.S. Senate, Kuznets supervised the first comprehensive estimates of U.S. national income during the Depression (Kuznets, 1934; NBER, 1934).

While Kuznets did not invent GDP as a moral measure of national success, he built a quantitative instrument to understand the scale and contraction of economic activity. He also warned against confusing national income with welfare, famously observing that the welfare of a nation can scarcely be inferred from a measurement of national income (Kuznets, 1934). In other words, the first architect of modern national income accounting already understood the limits of the new tool by then.

GDP has become powerful because it solved urgent policy problems during World War II and the post-war reconstruction era. National accounts helped governments plan production, mobilize resources, manage demand, and compare economic performance. After the 1944 Bretton Woods Conference established the IMF and the International Bank for Reconstruction and Development, macroeconomic measurement became embedded in global economic governance, debt analysis, aid allocation, and national planning and budgeting (Bretton Woods Conference, 1944; World Economic Forum, 2021).

The strength of GDP is also its weakness. It captures market production but not unpaid care work, environmental degradation, inequality, insecurity, social cohesion, or whether today’s production expands or erodes tomorrow’s capabilities. The Stiglitz-Sen-Fitoussi report and the OECD’s Beyond GDP agenda have therefore argued for richer dashboards that include quality of life, sustainability, distribution, and subjective well-being (OECD, 2018; Stiglitz et al., 2009).

With the measurement and simplification capabilities introduced through GDP, the world can now observe, compare, and classify economies through a common production-based lens. In today’s nominal GDP ranking, the United States, China, Germany, Japan, and the United Kingdom appear as the five largest economies, while Tuvalu, Nauru, the Marshall Islands, Kiribati, and Palau appear among the five smallest sovereign economies in terms of GDP. Between these extremes lies a rich continuum of countries, territories, and economies across all continents, each with its own production structure, population scale, development pathway, and welfare realities. Yet this is where the limitation of GDP begins, it tells us the size of economic output but not whether that output expands human potential, reduces vulnerability, or improves people’s lived capabilities Table 1

3. HDI, the People-Centered Correction

In 1990, the United Nations Development Programme (UNDP) introduced the Human Development Report and the Human Development Index (HDI). Associated with Mahbub ul Haq and Amartya Sen’s capability approach, HDI shifted the development debate from output to people. The first Human Development Report stated that development is a process of enlarging people’s choices, including the ability to live a long and healthy life, acquire knowledge, and access resources required for a decent standard of living (UNDP, 1990).

HDI was a major intellectual correction as it challenged the notion that income alone defines progress and gave governments a simple composite measure combining life expectancy, education, and income. Later measures, including inequality-adjusted HDI and the Multidimensional Poverty Index, deepened the analysis by showing how deprivations overlap within households and societies (UNDP, 2010; UNDP & OPHI, 2024).

However, HDI remains mainly descriptive and national-average oriented, it also tells us whether a country is doing better or worse on selected outcomes, but it does not fully answer the investment questions facing ministers of finance, planners, investors (public and private), and communities, which investments should be prioritized, for whom, where to allocate more to others, in what sequence, and under what future scenarios?

4. Frontier Knowledge Gaps- We Rarely Measure Potential

Globally, the development landscape is being reshaped by demographic change, climate risk, digital transformation, and geopolitical fragmentation. The Human Development Report 2023/24 warns that uneven development progress, inequality, political polarization, and weak cooperation are producing global gridlock (UNDP, 2024). At the same time, the global economy is becoming increasingly knowledge-intensive.

The existing development measurement indicators are static by design; they measure current or past outcomes more effectively than emerging opportunities. The fundamental questions they solve, which are also essential are: (i) how wealthy/poor is a country, (ii) how many years of schooling have children completed, (iii) what is GDP growth? The World Bank’s Human Capital Index estimates the productivity a child born today can expect to attain by age 18 under current health and education conditions, which is a major step forward, but it is not yet a full investment-routing system (Kraay, 2019; World Bank, 2018).

Most composite indicators remain weakly spatial as they often understate subnational/regional inequality, rural isolation, climate exposure, market distance, infrastructure density, conflict risk, service quality, among others. This is especially problematic in countries where national averages hide deep territorial divides (World Bank, 2024).

Measurement systems often separate human development from productive transformation. The case in point, education, health, and social protection are measured in one policy space (social sectors), while industrial policy, firm productivity, trade complexity, infrastructure, and private investment are measured in another (productive sectors). Yet development depends on whether human capability connects with productive opportunity. Economic complexity research shows that societies become richer when they accumulate productive knowledge and diversify into more complex activities (Hausmann et al., 2014; Harvard Growth Lab, 2026).

Current tools insufficiently integrate risk and resilience. Climate shocks, pandemics (they are rapidly emerging with a widening effect on economies), food price volatility, conflict, inflation, tight financing, and demographic pressure can rapidly destroy human capital gains. Recent data show that Sub-Saharan Africa’s recovery from the COVID-19 pandemic remains fragile, with subdued growth and social pressures linked to poverty, scarce opportunities, and governance challenges (IMF, 2024; World Bank, 2024).

The development metrics rarely provide a computational investment prescription. However, governments and investors need to know when the highest marginal return lies in early childhood nutrition, rural roads, girls’ secondary education, agricultural storage, clean energy, digital identity, teacher quality, industrial parks, climate insurance, or adaptive social protection systems, thus, a future-facing metric must become a decision-support engine, not only a ranking table.

5. Introducing the Dynamic Human Potential Index

The Dynamic Human Potential Index can be defined as a multidimensional, time-sensitive, and spatially disaggregated index measuring the capacity of people and communities to develop, use, protect, and expand their capabilities under changing economic, social, institutional, technological, and environmental conditions.

In this paper, I argue that DHPI differs from HDI in four ways.

• o First, it is dynamic, tracking trajectories, risks, and expected future states, not only current achievements.
• o Second, it is investment-sensitive in estimating which intervention combinations are likely to unlock the greatest human potential/potentials.
• o Third, it is spatial and distributional by generating statistics at national, regional, district, community, household, and demographic group levels.
• o Fourth, it is predictive and actionable, as it is embedded in a PDE model that forecasts scenarios and supports public and private investment decisions.

In short, while HDI asks a question of how are people doing, while DHPI will looks into what could people become if the right constraints were removed, and where should society invest first? This shift changes or complements the existing metric from a thermometer to a forward-looking navigation system.

6. Attempting to Define Predictive Development Economics (PDE)

Predictive Development Economics can be defined as an applied field combining development economics, data science, artificial intelligence, behavioral insights, geospatial analytics, climate modeling, and political economy to forecast development pathways and guide investment allocation under uncertainty and multilayer constraints.

PDE will not replace human judgment, but it will augment and enlighten it with the support of Artificial Intelligence (AI) computational powers and predictive analytics to help governments identify patterns in large datasets, forecast service demand, detect anomalies, model expenditure scenarios, and improve resource allocation. the recent work of the Organisation for Economic Co-operation and Development (OECD) on AI in public financial management highlights uses in forecasting, budgeting, fraud detection, and decision support, while emphasizing safeguards, institutional capacity, and trustworthy implementation (OECD, 2024).

In development policy, PDE should help answer five practical questions: (i) where is latent human potential highest but blocked; (ii) which constraints suppress that potential; (iii) which interventions produce the highest combined social and economic return; (iv) what should be sequenced -done first, second, and third...; and (v) how climate, demography, conflict, debt, or markets could alter expected returns.

7. A Proposed DHPI Computational Matrix

DHPI should not be a single simplistic score. It has to be a modular system with core pillars, predictive variables, and investment levers. The model should transparently generate not only a national score but also a DHPI Constraint Map that shows which constraints most reduce human potential in each geography and population group. Table 2

8. Use Cases- Sub-Saharan African Economies

Sub-Saharan Africa is the region where a DHPI-PDE framework may be most urgently needed. The World Bank’s Poverty, Prosperity, and Planet Report notes that in 2024, Sub-Saharan Africa accounted for 16 percent of the world’s population but 67 percent of the people living in extreme poverty. Growth in the region is insufficiently poverty-reducing. World Bank analysis reported in Africa’s Pulse indicates that a 1 percent increase in per capita GDP is associated with only about a 1 percent reduction in extreme poverty in Sub-Saharan Africa, compared with about 2.5 percent elsewhere. This weak poverty-growth elasticity enhances the case for why DHPI matters. It would push policymakers to ask not only whether GDP is growing, but whether growth is occurring in sectors, places, and forms that unlock human potential for the majority.

Sub-Saharan African countries also confront infrastructure and digital deficits. While progress has been recently made in digital access, there remains, however, a major gap between broadband coverage and actual mobile internet use, with affordability and skills limiting adoption. Electricity access remains among the major bottlenecks, with around 600 million people in Sub-Saharan Africa lacking electricity in 2023, according to the International Energy Agency (IEA, 2024). In this context, PDE would help countries move from sector-by-sector planning to portfolio planning by combining human capital, infrastructure, climate adaptation, productive transformation, and social protection in one investment logic.

Country Lens:

• o Madagascar is a compelling case for DHPI because it combines rich natural capital, a young population, biodiversity, agricultural potential, tourism assets, and significant human development constraints. The World Bank report shows that Madagascar’s population was estimated at 31.9 million in 2024, with persistently high poverty, affecting more than three-fourths of the population, low human capital, weak infrastructure, recurrent cyclones and droughts, high vulnerability to climate and global shocks and a lack of domestic adaptation capacity due to constrained fiscal space (World Bank, 2026). A DHPI diagnosis for Madagascar would likely identify chronic undernutrition, weak foundational learning, rural isolation, unreliable energy, low agricultural productivity, limited transport connectivity, climate exposure, underdeveloped financial inclusion, and weak capacity to convert natural wealth into broad-based livelihoods.
• o A PDE model could test investment bundles such as nutrition plus early learning plus adaptive social protection in high-stunting districts; rural roads plus irrigation plus storage plus market information in agricultural corridors; renewable mini-grids plus digital finance plus small enterprise support in underserved growth nodes; cyclone-resilient schools and clinics plus shock-responsive cash transfers in high-risk coastal zones; and tourism, biodiversity, and local livelihood packages where conservation and income generation can reinforce each other.
• o Rwanda offers a different but equally important DHPI case. Over the past three decades, the country has achieved strong growth, institutional coordination, digital ambition, and service delivery improvements, but still faces challenges in translating growth into broad-based poverty reduction and enough productive jobs. Rwanda's strategic development ambitions and policy commitments aspire to reach middle-income status by 2035 and high-income status by 2050, but job creation and productivity remain core challenges. A DHPI model for Rwanda would focus strongly on converting human capital into higher-productivity employment. Rwanda’s Human Capital country brief shows progress in primary completion, lower secondary enrollment, youth literacy, immunization, and life expectancy, while also pointing to youth NEET challenges, adult unemployment, and labor market constraints (World Bank, 2024).
• o A PDE investment typology for Rwanda could include skills-to-jobs forecasting linked to agro-processing, logistics, digital services, tourism, construction, and green technologies; secondary cities productivity packages combining housing, transport, digital access, local enterprise finance, and district-level labor market analytics; care economy investments to increase women’s economic participation; climate-smart value chains in rural areas; and digital public infrastructure linked to private innovation ecosystems.

9. Research Agenda and Frontier Knowledge Gaps

Before the DHPI and PDE models are implemented from concept to practice, several gaps need to be addressed through rigorous research, testing, and validation.

• o First is the basic calculus problem -weighting problem by defining how health, learning, income, resilience, climate exposure, and agency are weighted.
• o Second is the latent potential problem, by defining the approach by which societies measure what people and places could become, not only what they are today.
• o Third is the productive transformation problem, which relates to how human capital metrics can be linked to firm productivity, economic complexity, and labor demand forecasting to produce reliable and significant results. Thanks to Monte Carlo simulations and advances in computing power.
• o Fourth is the complementarity problem, which should display combinations of interventions that produce nonlinear gains. The simple analogy is that education without jobs may disappoint, but education plus electricity, transport, finance, and productive firms may transform livelihoods.
• o Fifth is the uncertainty problem, which forecasts how climate shocks, conflict, demographic change, disease outbreaks, commodity prices, social unrest (if any), and debt distress should be integrated into development predictions. Sixth is the governance problem, raising the critical aspect of how ethical AI architecture can prevent bias, data extraction, and technocratic decision-making disconnected from people.
• o The seventh is the country typology problem relating to how to structure DHPI as a useful model ready to generate tailored investment recommendations according to country-specific context, e.g landlocked economies, island states, fragile settings, resource-rich countries, agrarian economies, urbanizing economies, climate-vulnerable territories, and reform-oriented countries with implementation capacity.

These initial challenges should not stop us from moving forward with testing new approaches to development measurement. Instead, they should guide the assumptions, modelling choices, and computational design needed to ensure that DHPI and PDE are practical, credible, and useful for development policy now and in the future.

10. A Call for a New Development Coalition

The world does not need another index for ranking countries only. It needs a new generation of metrics that can help societies make better choices. GDP helped organize production, and HDI helped humanize development. The new measures, DHPI and PDE, could help unlock human potential under uncertainty. For low-income countries and Sub-Saharan Africa in particular, the stakes are high. The future will be shaped by whether young populations can become healthy, skilled, connected, resilient, and productively employed. This will not happen through growth statistics alone, but it will require robust and credible predictive, integrated, and ethical investment systems that identify where potential is blocked and how to release it.

The proposal is therefore an invitation: economists, social policy experts, statisticians, AI scientists, ministries of finance, planners, investors, civil society, universities, and communities should come together to build the DHPI-PDE model. The work should begin with pilots in diverse country contexts, including low-income, climate-vulnerable, landlocked, island, fragile, and reform-oriented economies.

The next frontier of development economics should not only ask how nations grow. It should inquire how people flourish, how societies anticipate risk, and how investment can be guided toward the highest human possibility. That is the promise of the Dynamic Human Potential Index and Predictive Development Economics.

Author's Note: The Dynamic Human Potential Index (DHPI) presented here is a conceptual proposal intended to stimulate discussion among economists, policymakers, data scientists, and development practitioners. Its purpose is not to replace GDP, HDI, MPI, or the Human Capital Index, but to explore whether the next generation of development metrics should move beyond measuring past achievements toward estimating a country's capacity for future holistic transformation.