A symbolic illustration of the complex journey to health, showing the connection between wealth, lifestyle choices, and environmental factors.

Can Money Buy You Health? Unpacking the Complex Link Between Income and Longevity

Lena Voss in Health & Wellness November 2025 • 4 min read.

"Explore how factors beyond just wealth impact life expectancy and well-being, challenging common assumptions."

For decades, data across various nations have indicated a parallel trajectory: as income, often measured by GDP per capita, rises, so does population health, typically indexed by mortality rates or life expectancy at birth (LEB). This seems intuitive; greater wealth should translate to better healthcare, nutrition, and living conditions. However, the interplay between these variables is far from simple, sparking debates and controversies.

Some researchers argue that cointegration models can effectively reveal the causal relationships between population health and income. These models attempt to demonstrate how variables move together over long periods, suggesting a linked relationship. But is it really that straightforward? Can we confidently say that income directly causes better health outcomes, or vice versa?

This article will explore the complexities of cointegration methodology, reveal statistical challenges and show how focusing too narrowly on economic indicators might overshadow other critical determinants of health. We will navigate these intricate debates, offering a clearer understanding of how income, health, and societal well-being truly intersect.

The Historical Quest to Understand Health and Income

A symbolic illustration of the complex journey to health, showing the connection between wealth, lifestyle choices, and environmental factors.

The relationship between health and living conditions has been a subject of speculation for centuries. Ancient Greek physician Hippocrates emphasized the importance of environmental factors like winds, water, and housing on the health of city dwellers, along with lifestyle choices. However, reliable, data-driven insights emerged much later.

In the mid-17th century, John Graunt and William Petty analyzed the Bills of Mortality in London, early forms of mortality statistics, to understand public health. They observed high death rates and linked them to overcrowding and exposure to smoke from coal and wood. Continuous migration from the countryside was necessary for London to maintain its population because deaths consistently outnumbered births.

1798: Malthus published "On Population," discussing population and income interaction, postulating higher income stimulates growth, reducing available resources.
19th-20th Centuries: Health progress occurred alongside income growth, measured by mortality decline and life expectancy rise.
Life Expectancy at Birth (LEB): An indicator using age-specific mortality rates, reflecting population health.

While Malthus's theories were later discredited, the rise of statistics allowed researchers to correlate income, population, and mortality rates. Data from the early 20th century, and even earlier for some countries, revealed that health improvements—measured by declining mortality rates and rising life expectancy—occurred alongside income growth, as indicated by increasing GDP per capita.

Rethinking the Link Between Income and Health

The relationship between income and health is undeniably intricate, shaped by a multitude of interwoven factors. While wealth can offer advantages, it's not a guaranteed ticket to longevity or well-being. A deeper understanding of these complexities is crucial for creating effective policies.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2407.15755,

Title: Income, Health, And Cointegration

Subject: econ.gn q-fin.ec

Authors: José A. Tapia Granados, Edward L. Ionides

Published: 22-07-2024

Everything You Need To Know

What is the Life Expectancy at Birth (LEB) and why is it important?

The Life Expectancy at Birth (LEB) is an indicator used to measure population health. It is calculated using age-specific mortality rates, providing a snapshot of how long a newborn is expected to live, on average, within a given population. Rising LEB signifies improvements in health conditions and healthcare access, indicating a healthier population. Conversely, a declining LEB may signal deteriorating health, potentially due to disease outbreaks, poor living conditions, or inadequate healthcare resources. Thus, LEB serves as a critical metric for tracking and comparing health outcomes across different countries and time periods, alongside measures like GDP per capita.

How did early researchers like John Graunt and William Petty contribute to our understanding of health and income?

John Graunt and William Petty made significant contributions to the understanding of health and its relation to environmental factors, primarily through their analysis of the Bills of Mortality in London. They observed high mortality rates, especially in overcrowded areas with significant exposure to smoke from coal and wood. Their work marked an early, data-driven attempt to link living conditions with health outcomes. Their findings were essential for recognizing the impact of environmental issues and the need for better public health measures. They highlighted the importance of urban planning and environmental quality on health.

What are cointegration models, and what challenges do they present in analyzing the relationship between income and health?

Cointegration models are statistical tools that researchers use to analyze the relationships between variables over long periods. In the context of health and income, these models try to determine how population health, often reflected in metrics like Life Expectancy at Birth (LEB), moves in relation to income, typically represented by GDP per capita. However, one significant challenge with cointegration models is establishing causality: it's difficult to determine whether income directly causes better health, or if better health enables higher income, or if external factors influence both. Focusing too narrowly on economic indicators can overshadow other critical determinants of health, leading to incomplete conclusions and potentially ineffective policies.

Why is the relationship between income and health considered complex, and what factors contribute to this complexity?

The relationship between income and health is complex because it is shaped by a multitude of interwoven factors that extend beyond simple economic indicators. While greater wealth can offer advantages like better healthcare and nutrition, it does not guarantee longevity or well-being. Other critical factors include access to quality healthcare, lifestyle choices, environmental conditions, and social determinants of health, such as education and community support. These factors can counteract the positive effects of income, creating a situation where higher income does not always translate into better health outcomes. Therefore, a deeper understanding of these complexities is crucial for creating effective policies that improve population health.

How did Malthus's theories influence early thinking about the connection between income and population, and what happened to those theories later?

In 1798, Malthus published "On Population," which discussed the interaction between population and income. He proposed that higher income could stimulate population growth, ultimately reducing the available resources per capita. However, these theories were later discredited as they did not fully account for technological advancements, changes in social structures, and improvements in healthcare. While Malthus's theories initially sparked interest, subsequent research using data from the 20th century and earlier, revealed that health improvements—indicated by declining mortality rates and rising Life Expectancy at Birth (LEB)—occurred alongside income growth, as indicated by increasing GDP per capita. This suggested that other factors and dynamics not considered by Malthus were at play.