Physical Quality of Life Index (PQLI): Measuring Basic Welfare

Gross Domestic Product tells you how much a country produces. It does not tell you whether people are living longer, whether babies are surviving their first year, or whether children can read a newspaper. By the 1970s, several economists had grown uneasy with using income alone as a stand-in for human welfare. Out of that unease came the Physical Quality of Life Index (PQLI), one of the earliest attempts to build a composite index that measures how people actually live, not just how much money moves through an economy.

Why economists looked beyond national income 🔗

The PQLI was developed in the mid-1970s by economist Morris David Morris, who worked with the US Overseas Development Council to design a measure that could capture social progress independent of GNP per capita. His 1979 monograph, Measuring the Condition of the World’s Poor, argued that per capita income figures hide as much as they reveal. Two countries can have similar incomes yet very different outcomes in health and education, especially when income is concentrated among a small elite rather than spread across the population.

Morris wanted a measure that was simple enough to calculate for any country, used data that governments already collected, and focused strictly on outcomes rather than inputs like spending or policy intentions. That is how he arrived at three indicators, all of which reflect whether basic human needs are actually being met.

The three indicators behind PQLI 🔗

The PQLI averages three separate measures, each converted to a common 1 to 100 scale where 1 represents the worst recorded performance across countries and 100 represents the best. This standardisation is what allows very different kinds of data, like years of life and percentage points of literacy, to be combined into a single number.

Life expectancy indicator (LEI) 🔗

This is the average number of years a person is expected to live from birth. It works as a rough proxy for nutrition, sanitation, healthcare access, and general living conditions across a population, since all of these factors shape how long people survive.

Infant mortality indicator (IMI) 🔗

This measures the number of infant deaths per 1,000 live births. A lower number means better performance, since fewer infants are dying before their first birthday. Infant mortality is considered a particularly sensitive indicator because it responds quickly to changes in maternal health, nutrition, and access to basic medical care.

Basic literacy indicator (BLI) 🔗

This is the percentage of people above a set age threshold who can read and write. In the Indian context, this mirrors how the Census of India defines literacy: anyone aged seven or above who can read and write with understanding in any language is counted as literate. Literacy captures access to education and, indirectly, future earning potential and civic participation.

All three indicators carry equal weight in the final index. Morris deliberately avoided giving any one factor more importance than another, since he wanted a measure that was transparent and easy to replicate rather than one built on debatable value judgments about which outcome matters most.

How the PQLI is calculated 🔗

Once each indicator is standardised to the 0-100 scale, the PQLI is simply the arithmetic mean of the three:

PQLI = (LEI + IMI + BLI) / 3

Suppose a country’s Life Expectancy Indicator is rated at 75, its Infant Mortality Indicator at 40, and its Basic Literacy Indicator at 65. Adding these and dividing by three gives a PQLI of 60. A score closer to 100 signals strong performance in meeting basic welfare needs, while a lower score points to gaps in health or education that policymakers may need to address.

The appeal of this formula lies in its simplicity. Unlike more complex indices that require weighting schemes or judgment calls about which variables to prioritise, the PQLI can be calculated with data that most national statistical offices already publish, which made it genuinely usable for comparing dozens of countries at a time when computing power and data infrastructure were far more limited than today.

What the PQLI reveals within India 🔗

The PQLI is not only useful for comparing countries. Researchers have applied the same method to compare states within India, and the results are revealing. States like Kerala consistently score high because of strong outcomes in health infrastructure and near-universal literacy, while some other states lag behind due to weaker literacy rates and higher infant mortality. This state-wise variation is a good illustration of why the index matters: two states can have comparable per capita income, yet one delivers far better basic welfare outcomes than the other.

India’s own literacy data shows how uneven progress can be even decades after independence. National literacy stood at just over 74 percent as of the last full census, with a wide gap between the highest and lowest performing states, and an even wider gap between male and female literacy rates. Because the PQLI folds literacy directly into its score, it exposes these disparities more directly than an income-based measure ever could.

Strengths and limitations of the PQLI 🔗

The biggest strength of the PQLI is also its biggest limitation: simplicity. Because it relies on only three indicators, it is easy to calculate and interpret, which is exactly why it became popular with development economists working in resource-constrained settings. It shifted the conversation in development economics away from pure income growth and toward outcomes that directly affect ordinary people’s lives.

At the same time, critics point out real weaknesses. Life expectancy and infant mortality are closely correlated, since both largely reflect the same underlying healthcare and nutrition conditions, so the index effectively counts health twice while giving education just one-third weight. The PQLI also leaves out income distribution, environmental quality, gender gaps, and political freedoms, all of which shape quality of life in ways that health and literacy figures alone cannot capture.

These gaps are part of why the Human Development Index (HDI), introduced by the United Nations Development Programme in 1990, eventually became the more widely used measure. The HDI builds on the same basic idea as the PQLI but adds an income dimension and uses updated methodology, giving a fuller picture of development that still draws directly from the logic Morris first proposed. In that sense, the PQLI did not disappear so much as evolve into something more comprehensive.

Why the PQLI still matters in social science today 🔗

Even though the HDI and other composite measures have largely taken its place in international rankings, the PQLI remains a foundational teaching example in data analysis and development studies. It shows students how a composite index is built from the ground up: pick relevant indicators, standardise them onto a common scale, decide how to weight them, and combine them into a single, comparable number. Every modern index, from the HDI to the Multidimensional Poverty Index, follows this same basic logic, just with more indicators and more sophisticated weighting.

The PQLI also carries an important methodological lesson that goes beyond development economics: numbers can only tell you what you chose to measure. A country or state can score well on health and literacy while still facing serious problems with inequality, environmental degradation, or political freedom, none of which show up in a PQLI score. Understanding this limitation is just as important as understanding the formula itself, especially for anyone learning to construct or interpret composite indices in social science research.

What do you think? 🔗

What do you think? If you were designing a welfare index for India today, would you still limit it to just three indicators, or would you add measures like access to clean water, gender equality, or environmental quality? And between a simple, easy-to-calculate index like the PQLI and a more complex one like the HDI, which do you think serves policymakers better when quick, comparable data is needed across many regions?

References

1. https://link.springer.com/rwe/10.1007/978-94-007-0753-5_2164
2. https://www.sciencedirect.com/science/article/abs/pii/0304400978900153
3. https://knowindia.india.gov.in/profile/literacy.php
4. https://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/recent_issues_pdf/2020/July/physical-quality-of-life-index-analysing-state-wise-level-of-development-in-india_July_2020_1593606303_4813705.pdf
5. https://www.oxfamindia.org/featuredstories/10-facts-illiteracy-india-you-must-know
6. https://www.thebillionpress.org/columns/society/keep-eye-human-development-indices-1303