Measuring Regional Imbalance in India: Methods and Indicators

Ask an economist how unequal India really is, and the honest answer is: it depends which ruler you use. A rupee-income ruler tells one story. A schooling-and-sanitation ruler tells another. And a rural-versus-urban ruler tells a third. Regional imbalance in India is not a single number, it is a bundle of overlapping gaps, and measuring it properly means knowing which indicator to reach for and why. This post walks through the main tools economists and policymakers use to size up how unevenly development is spread across Indian states and districts, and what the latest data actually shows.

Why measurement comes before policy 🔗

You cannot fix what you cannot quantify. Before any Finance Commission grant, any special industrial package, or any centrally sponsored scheme can be designed for a lagging region, someone has to establish two things: how large the gap is, and along which dimension it is widest. A textbook definition from an IGNOU study unit on balanced regional growth frames regional imbalance as the coexistence of relatively developed states such as Gujarat, Maharashtra, Punjab, and Tamil Nadu alongside economically backward states such as Bihar, Chhattisgarh, and Madhya Pradesh. That framing is intuitive, but converting it into policy requires hard numbers, not impressions.

Income-based indicators 🔗

Income remains the most commonly used yardstick because it is relatively easy to collect and compare across states over long time periods.

Per capita income and GSDP gaps 🔗

The simplest indicator compares each state’s per capita Net State Domestic Product (NSDP) with the national average. A detailed working paper from the Economic Advisory Council to the Prime Minister tracked this measure from 1960-61 to 2023-24 and found some striking divergences. Bihar’s relative per capita income has collapsed from about 70 percent of the national average at independence-era levels to roughly 33 percent today. Haryana, in contrast, now sits at nearly 177 percent of the national average, having overtaken Punjab, whose share slipped from 170 percent in the 1970s to around 107 percent now. West Bengal, once the country’s third-largest state economy, has fallen from 10.5 percent of India’s GDP in 1960-61 to just 5.6 percent today. These are not small statistical wobbles, they represent decades of divergent industrial and agricultural trajectories.

The Gini coefficient of regional income 🔗

Where per capita income shows the gap between two states, the Gini coefficient summarises inequality across all states in a single score between 0 (perfect equality) and 1 (total inequality). Applied at the national level to household earnings, India’s overall Gini has hovered around 0.42 in recent years, alongside a Theil index of roughly 0.33, both suggesting persistent, not narrowing, concentration of income gains. The same statistical tool can also be applied purely to state-level per capita GSDP figures, which is closer to what “regional” Gini actually measures in growth economics: it strips out within-state inequality and isolates how unevenly output itself is distributed across India’s geography.

Population and poverty-based indicators 🔗

Income averages can mask how many people actually live in deprivation. Two indicators fix that.

Share of population in low-income regions 🔗

This measure asks a more human question: not “how poor is the average state” but “how many Indians live in the poorest states.” Because Bihar, Uttar Pradesh, Madhya Pradesh, and a handful of other lagging states are also India’s most populous, a large share of the country’s population is concentrated in low-income regions even when the number of “backward” states looks small on a map. This is why regional policy in India cannot simply target geography, it has to reckon with sheer demographic weight.

The Multidimensional Poverty Index (MPI) 🔗

Income alone misses a lot: a household can have cash income yet lack clean cooking fuel, sanitation, or years of schooling. The National MPI, built by NITI Aayog with technical support from the UNDP and the Oxford Poverty and Human Development Initiative, captures deprivation across health, education, and standard of living using twelve indicators. Bihar had the highest MPI value in the country in 2015-16, but it also recorded the fastest absolute improvement, with its MPI score nearly halving by 2019-21. A subsequent NITI Aayog discussion paper estimated that close to 25 crore Indians escaped multidimensional poverty between 2013-14 and 2022-23, with Uttar Pradesh and Bihar accounting for the largest numbers of people lifted out. The MPI is particularly useful for regional imbalance studies because it can be disaggregated down to the district level, letting planners see pockets of deprivation that a state-level income figure would smooth over entirely.

The rural-urban divide 🔗

Regional imbalance is not only about which state you live in, it is also about whether you live in a city or a village. The Household Consumption Expenditure Survey, summarised by the Ministry of Statistics and Programme Implementation, put average monthly per capita expenditure at roughly Rs 4,122 in rural India against Rs 6,996 in urban India for 2023-24. The gap has been narrowing, down from about 84 percent in 2011-12 to roughly 71 percent more recently, but it remains substantial. Economists track this ratio over time as a proxy for whether growth is trickling down to the countryside or staying concentrated in metros. It is also a useful cross-check on state-level figures: a state can look prosperous on average NSDP while hiding a wide gulf between its capital city and its rural districts.

What a table of the numbers looks like 🔗

Labour productivity as an indicator 🔗

A less discussed but equally important measure is labour productivity, typically calculated as output per worker in a given sector or state. States with a larger share of workers in high-productivity manufacturing and services will naturally show higher per capita income than states where most workers remain in low-productivity agriculture. This is one reason Kerala, despite modest industrial output, ranks well on human development, while some agriculturally dominant states lag on income despite decent food output. Productivity gaps also explain migration patterns: when out-migration states cannot generate remunerative non-farm jobs, workers move toward states where labour productivity, and therefore wages, is higher. Research summarised on the policy portal Ideas for India has linked exactly this pattern to the return migration seen after pandemic-era lockdowns, when workers who had gone back to villages found local labour markets unable to absorb them.

Composite and alternative measures 🔗

Because no single indicator tells the whole story, researchers increasingly build composite indices. The Human Development Index approach, applied at the state level, blends income, education, and health into one score. More novel approaches use satellite data: a study published via PMC used night-time light intensity as a proxy for economic activity at the district level, computing Gini coefficients from light data to estimate regional inequality where formal GSDP figures are unavailable or unreliable at the sub-state level. These composite and proxy measures matter because India’s official statistical machinery does not always produce timely district-level income data, and light-based or survey-based proxies help fill that gap between census years.

What the numbers say about India today 🔗

Put the indicators together and a consistent picture emerges. OECD’s economic outlook on India points to persistent infrastructure bottlenecks, unreliable electricity, weak transport networks, and limited digital access as forces that continue to widen regional disparities even as the national economy grows. Delhi’s per capita income runs at roughly two and a half times the national average, while Bihar sits below a third of it. Southern and western states, led by Maharashtra, Gujarat, Karnataka, and Tamil Nadu, together account for a large and growing share of national GDP, while eastern states such as Bihar and West Bengal have either stagnated or declined in relative terms since the 1960s. None of this is unique to India; the OECD’s own Regions at a Glance work notes that within-country regional GDP per capita differences are often larger than differences between entire countries. What makes India’s case notable is scale: the gap between its richest and poorest large states rivals the gap between some of the world’s richest and poorest nations.

Why the choice of measure matters for policy 🔗

Different indicators point policymakers toward different remedies. A pure income gap argues for industrial incentives and infrastructure investment in lagging states. A high MPI score argues for targeted spending on health, sanitation, and schooling, areas where Bihar’s rapid improvement shows that outcomes can shift faster than income. A wide rural-urban gap argues for rural infrastructure, non-farm employment schemes, and better market access for farmers. Relying on just one measure risks solving the wrong problem, for example pouring capital into industrial corridors while ignoring that the bottleneck is actually low school completion rates or poor last-mile connectivity. This is exactly why India’s National MPI, the EAC-PM’s income-based work, and consumption expenditure surveys are treated as complementary tools rather than substitutes for one another.

A quick way to remember the toolkit 🔗

• Income gap: per capita NSDP relative to the national average
• Distributional spread: Gini coefficient and Theil index across states
• Human deprivation: Multidimensional Poverty Index across health, education, living standards
• Spatial divide: rural versus urban consumption gap
• Efficiency gap: labour productivity by sector and state

What do you think? If you had to pick just one indicator to decide which Indian state gets the next round of central infrastructure funding, would you trust per capita income more than the Multidimensional Poverty Index, or do you think the two need to be read together to avoid a skewed picture?

References

1. https://egyankosh.ac.in/bitstream/123456789/84862/3/Unit-10.pdf
2. https://eacpm.gov.in/reports/relative-economic-performance-of-indian-states/
3. https://competitiveness.in/income-inequality-across-india-patterns-and-progress/
4. https://www.undp.org/india/national-multidimensional-poverty-index-progress-review-2023
5. https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1996271&reg=48&lang=2
6. https://www.newsonair.gov.in/monthly-per-capita-expenditure-increases-by-9-in-rural-areas-around-8-in-urban-areas-in-2023-24
7. https://www.ideasforindia.in/topics/poverty-inequality/spatial-disparities-in-household-earnings-in-india
8. https://pmc.ncbi.nlm.nih.gov/articles/PMC7668594/
9. https://www.oecd.org/en/publications/oecd-economic-outlook-volume-2025-issue-2_9f653ca1-en/full-report/india_65a8d75a.html
10. https://www.oecd-ilibrary.org/urban-rural-and-regional-development/oecd-regions-at-a-glance-2011/regional-economic-disparities_reg_glance-2011-11-en