Razib has an excellent post with information familiar to India-watchers: India is very diverse. In particular, it has a South which does well on levels of human development (and increasingly income as well); while the states in the “BIMARU” North perform abysmally on both economic and human development indicators. These kinds of disparities are frequently overlooked by commentators — both in India and elsewhere — who have as their primary analytical unit the nation-state.
Will Wilkinson has dubbed a related phenomenon the “UN Fallacy” — the error of assuming that two areas can be usefully compared simply because they are nation-states. So, for instance, you hear nonsense related to how “China has overtaken Japan.” Of course, on a per capita basis China remains poorer than El Salvador. Yet because the Chinese have aggregated themselves into a relatively large political unit, we think of the Chinese as “getting rich” and the Salvadorians as “poor.” We think of India as surging ahead, though it has more poor people than Africa.
Of course, the role of geography intersects with numerous other determinants of inequality in India. Yet rough geography remains an enormous predictor of income and life status. For instance, tribals — a broad category referring to various unassimilated groups — are among India’s poorest populations. Yet tribal income in the (relatively prosperous) hill states is the same as the income for an upper caste in a poor state. Dalits — former “untouchables” — make almost as much in rich states as upper castes do in poor states, and earn substantially more in urban environments than upper castes do in rural areas (these statistics are from Sunil Jain’s excellent book).
Also interesting are fertility differences. Several South Indian states are already below replacement in their fertility rates, while women have around 4 children per average in poor northern states like Uttar Pradesh and Bihar — which would lead to a doubling of population every generation.
This means that India’s demographic dividend could easily become a bust. The demographic dividend is the idea that countries which transition from high-fertility to low-fertility equilibria experience a temporary sweet spot in which a high proportion of working-age adults can drive an economic transition. India, as a whole, is going through this process. However, it is not on a region-by-region basis. The excess labor is, overwhelmingly, coming from poor, undereducated, and underfed states. The jobs, overwhelmingly, are located in richer and coastal regions.
Bihar, for instance, is a state of over 80 million people, with a per capita income of around $150 (no, that’s not a typo — Bihar’s per capita income is lower than India’s by a factor of perhaps 6); yet industry accounts for less than 10% of GDP. 58% of Biharis are below the age of 25; but the only way they are going to contribute meaningfully to India’s economy is if they move elsewhere, or industry magically pops up within the state.
TeamLease has carefully documented the impact of this geographical mismatch. Between 2010 and 2020, the states of Uttar Pradesh, Bihar, and Madhya Pradesh will account for 40% of the increase in 15-59 year olds, but 10% of the increase in GDP. Four richer western and southern states will account for 45% of the increase in income, but 20% of the increase in workforce.
These economic differences have already sparked large increases in internal migration, and will presumably continue to do so in the future. However, this leads to political pressures (for instance, the rise of ethno-linguistic chauvinistic parties in rich Mumbai protesting against Bihari immigrants, among others), and mass migration is not a feasible option.
One comment I want to add in the Indian context is that even looking at states can be misleading. In part due to robust sub-national loyalties, India’s sub-national administrative divisions are hugely diverse, and contain within them areas of extreme poverty. A state like Uttar Pradesh has close to 200 million people — which would make it the world’s sixth largest largest country on its own. It, too, is home to a bewildering variety.
For instance, the district of Hardoi in Uttar Pradesh had (in 1991) a population of 2.7 million and an infant mortality rate of 129 per 1,000 births. Compare this to Guinea-Bissau, a country with 1 million people and an infant mortality rate of 148. Bahraich in the same state had a population of 2.8 million, and a female literacy rate of 11% (up to 23% by 2001). Compare that to Benin, a country with 4.8 million people in 2001 and a female literacy rate of 17%. Uttar Pradesh has 71 districts, many of which are as large as African countries; several of which rank equally bad on development indicators.
Meanwhile, the western part of the state is similar to neighboring Haryana and Punjab — which are hailed as economic successes. All three regions are home to a large Jat population which has eagerly adopted technologies related to the Green Revolution.
This part of the country (located in the far NW) is largely responsible for India’s abysmal statistics on gender ratios:
The brown regions indicate where the sex ratio is particularly low — indicating the killing, neglecting, or aborting of baby girls. Mass gendercide on this scale has attracted particular attention, and is behind the “missing women” phenomenon in which India has far fewer women than expected. On western countries, there tend to be more women than men. In the graph, the brown color indicates that there are fewer than 800 women per 1000 men.
While this situation is typically blamed on India’s “culture”; it’s clear from the graph that the problem of missing women is closely affiliated with particular folkways in certain parts of the country, and not especially related to income. Punjab, Haryana, and Western Uttar Pradesh have high incomes, but the worst gender ratios in the country. Much of the North does badly, but surprisingly even the high-income western states of Gujarat and Maharashtra have fewer women than men. By contrast, the low-income eastern tribal-dominated states do well in terms of gender ratios, as does the relatively egalitarian South.
It’s interesting to contrast this graph with the following one, which indicates those districts (in brown) with a female literacy rate above 50% as of 2001 (a useful proxy for “development” broadly):
Surprisingly, many districts with few women have (relatively) high female literacy rates.
This graph confirms the stylized fact from the state data that northern areas are worse off. Those districts in blue have a female literacy rate below 50%. In states like Bihar and Uttar Pradesh, this is true for nearly all districts; it is true for no district in high-literacy Kerala in the South.
But also check out the cluster of low literacy states in the “rich” South. These roughly correspond to the old princely state of Hyderabad. This is a part of the intra-state disparities which are growing as India’s exposure to globalization and growth proves uneven, and are playing an increasingly important role in the political process.
In this region, the state of Andhra Pradesh (a relatively rich state) has been hit by violence as protesters from its Telangana region (an all blue area) hope to break off from the wealthier coast. The floodwaters of the Krishna and Godavari rivers disproportionately benefit these irrigated coastal areas (a colonial legacy — thanks to Sir Arthur Cotton), leaving the uplands to rely on rain-fed agriculture that is at the whims of seemingly increasingly variable monsoons. Similar conflicts roil many other Indian states.
These issues relate to how you think the China v. India battle will play out. Will China’s catastrophic malinvestment in capital prove more destructive than India’s chronic underinvestment in its people? Your answer to that question determines the fate of Asia’s two rival giants.