In our research paper (India’s Curious Case Of Economic Divergence, IDFC Institute, 23 November 2016) and subsequent articles (including “Will GST Exacerbate India’s Income Divergence”, Mint, 8 February), we highlighted the issue of puzzling economic divergence among India’s large states. Using an extensive gross state domestic product (GSDP) data set from 1960-2015, we showed how the income gap between India’s richer states such as Tamil Nadu and Maharashtra and its poorer states such as Bihar and Uttar Pradesh is widening and not narrowing, as classical economic theory would postulate.
While individual income inequality in India and its potentially adverse social consequences have been discussed fervently, regional inequality among the different states of India can perhaps have a more adverse impact on the political economy of the country. As we showed in our paper, India is a complete outlier in the world in experiencing such regional income divergence vis-à-vis most other large federal economic unions such as the US, China, Canada and the European Union. The Economic Survey of 2016-17 also pondered over this issue of economic divergence of India’s states, as did the Organisation for Economic Co-operation and Development’s (OECD) recent economic outlook for India.
What is remarkable but perhaps not surprising is that our research shows that 1992 is the milestone year when India started to experience income divergence among its large states. The logical next question is—what are the reasons for such economic divergence in India? Tempting as it may be to blame the economic reforms of 1991 for India’s divergence, our research was insufficient to be able to offer any rigorous explanations. The Economic Survey too did not offer any evidence for the causes of such divergence but conjectured that the quality of governance across different states could be a plausible explanation.
In other words, the hypothesis of the Economic Survey is that the poorer states suffer from poorer quality of governance, which impairs their ability to grow as fast as the richer states. If that were to be true, then within the same state and presumably same standards of governance across the various districts of that state, the districts within a state should experience income convergence as economic theory would predict. Do districts within each state experience such income convergence, thereby validating this hypothesis? That is the question we investigate here, using a novel data set.
Unfortunately, we have reliable and official GDP and income data only at the state level and not at a more granular level. Not all states have official estimates of district domestic product (DDP) and even the ones that do are not updated. In order to impute economic activity and income levels at the more granular level of districts, we used a proxy data set of “nightlights” luminosity. There has been sufficient global research which demonstrates that nightlights luminosity is a good proxy for economic activity, including in India. It is important to note that we do not estimate actual levels of district GDP using luminosity, which could be problematic, but merely use it as a proxy trend to compare economic activities across regions.
We developed a nightlights luminosity data set for 640 districts (as per Census 2001 boundaries) and 543 Lok Sabha constituencies from 1992-2013. Keeping in line with our methodology of using the 12 largest states that account for 83% of the population and 80% of GDP, we measured economic convergence across 387 districts in these 12 states using the nightlights luminosity data set, which is perhaps the first attempt of its kind. Luminosity values are capped at a certain maximum value, which means that big cities such as Mumbai, New York, Tokyo, etc., that are very luminous, do not see their luminosity increase beyond a certain maximum value. This runs contrary to the fact that per capita GDP can grow infinitely without a ceiling. In other words, at the upper end, luminosity levels may be understating GDP. As a simple way to correct for this, our analysis excluded the five major metros of these 12 states—Mumbai, Kolkata, Chennai, Bengaluru and Hyderabad.
We first establish that state luminosity is indeed correlated tightly with state GDP, i.e., richer states have higher luminosity, and poorer states, lower. There is a 75-80% correlation between luminosity and state GDP across the years 1992-2013. Not so surprisingly, there is massive variation in luminosity levels across these districts, in line with income disparity (Chart 1, Lorenz curves). As many as 380 of the 387 districts in these 12 states are on average just one-fifth as bright as metro cities like Mumbai and Bengaluru at night. Even excluding the metros, 90% of all districts are just one-third as bright in the night as the top 10% of all districts. What is more striking is that this ratio is only worsening between 1992 and 2013, as the chart shows.
Further, using the standard Barro and Sala-i-Martin tests, we find that states exhibit similar divergence patterns on luminosity as they do with incomes. Since this conforms broadly with our observations of divergence across states using income data, it is then possible with confidence to test for intra-state trends across districts using luminosity. Our research shows that intra-state divergence across districts is as significant as inter-state economic divergence. Using luminosity as a proxy for economic activity, our analysis reveals that 10 of the 12 largest states exhibit strikingly similar divergence trends to what we observed across the richer and poorer states using per capita GDP.
In other words, within most of the large states, the economic gap between richer and poorer districts is widening, not narrowing. This is clearly at variance with the idea that governance differences can explain divergence across states, since this divergence is occurring within most states, not just across them. Chart 2 shows the divergence pattern (luminosity sigma) from 1992-2013 of all the districts within each state, excluding the metros. For reference, the divergence trend line of per capita GDP of all the 12 states is also shown. If the trend lines fall to the right, it signifies a divergent trend as the variation increases from 1992-2013.
Curiously, West Bengal is the only state to experience a convergence among its districts while Gujarat shows neither divergence nor convergence. All other states show strong divergence trends, in line with the trend observed on an inter-state basis using per capita GDP. In essence, not only is the income gap across rich and poor states of India widening, but so are income gaps across rich and poor districts within each state, albeit using luminosity as a proxy for income.
As we observed, if indeed the quality of governance in different states is what explained their economic divergence, as mooted by the Economic Survey, then what explains a similar divergence across districts within each state that ostensibly experience similar standards of governance? Could it be a case of an economic development model that inherently exacerbates economic divergence?
One simple test to check if economic divergence across districts within India’s states is related to the level of economic development, and, by extension, perhaps the development model, is to test if there is a tight relationship between GDP growth and economic inequality. Chart 3 shows trend lines for divergence across India’s districts relative to the per capita GDP of that state. It is evident that, as states get richer, economic divergence across its districts also widens, suggesting that perhaps the nature of economic development is a better explanation for regional disparity than just quality of governance. Admittedly, this is not a rigorous explanation but a mere hypothesis that needs to be tested further.
One simple way to understand this complex issue of economic divergence is to take the recent example of Apple wanting to set up a manufacturing base in India. Presumably, Apple’s proposed investment would add significantly to the economic output of that region and add jobs that would contribute to the wealth of the economy of that region. It is likely that the land and labour costs for setting up such a manufacturing unit would be much cheaper in the poorer states of Bihar or Uttar Pradesh than in much richer Karnataka or Tamil Nadu.
So, if such costs alone were decisive, Apple should choose to locate in Bihar or UP, but clearly there are other factors at play, such as quality of governance, infrastructure, resource availability, access to markets, connectivity, etc. If Apple then chooses Karnataka, it can either choose outer Bengaluru, where labour and land costs are much higher than a poorer district such as Shimoga. Again, if cost considerations were decisive, Apple should choose Shimoga but, for very similar reasons of network benefits, has reportedly chosen Bengaluru.
The real political economy question is whether Shimoga will continue to tolerate such inequality vis-a-vis Bengaluru until it becomes economically beneficial for Apple to move to Shimoga. Likewise, will Bihar continue to tolerate such inequality vis-a-vis Karnataka until the time it becomes economically beneficial for Apple to move to Bihar? This is perhaps the big question confronting a federal polity in an extremely diverse nation with widening regional income disparities. The winner of this race between the longer-run forces of economic convergence, as represented by Apple eventually finding it beneficial to locate in Bihar, and the medium-run forces of divergence will determine the future of federalism.
In a country such as India, with powerful regional political parties in almost every major state that cater exclusively to residents of their state, the tension caused by the centrifugal forces of regional economic divergence is bound to be exacerbated. Can India stave off regional inequality before it starts to threaten the political union of the country?
Praveen Chakravarty and Vivek Dehejia are senior fellows at IDFC Institute, Mumbai.
Ishita Trivedi of the IDFC Institute contributed inputs.
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