India’s Economic Divergence Problem

In a very innovative study carried out at the IDFC Institute, Praveen Chakravarty and Vivek Dehejia have found that there is significant economic divergence not only among Indian states, but also among regions inside states. This suggests that this divergence is caused not by variations in the quality of governance, but by agglomeration effects arising out of scale economies. A briefing report is available here, and a summary of their findings is available in a newspaper article.

The same researchers had determined in a previous study that there was considerable divergence among Gross State Domestic Products among the 12 largest states of India in the 1991-2015 period. They write

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.

They have reported that this effect is seen only in India, and other large federal unions such as US, China, Canada and Europe show convergence rather than divergence over time. The Government’s Economic Survey of 2016-17, and the OECD economic outlook for India have also considered this phenomenon. The Economic Survey conjectured that different levels of the quality of governance in different states may be the cause of this divergence.

In order to test this hypothesis about governance, Chakravarty and Dehejia have now gone one step further and tested for divergence at the intrastate level. Since the quality of governance is the same across regions of a state, one would expect convergence, or at least a lack of divergence, at the intrastate level, if the governance hypothesis is true.

Since data on output at intrastate or district level is either unavailable or unreliable, it is not easy to test for income divergence at the district level. Chakravarty and Dehejia have got around this problem using a very innovative approach. They have constructed a proxy data set of “nightlights” luminosity. The total luminosity of nightlights, observed from space, is a good proxy for economic activity, and satellite data on night lights has been used in the past to augment official income growth measures for countries where official national income accounts are poor or unavailable. For instance, see here.

Chakravarty and Dehejia have constructed a nightlights luminosity data set for India, using the archived satellite data from the US Air Force (USAF) Defence Meteorological Satellite Program (DMSP). This data ranges in time from 1992 to the present, and has been disaggregated by districts and also Lok Sabha constituencies for the largest 12 states of India. Using this data, they found that per capita output at the district level has also diverged in all large states except West Bengal and Gujarat.  The West Bengal result may possibly be explained by the fact that economic activity in the state is concentrated around the metro Kolkata, which like all other metros, has been excised from the data set for technical reasons.

This intrastate divergence shows that state level variation in governance is not what is causing this ongoing economic divergence. The most likely reason, that they cite, is network effects, i.e. agglomeration of activity because of economies of scale and also because of localised knowledge spillovers. Models of spatial economics that suggest agglomeration go back to Krugman’s famous 1991 paper on the effects of increasing returns on economic geography. This model implies that in the presence of increasing returns and low transportation costs, a two region economy will settle down into a steady state with a core region that specialises in advanced industry and has higher income, and a periphery which enagages in subsistence agriculture and has low income. In a multi-region model the same sort of result holds and an uneven economic structure emerges, that has a few high income industrial clusters. This is exactly the phenomenon that we appear to be seeing, as Chakravarty and Dehejia point out in their parable of Shivamogga vs. Bengaluru.

The political implications of this uneven pattern of development may be quite dire in the long run, as the researchers have pointed out, since such uneven development is likely to lead to popular resentment and upheavals. As they have also noted, the implementation of GST is likely to exacerbate the trend of divergence and leave no tax incentive tools in the hands of the laggard states with which to attract investment.

There is one theoretical argument that offers a policy solution. In a paper published in 2000, Richard Baldwin showed that while growth and increasing returns exert a centripetal force and give rise to uneven growth, on the other hand, long distance knowledge spillovers are a centrifugal force. Thus, lowering the cost of transferring knowledge gives rise to more balanced development. In this light, the government’s smart cities project, and also the project to start many new technological institutes, takes on added significance, as essential efforts in the attempt to achieve more balanced growth.



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