Social Impact

Uneven development

Print edition : August 05, 2016

At the Vaddera Basthi slum in Hyderabad. Photo: NOAH SEELAM/AFP

Liberalisation without government intervention has resulted in asymmetric growth leading to a highly skewed population distribution, overcrowded cities, mushrooming of slums, depletion of groundwater, and air pollution.

REFORMS IN INDIA SINCE 1991 HAVE BEEN responsible for improving the gross domestic product (GDP) growth rate, lowering the unemployment rate, and improving India’s foreign exchange reserves, to name a few benefits. However, growth needs to be seen in holistic terms and not just in terms of a few variables.

This article argues that many opportunities to create balanced growth have been missed in a reckless and unplanned process of liberalisation without sufficient oversight. The result is highly asymmetric growth, with excessive concentration of industries in select pockets, resulting in a highly skewed population distribution, overcrowded cities, the mushrooming of slums, the rise in the cost of urban housing to unaffordable levels, and pressure on land, whose outcome is serious quality-of-life issues such as water table depletion and air pollution.

Skewed distribution of development

The natural outcome of liberalisation without government intervention is concentration of industry in one place. This is because in an environment without artificial controls industry will prefer locations that already have an established business presence, apparently because local resources are adequate to sustain the business. Some important resources that matter to industry are consistent power supply, water supply, infrastructure such as roads, availability of finance, and a good local labour pool. Since an established concentration of industry already has all these factors in evidence, the tendency of any new player in a given industry is to open a presence where there already exists a strong preference.

While this is beneficial to the company, it is not necessarily (and usually is not) beneficial to either the location chosen or other locations that have been preferentially excluded.

The reason why it is not beneficial to the locations that are excluded is fairly obvious. A business presence confers employment and raises the standard of living in the local neighbourhood. This, in turn, stimulates the growth of consumer-facing businesses and generates more jobs.

But high concentrations of businesses in a given place can also be detrimental to that place. This is because it results in high pressure on land, making it very expensive for people to live there. Large numbers of people putting high pressure on land also leads to other quality-of-life issues such as vehicular pollution and shortage of water and electricity supply.

Before 1991, the government rationed the places where one could open a factory, how much one could produce, how many players could be in the field competing against one another, what prices could be set for products, and so on.

Post-1991, with the liberalisation of the Indian economy, the licence-permit raj was partially dismantled, and this took the shackles off industry in deciding where, when, and how much to produce. The state still retains a lot of control on location but a lot less than in the past.

It should be noted that even with state control, the geographical distribution of industry in India was quite uneven. But this has worsened since the dawn of liberalisation. This can be seen from Figure 1, which shows the distribution of the total industry in India from 1981 to 2003. The data show the percentage of the total industries in India that belonged to each of the States shown. The bulk of the industry (more than 90 per cent) was concentrated within these 16 States—both before and after liberalisation. It can be seen that the share of the three southern States of Tamil Nadu, Karnataka and Andhra Pradesh have been consistently going up, with the share of Tamil Nadu and Andhra Pradesh rising sharply after liberalisation. Maharashtra, Gujarat, Uttar Pradesh, West Bengal and Bihar show significant reductions in their share after liberalisation.

A different way of measuring relative concentrations of an industry in a place is to use a metric known as “location quotient”. The location quotient is defined as the ratio of the relative occurrence of an industry in a given location to the relative occurrence of that industry in the entire country. Relative occurrence is defined as the ratio of the presence of an industry (can be defined in different ways—for example, by total factory values) in a given region to the total presence of all industries (defined in the same way) in that region. The location quotient measures whether an industry is more common in a certain location than it is in the entire country. A higher location quotient implies a greater concentration of that industry in that location.

Figure 2 shows the location quotients for capital goods for the same 16 States, which in addition are divided into groupings of four States each—eastern, southern, central and north-west. The graph shows how the location quotient has changed over time by looking at four periods again: 1981-82, 1988-89 (before liberalisation), 1995-96 and 2002-03 (post-liberalisation).

It can be seen in this case that, for the specific case of capital goods, some striking changes have happened after liberalisation. Specifically, Haryana has shot up as a preferred location for capital goods, as has Gujarat, Madhya Pradesh, Punjab and Tamil Nadu.

The impact of liberalisation on preferential concentrations of industry within different States is thus evident. But this preferential concentration also occurs within a State as well, leading to sharp concentrations in urban agglomerations such as the Greater Mumbai region and the Pune region. This is illustrated by taking one State, namely Maharashtra, as an example.

For organisational and reporting purposes, the State of Maharashtra chooses to subdivide Maharashtra into six different industrial divisions: Konkan, Pune, Nashik, Aurangabad, Amaravati and Nagpur. This can be seen in Figure 3.

Figure 4 shows the number of people employed in special economic zones (SEZs) in Maharashtra across these different geographical divisions. The disparity is obvious. Pune and Konkan (which includes Greater Mumbai) dominate the landscape in industrial investments in SEZs. The picture is reinforced in Figure 5, which shows the amount invested in SEZs in Maharashtra.

A similar picture is obtained when one investigates the industries that come under the auspices of the Maharashtra Industrial Development Corporation (MIDC). Figure 6 shows the distribution of the industrial units in the MIDC between the six geographical divisions. The distribution is different from that of SEZs, which were dominated by Mumbai and Pune divisions, but the unevenness of the distribution is apparent. Figure 7 shows the distribution of employees in these MIDC factories.

The immediate consequence of this kind of uneven development is that populations tend to cluster in large cities or urban agglomerations. An example of an urban agglomeration is Greater Mumbai, which includes Mumbai, its suburbs and Navi Mumbai. There is large-scale emigration from rural areas and small towns to these large population centres. Figure 8 shows the populations of the top urban agglomerates with a population of more than two million people.

Not only has uneven growth led to huge concentrations in population, but the concentration is growing, as is seen from Figure 9, which shows the percentage of urbanisation of India as a whole and of the major States. Clearly, India is becoming more and more urban, with more and more people packed into compact urban centres.

What are the consequences of this kind of concentration of people? The immediate consequence is that housing starts to become more and more unaffordable. Figure 10 shows the Housing Price Index, which measures the mean rise in housing prices over a period of two years. It can be seen that the price of housing has increased by over 1.5 times within just two years.

The poor who come to big cities and cannot afford housing end up in slums—unhygienic, incredibly crowded agglomerates within cities and urban agglomerates that are not fit for human habitation. With no other recourse, people live in these and even pay atrociously high rents for them. The Census of India in 2001 and in 2011 tracked the growth of slums. In 2001, out of 3,799 statutory towns, there were 1,743 slum towns, whereas in 2011, out of 4,041 statutory towns, there were 2,613 slum towns.

One further consequence of this unprecedented pressure on land is that the basics of life are not enough for so many people within a small area. Water supply is a typical problem facing many people living in rapidly growing cities such as Pune and Bengaluru. When water supply from municipal corporations is inadequate, residents try to supplement their water supply with borewells. But this is also a finite resource, as the wells cannot support such a large population. So wells also start to run dry. Figure 11 shows a typical time trace of the location at which water is found with a borewell. This is expressed in the units of mbgl, or metres below ground level. It represents how deep one must dig to find water. What this tells us is that the water table is rapidly falling.

Another consequence of both high industrialisation and the vehicular needs of people who live in these urban agglomerates, with longer and longer commutes as one cannot afford to live close to the place of work but needs to travel further and further to get to work, is the attendant increase in air pollution.

Figure 12 shows the PM10 levels, a measurement of particulate emissions that is defined as particles with diameter of 10 microns or less, in micrograms per cubic metre. These arise from various sources such as diesel trucks and residential coal stoves. In large cities, automotive exhaust is a major source of this pollutant. A comparison of the 2008 and 2012 values shows that there has been a dramatic rise in PM10 levels in these four years. Air pollution has led to a documented rise in deaths of children under five—from 4,331 in 2004 to 6,905 in 2008.

Conclusion

The increasingly uneven growth since the start of liberalisation has had serious consequences such as the creation of extreme concentrations of people in large urban agglomerations; unprecedented pressure on land, making it impossible for most people to afford housing in these cities and urban agglomerations; tremendous growth of slums; shortage of water and electricity; long commute times for people; pollution of the air we breathe; and many other problems.

But it did not have to be this way. This should be viewed as a lost opportunity for India, for much of the growth of India that we see today has happened after liberalisation. India’s GDP was Rs.6,13,528 crore (at 2004-05 prices) in 1991-92, and in 2013-14 it was Rs.104,72,807 crore—an increase of 2,400 per cent.

Given that the growth has been so great, if sufficient thought had been put into the liberalisation process at the start, a more equitable growth would have resulted and the problems mentioned herein could have been avoided.

Seshadri Kumar is an R&D chemical engineer with a BTech from IIT Bombay and an MS and a PhD from the University of Utah, U.S. He writes regularly on political, social, economic and cultural affairs at http://www.leftbrainwave.com

References

1. Economic Survey of Maharashtra, 2014-2015.

2. Open Government Data Platform for Housing Price Index (https://data.gov.in/catalog/housing-price-index-india)

3. Planning Commission Data Book (22 December 22, 2014) (http://planningcommission.gov.in)

4. Maharashtra State Plan (http://planningcommission.nic.in/plans/stateplan/sdr_maha/ch-5-14-02-05.pdf)

5. Census of India for 2001 and 2011 (http://www.censusindia.gov.in)

6. Water Resource Website, Ministry of Water Resources (http://www.india-wris.nrsc.gov.in/GWLevelApp.html?UType=R2VuZXJhbA==?UName=)

7. Saikia, D. (2009):, “Industrial Location in India Under Liberalization”, MPRA Paper No. 27821, 2009 (online at https://mpra.ub.uni-muenchen.de/27821/).

8. World Health Organization (http://apps.who.int/gho/data/node.home)

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