The Tertiary Sector of the Indian Economy

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For all the 593 districts of India…………. Gross Domestic Product across sectors at current prices District Gross Domestic Product across sectors at constant prices Credit to District Domestic Product Ratio in various sectors Per Capita Gross Domestic Product by Economic Activity, at current prices Per Worker Gross Domestic Product by Economic Activity, at current prices Gross Domestic Product estimates at granular levels is aimed at helping government bodies, companies, analysts, researchers, consultants and strategists understand economic activity levels and growth over time. This product brings out information on Gross Domestic Product at current as well as constant (1999-00 prices) prices for all 593 districts in India. Gross Domestic Product is defined as the total value of all final goods produced within the specified geographical area in a given year. We provide estimates for 7 years from 2001-02 to 2007-08. Estimates have been provided at three different levels- district, state and country level. Estimates are provided for aggregate GDP, Primary sector (Agriculture, Mining, Fishing, Forestry), Secondary sector (Construction, electricity, Manufacturing), Tertiary sector (Transport, Storage & Communication, Trade, Hotel & Restaurant, Public Administration, Banking & Insurance, Others) – 3 major sectors and 12 major sub-sectors of the economy. Coverage - Primary Sector (Current Prices and Constant Prices separately) Agriculture (Current Prices and Constant Prices separately) Mining & Quarrying (Current Prices and Constant Prices separately) Fishing (Current Prices and Constant Prices separately) Forestry (Current Prices and Constant Prices separately) Secondary Sector (Current Prices and Constant Prices separately) Construction (Current Prices and Constant Prices separately) Manufacturing Sector (Current Prices and Constant Prices separately) Electricity, Gas & Water Supply (Current Prices and Constant Prices separately) Tertiary Sector (Current Prices and Constant Prices separately) Transport, Storage & Communication (Current Prices and Constant Prices separately) Trade, Hotel & Restaurant (Current Prices and Constant Prices separately) Public Administration (Current Prices and Constant Prices separately) Banking & Insurance (Current Prices and Constant Prices separately) Other Services (Current Prices and Constant Prices separately) Total Gross Domestic Product (Current Prices and Constant Prices separately)

Business Unterhaltung & Humor

The Tertiary Sector
of
The
Indian Economy

District GDP – Tertiary Sector (current prices)

Figures in Rs millions
District level estimates for 2007-08

Per capita District GDP – Tertiary Sector (current prices)

Figures in Rs ‘000s
District level estimates for 2007-08

District GDP – Transport, Storage and Communication (current prices)

Figures in Rs millions
District level estimates for 2007-08

District GDP – Trade, Hotels and Restaurants (current prices)

Figures in Rs millions
District level estimates for 2007-08

District GDP – Public Administration (current prices)

Figures in Rs millions
District level estimates for 2007-08

District GDP – Banking and Insurance (current prices)

Figures in Rs millions
District level estimates for 2007-08

District GDP – Other Services (current prices)

Figures in Rs millions
District level estimates for 2007-08

Share in District GDP – Tertiary Sector

Figures in %
District level estimates for 2007-08

Share in District GDP – Banking and Insurance

Figures in %
District level estimates for 2007-08

Share in District GDP – Public Administration

Figures in %
District level estimates for 2007-08

District GDP of India
2007-08
We provide estimates for 7 years from 2001-02 to 2007-08.
Estimates have been provided at three different levels-
district, state and country level.

Estimates are provided for aggregate GDP, Primary sector
(Agriculture, Mining, Fishing, Forestry), Secondary sector
(Construction, electricity, Manufacturing), Tertiary sector
(Transport, Storage & Communication, Trade, Hotel &
Restaurant, Public Administration, Banking & Insurance,
Others)

– 3 major sectors and 12 major sub-sectors of the economy.

As with all Indicus products, the product
comes with a query software with GUI
navigation which allows the user to drill down
to the states via clickable maps.
The users can extract data in spreadsheets, and
create maps and charts with parameters.

Options
The product is available in four versions
Basic – Estimates of Aggregate GDP, Primary, Secondary and Tertiary
sector GDP for 2007-08

Basic plus - Estimates of Aggregate GDP, Primary, Secondary and
Tertiary sector GDP, 4 sub sectors of primary sector, 3 sub sectors of
secondary sector and 5 sub sectors of tertiary sector for 2007-08

Premium - Estimates of Aggregate GDP, Primary, Secondary and
Tertiary sector GDP for seven years - 2000-01 to 2007-08

Premium plus - Estimates of Aggregate GDP, Primary, Secondary and
Tertiary sector GDP, 4 sub sectors of primary sector, 3 sub sectors of
secondary sector and 5 sub sectors of tertiary sector for seven years -
2000-01 to 2007-08

For queries or placing orders
please contact
Indicus Analytics Pvt. Ltd.
2nd Floor, Nehru House,
4 Bahadur Shah Zafar Marg
New Delhi- 110002.
Phone: 91-11-42512400/01
E-mail: products@indicus.net
www.indicus.net

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The phenomenon of increased urbanization in India is facing one of its foremost challenges in the form of disparity between redistribution of economic opportunity and growth. The centre of poverty is gradually shifting towards urban centres and this situation is further worsened by already high population densities, poor infrastructure and a general lack of effective housing policy and provisioning for the poor. The Census of India 2011 suggests that 66% of all statutory towns in India have slums, with 17.4% of total urban households currently residing. However, this estimate of slums takes into account certain criteria set by the Census for a settlement to be featured as a slum. A large proportion of households who are living in similar or poorer dwelling conditions than those living in slums have been omitted. This study encompasses all those settlements that comply with the definition of slums (as given by the Census of India) as well as those with similar or poorer dwelling conditions that those of slums as ‘Informal Settlements’, because these are primarily dwelling units where most of the urban poor live. Interventions should be targeted at all these informal settlements instead of only slums as defined by the Census, since the quality of life and infrastructure in these informal settlements are similar to those of slums. The objective of the present study is to look into the contribution of informal settlement households to urban economy. The primary reason for looking at this particular question is to determine whether the informal settlement households, who normally form the poor strata of the urban population, do contribute to the urban economy to a significant extent or not. If they do contribute to urban economy, whether providing proper urban services to them should be treated as their legitimate right? For greater comprehension, this study attempts to discover the role of informal settlement population as a productive agent in urban economy, which is in contrast to the general notion that this section of population is “burden to the city.”

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PREDICTING GROWTH OF URBAN AGGLOMERATIONS THROUGH FRACTAL ANALYSIS OF GEO-SPATIAL DATA Location Analytics is one of the fastest emerging fields in the broad area of Business Intelligence/Data Science. By some industry estimates, almost 80% of all data has a location dimension to it. Consequently, identification of trends and patterns in spatially distributed information has far reaching applications ranging from urban planning, to logistics and supply chain management, location based marketing, sales territory planning and retail store location. In view of this, we present an approach based on Fractal Analysis (FA) of highly granular geo-spatial data. Specifically, we use proprietary data available at approximately1 square km level for New Delhi, India provided by Indicus Analytics (India’s leading economic data analytics firm based in New Delhi). We compare and contrast the patterns and insights generated using the FA approach with other more traditional approaches such as spatial to correlation and structural similarity indices. Preliminary results indicate that there are indeed “selfsimilar” local patterns that are completely missed by spatial correlation that are accurately captured by the more sophisticated FA approach. These patterns provide deep insights into the underlying socio-economic and demographic processes and can be used to predict the spatial distribution of these variables in the future. For example, questions such as what are the pockets of population growth in a city and how will businesses and government respond to that growth can be answered using the proposed approach.

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India’s strong consumption story relies on its demographic structure, which, at this point in time, is highly favourable compared to most other emerging nations. As per the UN population statistics, this favourable demographic dividend will last for another 25–30 years. Before that, most other emerging nations would have already begun to witness a slowdown in the growth of young (working-age) population. The ensuing benefits with regard to the rising income and household spending would provide a significant boost to the consumption-driven growth story of India. A glimpse of the changing pattern of India’s consumption is already visible in the breakdown of private final consumption spending data provided by the government. There is a marked increase in spending on lifestyle products and services such as hotels, mobiles, transportation and other miscellaneous goods. As against that, spending on essentials has only remained stable. International retailers are well aware of these benefits that the Indian economy offers. Barring few legislative challenges that could be tackled through the policy reforms and opening up of the retail sector, retailers have often expressed their intention to enter and invest in India’s attractive retail sector. This is very well reflected in AT Kearney’s Global Retail Development Index 2012, where India ranks as the fifth most attractive retail market for international retailers. The retail sector is a significant contributor to India’s economic activity. Though a direct measurement of the retail sector is difficult to derive through government statistics, the trade, hotels and restaurant sectors come close to giving us an estimate of its contribution. That component, in which retail (both organised and unorganised) is the dominant activity, accounts for around 18% of India’s GDP. Within the services sector of India, this component is the largest contributor to the economy. Many institutions, however, may not agree with this possibly understated measurement of the retail sector, as it may not accurately account for the unorganised sector. For instance, as per the estimates of the Associated Chamber of Commerce and Industry (ASSOCHAM) presented in one of its retail reports of 2012, the contribution of both organised and unorganised retail stood at 22% of GDP. This would mean that Indian retail sector size should measure closer to INR 19.2 trillion in 2012. Leading research institutions such as AT Kearney and ASSOCHAM estimate this sector to grow at around 15% y-o-y over the next three–five years as against a 12%–13% nominal growth of India’s GDP estimated by the International Monetary Fund (IMF). Going by that logic, the retail sector should reach a size of INR 34 trillion by 2016. This is a significant growth. The sector is also an important contributor towards the socioeconomic well-being of the economy as it employs close to 9.4% of India’s labour force, as per the association.

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So the Food Security Bill is through. More than two thirds of the country’s population has now been promised highly subsidized food. Congress and UPA will get a couple of extra percent points of votes, add another 2-3 percentage points because of the good monsoons and you get a good enough swing for it to come back next year. The BJP was checkmated as it was impossible for it to play its usual flawless doublespeak. I am asked what could be bad about ensuring elimination of hunger and malnutrition. I would like to ask a counter question? What is good about theFood Security Bill? It promises to finally eliminate hunger and malnutrition, they say. How? Because now the poor can buy wheat, rice and coarse cereal at highly subsidized rates. How will the poor be identified I ask; that will happen they say. Where will the poor buy from I ask; the Public Distribution System (PDS)they say. Where? I ask again. The PDS shop, they say. And why will the PDS shop now suddenly start working when it has not for so many decades? Because now it’s a right, and people can demand redressal from the courts, they say. So let’s grant this – the PDS will now start to function because the government will better use better technology. They will use GIS, GPS, perhaps Aadhar card and biometrics, etc. and this will eliminate the problems that the PDS system has. How will it work? The government will buy grains from production centres, store and transport them to consumption centres, and then sell them at subsidized rates through the public distribution system. Each of these will cost. Of course the PDS system itself will need to be strengthened almost everywhere. This will also cost. The high-tech sounding technology is not costless; the Aadhar number needs biometric identification, etc. etc. All of this will cost a lot. A paper coming from the government’s own Commission for Agri Cost and Prices (CACP) puts the total figure at about 682 thousand crores over a three year period. It is highly unlikely that the government can spend this, and the system cannot work well unless it is implemented very well. Chidambaram fighting his needless forex battles cannot loosen the purse strings. And even if he did, no one in this government has the ability to implement it. And without some serious money backed by serious project management skills the subsidized food will not reach where it is intended to. There will therefore be leakages. The estimated leakage itself is about 200 thousand crore by the CACP. I think it will be more as leakage is not only amount getting diverted, but also the amount wasted. When the numbers are so high it is obvious what kind of people will like to get into politics and into the government, and which ones would stay away.But these are all nitty-gritties of implementation. The Food Security Bill is inherently flawed in many other ways.Who will have control over this whole process?

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The Economic Freedom of the States of India 2012 estimates economic freedom in the 20 biggest Indian states, based on data for 2011. The aim of this report—to measure the level of economic freedom within India—grows out of a larger project begun in the 1980s by the Fraser Institute and culminating in the annual Economic Freedom of the World report (co-published by the Cato Institute in the United States). That exercise has proved fruitful in establishing a strong empirical relationship between economic freedom and prosperity, growth, and improvements in the whole range of indicators of human well being. The global report has also produced an explosion of research by leading universities, think tanks and international organisations on the critical role of economic freedom to human progress, including its importance to sustaining civil and political liberty. The Cato Institute is pleased to co-publish the present report on India with Indicus Analytics and the Friedrich Naumann Foundation at a time when both India’s high growth prospects and its commitment to reform have come under scrutiny. The main highlights of this study are as follows. 1. The top state in India in economic freedom in 2011 was Gujarat. It displaced Tamil Nadu, which had been the top state in 2009. Gujarat’s freedom index score has been rising fast, and at 0.64 it is now far ahead of second-placed Tamil Nadu (0.56). Madhya Pradesh (0.56) is close behind in third position, Haryana (0.55) retains fourth position and Himachal (0.53) retains fifth position. 2. The bottom three states in 2011 were, in reverse order, Bihar, Jharkhand and West Bengal. In 2009, the reverse order was Bihar, Uttarakhand and Assam. Uttarakhand has moved up sharply from 19th to 14th position, and this improved freedom is reflected in its average GDP growth rate of 12.82 per cent in 2004-2011, the fastest among all states. This is an impressive achievement for a once-backward state. 3. Earlier the median score for economic freedom for all states had declined from 0.38 in 2005 to 0.36 in 2009. But it has now improved substantially to 0.41 in 2011. This is good news. Still the median score lags way behind Gujarat’s 0.64, so other states have a long way to go. 4. The biggest improvement has been registered by Madhya Pradesh. Its freedom index score rose from 0.42 in 2009 to 0.56 in 2011, enabling it to move up from 6th to 3rd position. This improved economic freedom was associated with acceleration in its GDP growth. This averaged 6 per cent per year from 2004-2009, but then accelerated to 9 per cent per year in 2009-2011. 5. The biggest decline in economic freedom has been recorded by Jharkhand, which slumped from 8th to 19th position. Its score declined from 0.38 to 0.31. Unsurprisingly, its GDP growth has averaged only 4.6 per cent in 2004-2011, one of the lowest among all states . Jharkhand has special problems as a heavily forested state suffering from Maoist insurrections.

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Education is clearly important in tapping the so-called demographic dividend. There is nothing automatic about a demographic dividend materializing. Among other things, that is a function of health and education outcomes. More specifically, there is question of skills. The overall skills deficit has often been flagged. For instance, in 2002, the S.P. Gupta Special Group constituted by the Planning Commission stated, “It should be noted, however, that on the average the skilled labour force at present is hardly around 6-8 per cent of the total, compared to more than 60 per cent in most of the developed and emerging developing countries.” In 2001, the Montek Singh Ahluwalia Task Force , again constituted by the Planning Commission, stated, “Only 5% of the Indian labour force in this age category has vocational skills.” While the numbers are marginally different, the Eleventh Five Year Plan document adds the following. “The NSS 61st Round results show that among persons of age 15-29 years, only about 2% are reported to have received formal vocational training and another 8% reported to have received non-formal vocational training indicating that very few young persons actually enter the world of work with any kind of formal vocational training.” Among the youth, most of those with formal training are in Kerala, Maharashtra, Tamil Nadu, Himachal Pradesh and Gujarat. A better indicator of a State’s performance is the share of the young population that has some variety of formal training. In this, Maharashtra, Kerala, Tamil Nadu, Gujarat and Andhra Pradesh perform well. Is this because there is better training capacity and infrastructure? Is it because industrial activity exists in these States? Is it because there is a positive correlation between some minimum level of educational attainment and acquisition of formal training? The answer is probably a combination of various factors.

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Growth StoryG rowth is never an end in itself. It is a means to an end, especially because by growth one typically means growth in gross State domestic product (GSDP). In the context of a country, GSDP is akin to GDP (gross domestic product), the total value of goods and services produced in a country over a fixed time period,typically one year. GDP isn’t the same as GNI (gross national income), since GNI also includesnet factor income from abroad. The principle is no different for a State and GSDP is notnecessarily the same as gross state income (GSI). The difference can be important for a Statewhere migration and remittances are major variables. However, having accepted the point, oneis stuck, since no credible estimates exist for GSI. One only has figures on GSDP and mustaccept it as a surrogate indicator. GSDP figures are compiled by Directorates of Economics andStatistics of different State governments. They are then “vetted” by Central StatisticalOrganization (CSO) and finalized. GSDP figures can be in current prices, or in constant prices.If we do not wish to get carried away by inflation, we should focus on constant price numbers.In the present case, this means that everything is expressed in 2004-05 prices.

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Household consumption patterns depend on many factors, and the age of the chief wage earner is a key determinant. The Indicus Indian Urban Consumer Spectrum classifies urban households into three broad categories: younger years, in which the chief wage earner is predominantly less than 34 years of age; middle years, in which the chief wage earner is mainly in the age group of 35 to 54, and mature years, households in which the chief wage earner is usually over the age of 54. At each life stage, there are different income and consumption patterns; as the chief wage earner moves into the older years, the family structure also changes. So the category of younger years does not necessarily denote younger households; in fact, households in mature years have more than 40% of its population under the age of 18. Creating consumer segments by the age of the chief wage earner of the household reveals patterns that are otherwise hidden in data. Take for instance occupations—the sector that employs the highest share of chief wage earners in younger and middle years is manufacturing, which takes up a lower share for chief wage earners in mature years. On the other hand, manufacturing falls to second slot for chief wage earners in mature years; and more interestingly, public administration/defence accounts for the third largest share of employment in this segment. This does point to the changing structure of employment over time, and also gives an indication of the income and consumption behaviour of these households. Then there is the size of the household—households where the chief wage earner is in his younger years are to a large extent small in size; close to 60% are single member households—the earning member in the city is single or married and living away from his family. This is the smallest segment, comprising less than 15% of urban households, and around 5% of urban population. The largest segment, which accounts for more than 60% of urban households, is those in which chief wage earners are in their mature years; here, a majority have five or more members and almost a quarter have more than two earning members. This, therefore, forms a bulk of urban consumer spends; and, since it includes senior citizens as well as minors, it caters to the needs of all age groups. The segment in which chief wage earners are in their middle years accounts for more than a quarter of urban households. This segment stands out as the one in which almost all households have minors; this would, therefore, be extremely cued into the needs of growing children—whether it comes to education, food or entertainment, it is in these households that children rule. The younger years segment feeds into the others as chief wage earners marry, or bring their families to the cities and have children, save to buy houses, two-wheelers, cars and so on, and the maturity of the chief wage earner naturally shows up in higher incomes and asset penetration across the groups.

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mall durables—the little items that personalize households and make each home different—can be divided into four main groups: furniture and fixtures, household appliances, recreational goods, and other personal goods including mobile handsets, watches, clocks, plastic goods and decorative items. As a group, they account for less than 2% of total household expenses, as other basic necessities such as food, travel and rent take up the bulk of the budget. The largest sub-groups in this category are other personal goods and household appliances, accounting for more than 80% and 11%, respectively, of the total expense within the group. There are variations across states. In Chandigarh, Goa and Kerala, household appliances take up close to 20% of the expenses in this category, double the average. Within this group of small durables, there is a wide variety, with prices and brands to suit every pocket, and as households move up the income ladder, they spend on higher-value items within the group; per-household annual expense on small durables, therefore, rises from Rs. 1,255 on an average in the lowest income segment, which are households earning less than Rs. 1.5 lakh a year, to Rs. 11,807 in the highest income segment of households earning more than Rs. 10 lakh a year.

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With technology based solutions seen as key to achieving financial inclusion, the role of e-money becomes important in reaching out to the unbanked masses. While regulatory space in India has been slowing opening up to allow non-banks to act as e-money issuers and prudential norms are in place, regulatory concerns remain regarding the safety of customer funds and the potential impact of e-money on monetary aggregates. The regulator’s dilemma, as described by David Porteous, is whether or not to implement measures that may hinder expansion of access to nonusers in the interest of greater protection for those who already have access, and it is for each country to evolve models and practices appropriate to their economy. It is however instructive to absorb lessons from international experiences that exemplify how regulations can evolve to meet the challenges involved in non-bank e-money issuers, all with the aim of bringing about universal financial inclusion.

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Small durables a grwoing market

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1. The Tertiary Sector of The Indian Economy

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3. Per capita District GDP – Tertiary Sector (current prices) Figures in Rs ‘000s District level estimates for 2007-08

4. District GDP – Transport, Storage and Communication (current prices) Figures in Rs millions District level estimates for 2007-08

5. District GDP – Trade, Hotels and Restaurants (current prices) Figures in Rs millions District level estimates for 2007-08

6. District GDP – Public Administration (current prices) Figures in Rs millions District level estimates for 2007-08

7. District GDP – Banking and Insurance (current prices) Figures in Rs millions District level estimates for 2007-08

8. District GDP – Other Services (current prices) Figures in Rs millions District level estimates for 2007-08

9. Share in District GDP – Tertiary Sector Figures in % District level estimates for 2007-08

10. Share in District GDP – Banking and Insurance Figures in % District level estimates for 2007-08

11. Share in District GDP – Public Administration Figures in % District level estimates for 2007-08

12. District GDP of India 2007-08 We provide estimates for 7 years from 2001-02 to 2007-08. Estimates have been provided at three different levels- district, state and country level. Estimates are provided for aggregate GDP, Primary sector (Agriculture, Mining, Fishing, Forestry), Secondary sector (Construction, electricity, Manufacturing), Tertiary sector (Transport, Storage & Communication, Trade, Hotel & Restaurant, Public Administration, Banking & Insurance, Others) – 3 major sectors and 12 major sub-sectors of the economy.

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