Solving the Income Data Puzzle

The problem with Income Data in India
Income data in India has always been a contentious issue. There is a lot of intuitive discomfort that we have with the numbers, especially when you have to explain them to someone from overseas who is evaluating the potential of the Indian market with a view to investing in it. "How can any one who earns so little, afford to buy so many things, and still manage the living expenses of a family of five?", they ask, puzzled! We can definitely vouch for the fact that the income data is generated by reputed, world class organizations, using rigorously designed, huge sample size surveys that would satisfy any survey data excellence standard, anywhere in the world. So, there is no "survey science" flaw on which to hang our discomfort with the data.

Obviously something isn't adding up. For example, consider the NCAER data (2001-02, the latest they have available). They describe a lower middle tier of consumers that they call the climbers, earning between Rs.2-5 lakhs a year, 70% of whom have basic durables like TVs and refrigerators, a little less than one third of whom have entry level cars and 13% have ACs. Take their next category - the upper middle class that they call the strivers. The survey data shows them as having a annual income between Rs.5 -10 lakhs a year and on that income, one in two have cars, and others luxuries. Since the number of rich households earning Rs.10 lakhs or more a year is a mere 8 lakh households in 2001-02 (and about 11 lakh households now), they alone cannot be contributing to all the consumption increase that we are seeing from the supply side. And we are not that much of a credit driven society in any case. So the consumption data must be correct.

Consumption data, is like maternity. A certainty. Income data is like paternity. A matter of opinion. Various people have changed survey income data to suit their logic comfort levels. For example, a presentation made by one of the big consulting firms, arbitrarily moved all the income categories upwards by about 40%, claiming that "team analysis" had led them to conclude that this is the extent of understatement of income that people give in surveys, to save themselves from income tax. But these are fact free analyses which are high on conjencture.

But, income data has its pluses...
Interestingly, as we will show later in this article, all survey income data produces more or less the same results of income distribution ie what income percentile has how much of the income. There is very consistent lying (under-reporting) from the people of India when asked the question on income - no matter which agency does the survey. We are forced to conclude that the income data we are getting is what, in statistics-speak, we would call 'reliable' but not 'valid'. 'Not valid' because it does not measure what it is supposed to. But reliable because it repeatedly identically gives you the same result on whatever it is, that it is measuring.

Obviously, the survey income data is some measure of exactly under reported income or expenditure data, which is on an interval or a relative scale, where the distance or difference between Rs.500 000 and Rs.530 000 on the scale, is the same as the distance or difference between Rs.60,000/- and Rs.90,000/-. However purchasing power has to be an absolute number, which can be compared with other such numbers from around the world. So the relative scale of income distribution doesn't really help; except to make comparisons between people or between periods of time.

And GDP per capita data its minuses
And so business leaders, economists, politicians, equity researchers - in short everyone other than marketing folk who think of markets as made of people and not macro statistics - prefer to use the GDP per capita or related number as the real income number. Yes this is a reliable and hopefully a valid number. So Indian GDP per capita in 2003-04 is US $ 550, and we know exactly where this stands relatively to any other country. And with the notion of Purchasing Power Parity, at least intellectually, the concept is clear even if not intuitively or strategically!

The only trouble with per capita GDP or any such macro number is that you cannot identify people (consumers) based on their per capita GDP, and band then together, and then study each band (or per capita GDP consumer segment) in further detail. Therefore we do not know what people in each per capita GDP category own, and how this is changing over time, where they live (in terms of town class and so on). Most of all, there is a magic number that people use of GDP per capita above which, they say consumption will 'take' off. This number ranges from US $1200 to US $2000. And is used often to determine the size of the consuming class in India. However we do not know whether this magic number, no doubt empirically validated from other economies around the world, makes sense for this market because a market's potential in terms of how many can afford to consume depends on (a) income levels and (b) cost of goods. We have seen 2 wheelers and telecom take off at well below the magic number because price thresholds were discontinuously lowered but performance maintained.

Therefore we decided to get together as a team and look at all available income constructs and see how they relate to each other. The purpose of doing this is not to arrive at a single measure of affluence for all to use; but to enable a more informed choice. Further, recognizing that given the limitations of each, multiple measures will need to be used, the endeavour is to be able to establish some consonance between GDP per capita and survey data on income or expenditure.

We have included this article in the BW White Book because we felt that this academic detour could form the necessary lens with which to look at the rest of the data in this book.


Table 1. Components of National Income at Current Prices,2003-04 

Economic Indicators (Refer Box 1 for definition) Total Per Capita Annualized Growth Rate between 1993-94 and 2003-04
Rs. Billion US$ Billion $ PPP terms Rs. US$ $ PPP terms
Gross Domestic Product 27,600 599 3,036 25,356 550 2,789 6.18
National Income 22,520 489 2,477 20,690 449 2,276 6.41
Net National Disposable Income 25,971 563 2,856 23,860 518 2,624 6.55
Private Income 25,296 549 2,782 23,240 504 2,556 6.73
Personal Income 24,219 525 2,664 22,250 483 2,447 6.59
Personal Disposable Income 23,585 512 2,594 21,667 470 2,383 6.57
Domestic Saving of Household Sector 5,799 126 638 5,328 116 586 9.77

Box 1: Definitions 

Gross Domestic Product: Total value of goods and services produced by a nation.
Net National Disposable Income: (Net value of all goods and services produced in a nation's economy, including goods and services produced abroad at market prices) + (Other net current transfers from rest of the world)
Private Income: (Income accruing to private sector from domestic product) + (Interest on public debt) + (Current transfers from govt. administrative departments) + (Other net current transfers from rest of the world) + (Net factor income from abroad)
Personal Income: (Private income) - (Saving of private corporate sector net of retained earnings of foreign companies) - (Corporation tax)
Personal Disposable Income: (Personal Income) - (Direct taxes paid by households and miscellaneous receipts of govt. administrative departments)
Domestic Saving of the household Sector: Financial saving and saving in fixed assets by the household sector.

Even though India's total household income and saving can be known from National Accounts Statistics, it does not provide the same information across economic groups. Therefore, the pattern of distribution of total income and saving across households with different economic status is not known. Thus, "What share of India's total personal disposable income comes from the richest 10% of the households?" or "Do the poorest 10% of the households save anything at all?" - these questions remain unanswered. Moreover, per household income or saving for households with different economic status is also not known. This paper tries to find out how India's total personal income and saving differs across different economic groups.

Overview of surveys
There are three surveys that, between them form the holy grail of income / affluence data, with different users consistencies being partial to one of these. These are the NSSO expenditure survey (National Sample Survey Organisation) of the Government of India; the NCAER (National Council for Applied Economic Research) household survey called MISH and the marketing world's favourite, the IRS (originally christened the Indian Readership Survey), conducted by Hansa Research for the Media Research User's Council.

In 2004-05, we added another interesting survey called the National Data Survey on Savings Patterns of Indians (NDSSP), conducted for the Ministry of Finance, overseen by lowest India Economic Foundation and conducted by AC Nielsen.

All these surveys have an all India sample - details of which are available on the individual websites. The IRS covers 240 000 households 120,000 in each half of any year with the data reported on a rolling basis. It however has a straight and simple income elicitation measure using a show card with different income categories written on them. The NSSO covers a thick sample of 120,000 households and a thin sample of 40,000 each year; it is an expenditure survey but also collects data on income and wages but does not include self employed and businessmen. The NCAER MISH covers about 300 000 households and also asked the respondent his or her perceived income the way the IRS does.

In our knowledge the NDSSP is the only all India survey in recent times that has a specific method for ascertaining the incomes of the respondents. Incomes for wage earners are easy enough to ascertain; however, for self-employed, entrepreneurs, farmers, fisherman, etc. simply asking a question on income can yield poor results as respondents may confuse revenues with incomes. For non-wage earners of all types, the survey tool specifically queried respondents on the revenues from their business and expenditures related to business. The income was then specifically derived. This measurement of income is sharper than the IRS, which asks respondents to indicate which income category their household falls into, thus measuring household income as belonging to an income band, rather than eliciting a specific number.

Comparison of Results

Pattern of Income Distribution 1999 / 2004Income Shares * % 

  Quintile 1 (lowest) Quintile 2 Quintile 3 Quintile 4 Quintile 1 (Highest)
NDSSP adj 2004 4.8 8.7 13.2 20.5 52.8
NSS 1999 4.0 7.9 12.2 20.6 55.3
NCAER MISH 1999 6.3 10.5 15.3 22.2 45.7
IRS 2005 4.8 9.0 13.7 21.4 51.1

Source: Bhalla, Surjit Singh. (2004), Reforming Personal Income Tax in India, Oxus Research & Investments, New Delhi, India.. NACER, NDSSP: National Data Survey on Saving Patterns, NSS: National Sample Survey, NCAER-MISH: National Council for Applied Economic Research - Market Information Survey of Household and Indicus Estimates, IRS: Hansa Research conducted for MRUC

*  Household income shares for NDSSP, NSS, IRS Per Capita income shares for NCAER. 
**  NDSSP adjustments have been made based on Indicus Analysis, to derive income and savings of the total household, based on the data of one earning member, and the demographic profile of other members of the household, using the survey data of members interviewed with similar profiles in other households. For details, see

All sources of survey data, especially the NDSSP adjusted and IRS, have almost identical percentage distributions of what the income share of each income quintile is, thus showing the data to be reliable and robust in percentage distribution terms, even if not comparable on the absolute incomes.

For the rest of this analysis, we have decided to adopt the NDSSP survey distributions on income and savings as the standard survey data to use and not the IRS or NCAER, because IRS data is available only by quintiles, not deciles; and NCAER data is too dated.

Household income and savings distribution, from NDSSP (adjusted), 2003 -4, and expenditure distribution
from NSS

Deciles Income Distribution(%) Saving Distribution(%) Expenditure Distribution(%)
1 (lowest) 2.0 0.6 2.5
2 3.2 1.4 3.8
3 4.1 2.2 4.7
4 5.4 3.6 6.0
5 6.2 4.5 6.8
6 8.8 7.3 9.3
7 8.4 7.1 8.9
8 11.9 11.1 12.2
9 15.8 16.8 15.5
10 (highest) 34.1 45.3 30.4
Total 100 100 100
Top 20% 49.9 62.1 45.9
Top 5% 22.7 31.4 19.9
Top 1% 8.6 12.6 7.3

The higher the income per capita per household, the higher their savings as well. 34% of total income is earned by top 10% of the households. When we look at the saving pattern across economic status, it is found that as high as 45.3% of the savings comes from the richest 10% of the households.

The poorest 20% of the households contribute just 5% to India's total personal disposable income. In case of household saving also, only 2% of the savings come from these households, and to apply these distributions to all macro data, so that we can get a distribution of macro indicators across income deciles - thus bridging the gap between the more valid macro numbers with no distribution available, and the less valid survey numbers which have a distribution available.



As we said earlier, the endeavour of this exercise is to create a bridge that can link survey data (plus: provides a reliable distribtution, minus: provides in valid absolute numbers) and macro data on income and savings (plus: valid absolute numbers, minus: no distribution available), so that we can have the best of both worlds.

We now apply these survey data distributions to all macro data, so that we can get a distribution of macro indicators across income deciles - thus bridging the gap between the more valid macro numbers with no distribution available, and the less valid survey numbers which have a distribution available.

Personal Disposable Income , Household Sector Saving and GDP across Deciles, All India, 2003-04 

Deciles Personal Disposable Income (Rs. billion) Saving (Rs. Billion) Per Capita Income(Rs.) Per Capita Saving(Rs.) Per Household Income (Rs) Per household savings (Rs)
1 (lowest) 471.25 32.62 3,813 264 22806 1579
2 752.21 80.80 6,437 691 36167 3885
3 968.19 130.33 8,806 1,185 49537 6668
4 1,281.29 209.09 11,066 1,806 59909 9776
5 1,465.41 263.56 13,750 2,473 75990 13667
6 2,083.93 423.65 17,034 3,463 89123 18118
7 1,992.33 413.51 21,061 4,371 115168 23903
8 2,809.31 645.99 26,910 6,188 137889 31707
9 3,725.28 971.78 37,602 9,809 183476 47862
10 (highest) 8,035.84 2,627.84 88,940 29,085 395551 129351
Total 23,585.03 5,799.17 21,767 5,352 115981 28518
Top 20% 11,761.12 3,599.61 62,090 19,003 289544 88618
Top 5% 5,365.25 1,818.12 124,642 42,237 527731 178832
Top 1% 2,038.44 732.12 257,041 92,318 1017456 365425

India's total personal disposable income during 2003-04 was Rs. 2,358,503 crore, and the domestic savings of the household sector were approximately Rs.5,799,00 crore.

For those who prefer to think in terms of US Dollars GDP per capita, the top 10% of Indian households have a GDP of USD 204.3 billion, translating into a per capita GDP of USD 1857; while the lowest 10% have a GDP of USD 12 billion translating into a per capita GDP of USD 109. For those who do believe that USD 1200 per capita GDP is indeed the magic number at which consumption takes off, the size of the consumer base is a bit over 20% of India, a population of 220 million or so.

Deciles GDP (USD billion) Population (mn) GDP Per Capita (USD)
1 (lowest) 12 108.8 110.2
2 19.2 108.8 176.3
3 24.6 108.8 225.8
4 296.6 108.8 32.3
5 37.1 108.8 340.7
6 47.9 108.8 439.9
7 50.3 108.8 461.7
8 71.3 108.8 654.7
9 94.6 108.8 868.7
10 (highest) 204.3 108.8 1876
Total 599 1088 550
Top 20% 298.9 217.6 1373.6
Top 5% 2500 54.4 136
Top 1% 51.5 10.9 4733

Finally, linking consumption / ownership to GDP Per Capita
Now that we have established the robustness of the survey data like the IRS in terms of getting income shares of income percentiles right, and applied those shares to GDP per capita, now the last step is to link the GDP per capita to the extent of consumption that happens at each level of GDP per capita.

Rama Bijapurkar is an independent market strategy consultant; (
Laveesh Bhandari is the founder and head of Indicus Analytics.(
IRS Analysis, courtesy Vineet Sodhani,, Hansa Research (