Type of Data set Source Yearly Time series
Primary Census Abstract (PCA) Census of India, RGI 2011
Estimation of Population & Number of Households Census of India, Akara’s Estimations 2012 onwards
Estimation of Purchasing Power RBI, Akara’s Estimations 2012 onwards
Estimation of PFCE NSSO, Akara’s Estimations 2012 onwards
Estimation of Index of Economic Activity (IEA) RBI, Akara’s Estimations 2012 onwards

Type of Data set Districts For Districts, Whether Rural & Urban Breakup Provided Towns RBI Banking Centers*
Primary Census Abstract (PCA) Available Yes Available Not available
Estimation of Population & Number of Households Available Yes Available Not available
Estimation of Purchasing Power Available Yes Available Available
Estimation of PFCE Available Yes Available Available
Estimation of Index of Economic Activity (IEA) Available Yes Not available Not available

*A banking centre is defined as an administrative unit – usually a town/panchayat-town or a village – which has at least 3 bank branches of any scheduled commercial banks of India.

  • RBI publishes credit and deposit data for about 6000 banking centres, every quarter.
  • Estimation of population for these banking centres basis Census 2011

It may be noted that except for these two information, there is no other standard, defined, socio-economic information for these units in India. Therefore, we rely on these two information types for the estimation of purchasing power and per-capita purchasing power for these banking centres. It is possible that these banking centres could have been village panchayats at the time of Census 2011, or specified as towns.


The number of households and population for the district/town projected based on 2001 and 2011 Census growth rates for rural/urban/town. This is the estimated number of households in the given income group in the selected district/town The number of households and population is obtained by projecting the total households (rural/urban/town) from 2011 based on decennial growth rate for 2001-2011. The growth rates are separately obtained for rural and urban households

The estimation of Purchasing Power (PP) or Disposable Income is based on observed data at the district level from the Reserve Bank of India.

The overarching hypothesis is the observed strong correlation between state per-capita GDP and state level aggregates of banking sector data adjusted for district's population. The State level purchasing power is then a function of the banking sector data. The state purchasing power is broken down for the district level based on the district level observed values of banking sector data. The rural and urban break-up is also available based on banking parameters.

The total number of households of a district/town is projected based on the decadal growth rates in Census, between 2001 and 2011; the rural and urban growth rates are separately obtained for each district, as also for the respective town.

The Purchasing Power estimates include

  • Distribution of HHs by Income Groups
    The number of households in different income groups estimated based on the per-capita purchasing power, spread of economic activities, and the NSSO estimates on the expenses.
  • Distribution of Purchasing Power by Income Groups:
    This is the combined purchasing power of the households in each income group for the district. In other words the total purchasing power of a district is split among the households in different income groups. This is a function of the number of households in each income group.

Purchasing power for centres is based on analysis of parameters such as credit and deposit of the respective center which is analysed as a function of the total banking business of the district. This relationship is used to estimate the purchasing power at the centre-level .

Purchasing power is estimated for each year. The user can select any set of time periods, and the system computes the compounded annual growth rate (CAGR) in terms of percent per annum.

CAGR = [ (EP/SP)^(1/n) ]– 1

Where,   EP = End period,   SP = Start period,   n = number of years

According to the Ministry of Statistics and Programme Implementation (MoSPI), private final consumption expenditure (PFCE) is defined as the expenditure incurred on final consumption of goods and services by the resident households and non-profit institutions serving households (NPISH)

Akara’s PP estimates at the district-level amount to more than 90% of the All-India PFCE figures released by the Ministry of Statistics and Programme Implementation (MOSPI) annually. Therefore, a strong relationship is established at this juncture which is also consistent across timeseries.

The overarching hypothesis on estimation of PFCE is the relationship between PP estimates of Akara and the PFCE released annually by MOSPI, through this the district/town and centre PFCE estimates are computed.

Commodity-wise estimation:

For commodity-wise PFCE estimates, state-level data on food and non-food items is used for estimating commodity-wise proportion to total PFCE. Mapping of NSS to PFCE heads is presented in the Annexure. Based on the mapping of commodities, the aggregate PFCE for each commodity.

Mapping of Food & Non-Food items from NSS Data to PFCE
PFCE Commodity Component in NSS Data
Bread, cereals and pulses cereal
Meat goat meat/mutton
beef/ buffalo meat
Fish and seafood fish, prawn
Milk and milk products milk & milk products
Eggs Eggs
Oils and fats Edible Oil
Fruit Fresh fruits
Dry fruits
Vegetables Vegetables
Sugar, jam, honey, chocolate and confectionery Sugar
Jam, Jelly
Food products n.e.c. Spices
Cooked meals
Processed food
Sweets, cake, pastry
Biscuits, chocolates
papad, bhujia, namkeen
chips (gm)
pickles (gm)
Coffee, tea and cocoa Tea
Tea leaves
Mineral waters, soft drinks, fruit and vegetable juices mineral water (litre)
cold beverages (litre)
fruit juice and shake (no.)
Alcoholic beverages toddy (litre)
country liquor (litre)
beer (litre)
foreign/refined liquor or wine (litre)
other intoxicants
Tobacco Tobacco
Narcotics snuff (gm)
hookah tobacco (gm)
cheroot (no.)
zarda, kimam, surti (gm)
ganja (gm)
Clothing Clothing
Footwear Footwear
Gross rentals for housing house rent, garage rent (actual)
Water supply and miscellaneous services relating to the dwelling water charges
bathroom and sanitary equipment
plugs & other electrical fittings
residential building & land (repairing cost)
Electricity electricity (std. unit)
Gas L.P.G. (kg)
Liquid fuels Kerosene -PDS
Kerosene - Other sources
Other fuel
Solid fuels coke (kg)
firewood and chips (kg)
dung cake
matches (box)
coal (kg)
charcoal (kg)
candle (no.)
gobar gas
Furniture and furnishing, carpets and other floor coverings furniture & fixtures
Household textiles bed sheet, bed cover (no.)
rug, blanket (no.)
pillow, quilt, mattress (no.)
cloth for upholstery, etc (m.)
Household appliances cooking & household appliances
Glassware, tableware and household utensils crockery & utensils
Tools and equipment for house & garden torch
umbrella, raincoat
lighter (bidi/ cigarette/ gas stove)
other minor durable-type goods
Goods and services for routine household maintenance bucket & other plastic goods
coir, rope, etc.
washing soap/ soda/ powder
other washing requisites
incense (agarbatti), room freshener
flower (fresh), all purposes
mosquito repellent, insecticide, acid etc.
other petty articles
domestic servant/cook
barber, beautician, etc.
washerman, laundry, ironing
grinding charges
Health medical, institutional
medical, non-institutional
Purchase of vehicles bicycle
motorcycle, scooter
motor car, jeep
other transport equipment
Operation of personal transport equipment tyres & tubes
Transport services conveyance
Communication telephone charges, landline
telephone charges, mobile
postage & telegram
internet expenses
Audio-visual, photographic and information processing equipment radio, tape recorder, 2-in-1
VCR/VCD/DVD player
camera & photographic equipment
CD, DVD, audio/video cassette, etc
Other major durables for recreation and culture musical instruments
other goods for recreation
Other recreational items and equipment, gardens and pets pet animals (incl. birds, fish)
Recreational and cultural services entertainment
Newspapers, books and stationery books, journals, first hand
books, journals, etc., second hand
newspapers, periodicals
library charges
stationery, photocopying charge
Education tuition and other fees (school, college, etc.)
private tutor/ coaching
educational CD
other educational expenses
Restaurants and hotels cooked meals purchased (no.)
hotel lodging charges
Personal care toilet articles
Personal effects n.e.c spectacles
contact lenses, hearing aids, etc.
other medical equipment
therapeutic appliances
clock, watch
other machines for household work

4-Tier estimation from aggregate PFCE:

The aggregate PFCE by commodity is classified into four categories:

  1. Basic
  2. Intermediate
  3. Premium
  4. Luxury

The proportion of households in each category in a district (the income distribution) is used to estimate the sub-group-level estimate of PFCE for the district/town. In districts with larger number of households in higher income groups, the estimated PFCE in luxury items is higher. The population effect is taken in to consideration to account for the consumption of essential commodities.

The table below at the level of the relevant geography will appear as follows :

Consumption category Consumption (Rs. Crores) By District for CLOTHING
Ajmer Adilabad Agra
Basic 131.13 46.49 250.39
Intermediary 207.18 54.66 372.71
Luxury 180.64 30.55 189.06
Premium 936.31 158.86 600.22
PFCE Clothing total 1455.3 290.56 1412.4

Rationale: In the above example, for the same category of consumption, say, “Clothing”, Ajmer has higher estimated expenditure, compared to Adilabad. That is an obvious conclusion. But the interesting comparison is between two similar towns with similar total purchasing power – that is Ajmer and Agra. However, the redistribution of total purchasing power under the Clothing category will be a function of number of estimated number households (rural and urban, separately projected). Further the redistribution of estimated expenses under “Clothing” into different categories – Basic, Intermediary, Luxury and Premium will depend upon the income distribution of the respective districts.

4-Tier commodity classification:

Sub-classification of PFCE by type of consumption into the following levels

  1. Basic
  2. Intermediate
  3. Premium
  4. Luxury

The subclassification into the 4 classes of consumption takes into account the income distribution of the district/town.

Based on price, the same commodity can be classified into different categories

4-Tier commodity classification – Use-cases
Case 1: Coffee, tea and cocoa

For instance, a coffee in a road-side stall at Rs. 15 will constitute consumption as a basic good, while the same consumed in an ambient 5-star hotel is a luxury good. While we cannot provide an estimate of commodity consumed – for instance the consumption of coffee, we shall provide an estimate of PFCE on “coffee, tea and cocoa” at the district (with rural/urban breakup) and towns with breakup of consumption in the 4 broad categories.

Case 2: Cereals and pulses

Similar to the case of coffee, cereals can be classified into 4 groups. Basic consists of coarse grains, intermediate consists of unbranded grains available in loose-packaging, premium refers to branded cereals (FMCG companies) and premium consists of organic cereals. It is to be noted that the price-point is the key differentiator.

From the latest update of District Metrics, we have the data of total purchasing power in different income groups in the table below:

Distribution of Purchasing Power by Income Groups (March 2019) District
Tiruppur Vellore Tiruvannamalai
> Rs. 75000 1017 1262 933
Rs. 75,000-200,000 2306 2274 1193
Rs. 200,000-300,000 2645 2977 2285
Rs. 300,000-400,000 5981 7880 6754
Rs. 400,000-500,000 6180 6455 1426
Rs. 500,000-600,000 1597 1625 1006
Rs. 600,000-650,000 1134 1177 773
>Rs. 650,000 20130 16867 5383
TOTAL Purchasing Power (Rs.crore) 40991 40517 19753

As can be observed, the total purchasing power in Tiruppur and Vellore is similar. However, the income distribution is different which is shown in table below:

Distribution (%) of Purchasing Power by Income Groups (March 2019) District
Tiruppur Vellore Tiruvannamalai
Rs. 0-75000 2.48% 3.11% 4.72%
Rs. 75,000-200,000 5.62% 5.61% 6.04%
Rs. 200,000-300,000 6.45% 7.35% 11.57%
Rs. 300,000-400,000 14.59% 19.45% 34.19%
Rs. 400,000-500,000 15.08% 15.93% 7.22%
Rs. 500,000-600,000 3.90% 4.01% 5.09%
Rs. 600,000-650,000 2.77% 2.91% 3.91%
>Rs. 650,000 49.11% 41.63% 27.25%

Both the districts have the largest distribution of purchasing power in the highest income group. Yet, the category in the income group Rs. 3,00,000 to Rs. 4,00,000 per year there is a difference. Further, the estimated number of households in the different income groups for the districts as shown in table below:

Distribution of Purchasing Power by Income Groups (March 2019) District
Tiruppur Vellore Tiruvannamalai
Rs. 0-75000 15.92% 17.46% 20.46%
Rs. 75,000-200,000 21.76% 21.56% 21.71%
Rs. 200,000-300,000 11.59% 11.54% 14.03%
Rs. 300,000-400,000 18.72% 21.81% 29.62%
Rs. 400,000-500,000 15.05% 13.90% 4.87%
Rs. 500,000-600,000 3.18% 2.86% 2.81%
Rs. 600,000-650,000 1.99% 1.82% 1.90%
>Rs. 650,000 11.79% 9.04% 4.61%

So, the estimate of expenses under different categories of goods as in the PFCE table is a function of the total number of households, the distribution of households in different income groups and their respective purchasing power. In the given example, “Clothing” expenditure in stores such as Manyavar or purchase of silk sarees of Rs. 12,000/- and more could be classified as “Luxury/Premium”, while expenditure for shirts/chudidhars at Rs. 300 per piece could constitute “Basic”. What our estimate provides is: Given the total households in the district, and the distribution of the households into different income groups, what is the total quantum the district/town in question spends in different category f goods/services? Of this expense, for each product, what is the estimated potential to be spent in different price ranges?

How is Index of Economic Activity (IEA) estimated ?

Having provided the estimates of the purchasing power, and the potential expenditure into different category of goods, the major macroeconomic component at the district level is the source of income. That is, what are the economic activities that drive the purchasing power in a district? Akara provides the “Index of Economic Activities” to answer this question.

The index gives the relative contribution of the district to all India, the total India being equal to 1000 for the select economic activity. The data can be analysed across the columns as well as rows. A district's IEA for a chosen activity indicates its share in the business compared to all-India total for that business. This is for a summing on columns. Individual IEA value across a row indicates the relative importance of that activity in the district's purchasing power. For instance, Namakkal district in Tamil Nadu will have a lower value in most economic activities but a high value in transport (across row); Namakkal will have a larger share in transport IEA compared to other districts for the Transport IEA (along a column)

IEA is available for the following economic activities:
Agriculture Mining and quarrying Manufacturing and processing Electricity, gas and water
Construction Transport operators Professional and other services Loans for housing
Loans for purchase of consumer durables Rest of the personal loans Wholesale trade Retail trade
Finance Others Composite

The composite index of economic activity is based on the total business activity reflected in the banking sector. The composite index is useful in understanding the total business potential of the district.

  1. The scorecard is an index number for the district, with index assigned for certain key parameters.
  2. Our objective is to bring out the significant factors which have an impact on per-household purchasing power; as such, in our analysis per-household purchasing power is kept as the dependent variable.
  3. Districts which have the complete data for all parameters are selected.
  4. A multiple regression analysis is done, which reveals the strength and direction of relationship between the factors
  5. Results of multiple regression indicated a strong relationship between per-household purchasing and the following parameters, with the least muliti-collinearity amongst them have been used as the independent variables
    1. Rural female literacy rate: Obtained from Census 2011
    2. Urbanisation rate: Obtained from Census 2011 (total urban population as a percentage of total district population)
    3. Spread of economic activities: Obtained from Index of Economic Activity (IEA). This indicates the number of activities where the district’s rank is within top 100 of the country’s 610 districts.
    4. Growth rate of Purchasing Power: Obtained from Purchasing Power (PP) estimates for the district from 2004 and each district’s growth rate to reach 2015 January value.
    5. Growth rate of Economic activity: Obtained from IEA estimates for the district from 2003 and the district’s growth rate to reach 2014 March value.
    6. Volatility of Purchasing Power: Coefficient of variation for annual growth rates of purchasing power during the decade 2004-2014
    7. Volatality of economic activity: Coefficient of variation for annual growth rate of IEA during the decade 2004 – 2014.
  6. Principal component analysis (PCA) technique was used to assign weights to the parameters, including per-household purchasing power. For this, logarithmic values were obtained in order to standardize the data.
  7. Weights obtained are then assigned to individual district level parameters to obtain the final index value.
How to read the scorecard chart ?

The chart for each district depicts how the district performs in each parameter compared to the best performing district in that category. There could be different best performing district for each indicator. The user can judge the select district’s performance based on the distance from the best value shown in the chart. The greater the area covered by the district in the chart, the better is the score for the district.

For development practitioners and policy makers, the chart is an indicator of where the district could focus for better economic performance.

How to interpret spider chart ?

The spider chart is used to help users interpret the relative performance of a district on different parameters vis-à-vis the other.

“A spider chart represents the values of various parameters obtained by a district/town in a 2-dimensional axis, and helps in comparing the value of the parameter against another district/town. In this case, we provide the comparison to , the best index in that category”

Relative Performance of a district
Chosen District (For ex: Uttar Dinajpur) Value Chosen District (For ex: Uttar Dinajpur) Rank Best value in the category District with best value in this category
Per household purchasing power 0.158 467 0.175 Mumbai
Rural female literacy rate 0.152 497 0.184 Mahe
Urbanisation rate 0.134 460 0.249 Kolkata
Spread of Economic Activities 0.068 187 1.019 Kolkata
Growth rate of Purchasing Power 0.170 169 0.246 Kurung Kumey
Growth rate of Economic activity 0.145 495 0.260 Sonipat
Volatility of PP 0.159 338 0.289 Saran
Volatality of economic activity 0.188 150 0.295 Shajahanpur
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