|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.
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
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.
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.
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.
|PFCE Commodity||Component in NSS Data|
|Bread, cereals and pulses||cereal|
|beef/ buffalo meat|
|Fish and seafood||fish, prawn|
|Milk and milk products||milk & milk products|
|Oils and fats||Edible Oil|
|Sugar, jam, honey, chocolate and confectionery||Sugar|
|Food products n.e.c.||Spices|
|Sweets, cake, pastry|
|papad, bhujia, namkeen|
|Coffee, tea and cocoa||Tea|
|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)|
|foreign/refined liquor or wine (litre)|
|hookah tobacco (gm)|
|zarda, kimam, surti (gm)|
|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)|
|Liquid fuels||Kerosene -PDS|
|Kerosene - Other sources|
|Solid fuels||coke (kg)|
|firewood and chips (kg)|
|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|
|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|
|barber, beautician, etc.|
|washerman, laundry, ironing|
|Purchase of vehicles||bicycle|
|motor car, jeep|
|other transport equipment|
|Operation of personal transport equipment||tyres & tubes|
|Communication||telephone charges, landline|
|telephone charges, mobile|
|postage & telegram|
|Audio-visual, photographic and information processing equipment||radio, tape recorder, 2-in-1|
|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|
|stationery, photocopying charge|
|Education||tuition and other fees (school, college, etc.)|
|private tutor/ coaching|
|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|
|other machines for household work|
The aggregate PFCE by commodity is classified into four categories:
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|
|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.
Sub-classification of PFCE by type of consumption into the following levels
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
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.
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|
|> Rs. 75000||1017||1262||933|
|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|
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|
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?
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)
|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|
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.
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.
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”
|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|
|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|