Proprietary Research

An important element in MICA's mission statement is to conduct research as applied to the needs of the marketing and communications industry. The MICA Rural Market Ratings (MRMR) is the outcome of this objective.

It's our studied belief that, increasingly, in the future, organisations with a broader perspective will need to penetrate deeper into different strata of our population that is now nearing the billion mark. This will be true, whether they be marketing companies, development agencies, NGOs or academia.



Rationale for the study

Today, rural markets - in terms of volumes and growth - have already overtaken urban markets in many categories of mass-consumption branded goods. And, for many companies, a strong presence in the rural markets has even now become crucial for achieving corporate objectives. Prosperity in agriculture, widening retail network, and impact of TV are expected to further boost the importance of the rural sector in the near future.

This trend leaves little doubt in our minds that corporates that have a perspective on the rural scene and an understanding of the opportunities available will stand to have a major advantage. To this extent we are sure that the information contained in the MICA Rural Market Rating will prove to be valuable and essential to all who have rural India in their scheme of things.


Content Overview

MICA Rural Market Ratings is a comprehensive guide, which is designed to serve the above-mentioned needs. Data has been collected based on the 1991 census reports. All the 459 districts of the country have been covered. A total of 42 socioeconomic indicators have been included, districts have been taken as units and the rating denotes the relative market potential of the respective districts. This study is presented in the form of an interactive, multimedia CD-ROM which is divided into the following three parts:

A. Digital Maps
All the districts in the country including those of Jammu & Kashmir are included in the maps. These cover:
a) Boundaries of districts
b) Location of tehsil headquarters
c) National highways
d) State highways
e) Metalled roads
f) Railway lines along with railway stations
g) All urban centers
h) Names of all 41,888 places where haats (weekly bazaars) are held
i) Days of the week when haats are held
j) Distance from the nearest town

The software allows you to:
1. View the maps in various formats
2. Search or retrieve data in desired clusters
3. Print data

B. Rural Socioeconomic Indicators
A total of 42 socioeconomic indicators are given for all the districts in the country except the 14 districts of Jammu and Kashmir. For each district the socioeconomic indicators are classified in the following categories:

Demographics
Major Occupations
Communication methods
Education profile
Shops and other establishments
Commercial Banks
Agriculture data
Medical facilities
Major crops of the district

Click here to see sample data sheets

C. Names and population of all villages in India
The data covers each of the 631,307 villages in the country.


Methodology

1.The primary objective of the MICA Rural Market Ratings exercise is to derive a single value for the district, which can reflect the market potential of the rural sector, for that district, as close to reality as possible. It is well recognised that the market potential of any area may largely depend on the total population of the area, however, the socioeconomic status of the area also plays an important role. This is more so with regard to the rural sector. In a vast country like India, s single economic indicator is neither available nor feasible.

MICA Rural Market Ratings is based on a linear combination of six variables, which have formed the basis for measuring the market potential. These are:
Number of cultivators
Total cropped area
Total irrigated area
Fertiliser consumption
Bank credit
Value of agricultural output

The methodology for determining these variables and deriving the coefficients in the linear combination is described in detail in the following paragraphs.

2.The methodology consists of two steps. Step 1, identification of important variables and Step 2, derivation of linear combination formula.
A total of 19 demographic and economic variables pertaining to rural sector were listed, after taking into consideration the availability of district -wise information for these variables. The 19 variables considered were:

District wise information for all variables was collected from various secondary sources; however, the value of agricultural output has been calculated by multiplying the production of crop in the district by the average national level price of the crop. In, all 42 crops have been taken into account. It has been verified, that the value of agricultural output so derived, accounts for over 75% of the Total Agricultural Production Value at the state level.

The criterion adopted for selecting the important variables has been that they must be related to each other to "varying degrees." Interpreted in marketing terms, such a group of variables present different perspectives of the district. The extent of relationship is measured by the correlation coefficient between the variables.

To begin with, pair-wise correlation coefficients were calculated among all the 19 variables. The average of these correlation coefficients, for each variable with respect to the other 18, was used as the basis for selecting the important variables.

The following six have been selected as important variables. The figures give the average of the correlation coefficients of each variable with the other 18 variables.

It can be seen that the selected variables are highly relevant in understanding the market size. This aspect has been ratified from experts.

3. For arriving at the linear combination of the six variables, the criterion of 'best discriminating function' has been adopted.

Having identified the six important variables, a Discriminant Analysis was carried out choosing one of the six variables as criterion variable and the other five as predictor variables. This gave a set of coefficients for the five variables to be used for that linear combination. With a view to increase the discriminating efficiency of the linear combination, the districts were first arranged in the increasing order of the value of the criterion variable and groups were formed by grouping the consecutive five districts.

This exercise was repeated five times over, each time considering one of the variables as criterion variable and the rest as predictor variables.

The coefficients for the final linear combination have been arrived at by taking the average of the coefficients obtained for each variable in the above rotation exercise. Thus, the coefficients for the linear combination for deriving market rating are as under: