It has been a longstanding need to have a database on the resources available in the state. Thus, the idea of a Resource Mapping Exercise occurred to comprehend the availability and distribution of raw materials and human resources in the region. Proposal for Multidisciplinary Skill Development Centres (MDSD) and formulation of a new State Industrial Policy effecting from 2013, have finally triggered to bring about the exercise. Nevertheless, importance of such a mapping, providing information to the entrepreneurs, researchers and policy makers can never be ruled out. The outcome of the survey is perceived as a major breakthrough for decision making and understanding local competency.

Data plays a vital role in decision making and policy formulation. Setting out the Resource Mapping Survey, collection of data on various aspects had been considered to be the paramount task. Task of data collection on Raw Materials, Human Resources and Infrastructure had been taken up at the developmental block level. The State has 219 such blocks under 27 districts and Field Officers of the Industries and Commerce Department had been engaged extensively to go on data collection drive. Local Panchayats and Statistical Sub-Inspectors under the BDOs had been relied on while gathering data. Data so collected had been verified later with the secondary data available with the government agencies and other reliable sources.

A questionnaire had been designed for collection and recording of data. Data recording has mostly been in pre-assigned codes. Coding of data ensures uniformity and relieves the investigators of tedious recording procedures. Field Officers were assigned to fill in the questionnaire for each Block.

Data collected in the questionnaire are about occurrence, availability and usage of a resource. Different codes have been assigned for qualitative values or ordinal variables of each of these parameters. Each resource would therefore assume a code combination of three digits. Every permutation of the codes recorded has a distinct interpretation as to the competency of the resource. Economic viability of a resource has a direct bearing on its availability and usage. For example, in the following table, codes for rice, pineapple and lemon are recorded as 111, 114 and 248 respectively. Code 248 shows that availability of lemon in the locality is nil. On the other hand, according to the first two digits, both rice and pineapple have high availability (code 11 each). However looking at the third digit (rice 1 and pineapple 4), rice is used for consumption whereas pineapple goes un-utilised. As such, pineapple has a competitive edge over the other item from entrepreneurs' or investors' perspective. Based on this principle economic viability of the resources has been analysed.

# | Raw Material | Occurrence [*] | Availability [* *] | Present Usage[*^*] |
---|---|---|---|---|

i | Rice | 1 | 1 | 1 |

ii | Pineapple | 1 | 1 | 4 |

iii | Lemon | 2 | 4 | 8 |

Statistical regression methodology has been applied to deduce the economic viability of the resources.

Resources available in the developmental blocks have been categorised by assigning scores according to their viability through a multiplicative model. Based on the score so derived for a resource, the economic viability of the resource varies in degrees, such as 'High', 'Moderate', 'Low' or 'Nil'. On the basis of the score obtained by a particular resource across the blocks, blocks too are arranged according to the degrees as mentioned above.

Where,

**S :** viability score

**x :**
Indicator variable, stands for 'Occurrence' of raw materials taking
values x=1 while value of occurrence is 'Yes' or x=0 while value of
occurrence is 'No'

**y :** Numerical variable,
stands for 'Availability' of raw materials taking values y > 0 when
the value of x = 1 and y = 0 when x = 0. Corresponding to the degree
of availability of raw materials following values for y have been
deduced.

Availability of raw material | Normal Distribution | Value of y |
---|---|---|

Consumption, Sale & Processing | x+3σ | 80 |

Consumption only | x+1σ | 60 |

Not enough for consumption | x | 50 |

Not applicable | 0 |

Approximation of Normal Distribution method (x + kσ) is used for arriving at the values of y for various degrees of availability of raw materials by taking mean (standard) x as 50 and standard deviation s as 10.

Again, the third variable z, related with 'Present usage' takes various values corresponding to the combination of availability and usage. Here the nature of z is just like a catalyst. So, it takes the value with standard 1. Greater the value of z, more is the viability of the resource as a commodity or raw material. Value of z corresponding to different combination of codes can be viewed here.

Code | Expansion of the code | Grading | Score | Computation | ||||
---|---|---|---|---|---|---|---|---|

occurence=x | Availability=y | Usage=z | x | y | z | RTs=xyz | ||

111 | Y | CSP | C | 8 | 1 | 80 | 1.8 | 144 |

112 | Y | CSP | S | 7 | 1 | 80 | 1.7 | 136 |

113 | Y | CSP | P | 5 | 1 | 80 | 1.5 | 120 |

114 | Y | CSP | W | 9 | 1 | 80 | 1.9 | 152 |

115 | Y | CSP | Csp | 4 | 1 | 80 | 1.4 | 112 |

116 | Y | CSP | CS | 6 | 1 | 80 | 1.9 | 152 |

117 | Y | CSP | SP | 4 | 1 | 80 | 1.4 | 112 |

121 | Y | C | C | 6 | 1 | 60 | 1.6 | 96 |

122 | Y | C | S | 8 | 1 | 60 | 1.8 | 108 |

123 | Y | C | P | 7 | 1 | 60 | 1.7 | 102 |

124 | Y | C | W | 9 | 1 | 60 | 1.9 | 114 |

125 | Y | C | CSP | 3 | 1 | 60 | 1.3 | 78 |

126 | Y | C | CS | 3 | 1 | 60 | 1.3 | 78 |

127 | Y | C | SP | 3 | 1 | 60 | 1.3 | 78 |

131 | Y | NE | C | 6 | 1 | 50 | 1.6 | 80 |

132 | Y | NE | S | 8 | 1 | 50 | 1.8 | 90 |

133 | Y | NE | P | 7 | 1 | 50 | 1.7 | 85 |

134 | Y | NE | W | 9 | 1 | 50 | 1.9 | 95 |

135 | Y | NE | CSP | 3 | 1 | 50 | 1.8 | 65 |

136 | Y | NE | SP | 3 | 1 | 50 | 1.8 | 65 |

137 | Y | NE | SP | 3 | 1 | 50 | 1.8 | 65 |

248 | N | NA | NA | 0 | 0 | 0 | 0 |

Finally, score S is derived by multiplying x, y and z and based on the scores, resources have been placed under the following categories of viability:

High | Low | Moderate | Nil |
---|---|---|---|

110≤S | 85≤S<110 | 0<S<85 | S=0 |

Where,

**S :** Commercial viability of HR

**x
:** Indicator variable, stands for 'Occurrence' of human
resources taking values x=1 while value of occurrence is 'Yes' or
x=0 while value of occurrence is 'No'

**y :**
Numerical variable, stands for 'commercial viability' of human
resource taking values y > 0 when the value of x = 1 and y = 0 when
x = 0. Corresponding to the degree of commercial viability of human
resources following values for y have been deduced

Commercial viability of HR | Normal Distribution | Value of y |
---|---|---|

Good | x+3σ | 80 |

Fair | x+1σ | 60 |

Poor | x | 50 |

Not applicable | 0 |

Approximation of Normal Distribution method (x + kσ) is used for arriving at the values of y for various degrees of viability of human resources by taking mean (standard) x as 40 and standard deviation σ as 10.

Again, the third variable z, related with 'Present usage' takes various values corresponding to the combination of availability and usage. Here the nature of z is just like a catalyst. So, it takes the value with standard 1. Greater the value of z, more is the viability of the resource as a commodity or raw material. Value of z corresponding to different combination of codes can be viewed here.

Code | Expansion of the code | Grading | Score | Computation | ||||
---|---|---|---|---|---|---|---|---|

occurence=x | Viability=y | Usage=z | x | y | z | HTs=xyz | ||

111 | Y | Good | Own | 2 | 1 | 80 | 1.2 | 96 |

112 | Y | G | Comm | 4 | 1 | 80 | 1.4 | 112 |

113 | Y | G | W | 1 | 1 | 80 | 1.1 | 88 |

114 | Y | G | OC | 4 | 1 | 80 | 1.4 | 112 |

121 | Y | Fair | Own | 2 | 1 | 60 | 1.2 | 72 |

122 | Y | F | Comm | 4 | 1 | 60 | 1.4 | 84 |

123 | Y | F | W | 1 | 1 | 60 | 1.1 | 66 |

124 | Y | F | OC | 4 | 1 | 60 | 1.4 | 84 |

131 | Y | Poor | Own | 2 | 1 | 40 | 1.2 | 60 |

132 | Y | P | Comm | 4 | 1 | 40 | 1.4 | 70 |

133 | Y | P | W | 1 | 1 | 40 | 1.1 | 55 |

134 | Y | P | OC | 4 | 1 | 40 | 1.4 | 70 |

245 | N | NA | NA | 0 | 0 | 0 | 0 |

Finally, score S is derived by multiplying x, y and z and based on the scores, quality of infrastructure have been placed under the following categories:

High | Low | Moderate | Nil |
---|---|---|---|

88 ≤ S | 66 ≤ S < 88 | 0 < S < 66 | S=0 |

Where,

**S :** Accessibility of infrastructure

**x :** Indicator variable, stands for 'availability'
of general infrastructure taking values x=1 while value of
availability is 'Yes' or x=0 while value of availability is 'No'

**y :** Numerical variable, stands for 'Accessibility'
of the general infrastructure taking values y > 0 when the value of
x = 1 and y = 0 when x = 0. Corresponding to the degree of
accessibility of the general infrastructure the following values for
y have been deduced.

Accessibility of infrastructure | Normal Distribution | Value of y |
---|---|---|

Easy | x+2σ | 60 |

Moderate | x+1.5σ | 55 |

Difficult | x | 40 |

Not applicable | 0 |

Approximation of Normal Distribution method (x + kσ) is used for arriving at the values of y for various degrees of accessibility of the infrastructure by taking mean (standard) x as 40 and standard deviation σ as 10.

The third variable z, related with 'Condition' takes various values corresponding to the combination of degree of accessibility and condition. Here the nature of z is just like a catalyst. So, it takes the value with standard 1. Greater the value of z, better is the infrastructure. Value of z corresponding to different combination of codes can be viewed here.

Code | Expansion of the code | Grading | Score | Computation | ||||
---|---|---|---|---|---|---|---|---|

occurence=x | Accessibility=y | Condition=z | x | y | z | GITs=xyz | ||

111 | Y | Easy | Good | 3 | 1 | 60 | 1.3 | 78 |

112 | Y | E | Fair | 2 | 1 | 60 | 1.2 | 72 |

113 | Y | E | Poor | 1 | 1 | 60 | 1.1 | 66 |

121 | Y | Moderate | Good | 3 | 1 | (55) | 1.3 | (72) |

122 | Y | M | Fair | 2 | 1 | (55) | 1.2 | (66) |

123 | Y | M | Poor | 1 | 1 | (55) | 1.1 | (61) |

131 | Y | Difficult | Good | 3 | 1 | 40 | 1.3 | 52 |

132 | Y | D | Fair | 2 | 1 | 40 | 1.2 | 48 |

133 | Y | D | Poor | 1 | 1 | 40 | 1.1 | 44 |

244 | N | NA | NA | 0 | 0 | 0 | 0 |

Finally, score S is derived by multiplying x, y and z and based on the scores, resources have been placed under the following categories of viability :

Easy | Moderate | Difficult | Nil |
---|---|---|---|

70 ≤ S | 60 ≤ S < 70 | 0 < S < 60 | S=0 |

Again, depending on the degrees of viability or potentiality of a resource in various blocks under a district, the district can be compiled as 'Major', 'Medium' or 'Deficit' for the particular resource. Weighted average method has been applied for scoring the district in respect of a resource taking its commerciality in all the blocks within the district. In this method blocks with non availability of the resource would not be considered.

Thus

**S={(80xP)+(50xQ)+(40xR)}/(P+Q+R),**

where

**P=No. of 'Major' blocks Q=No. of 'Medium' blocks
R=No. of 'Minimum' blocks**