chapter-7
Analytical technique
For this part of the study we have to use the “Dummy Variables or Binary Variables (A dummy variable is a variable which we construct to describe the development or variation of the variable under-consideration) according to the supervisor and also due to the quantitative results of the tabulation. So we are going to measure the “age” of the children working in mechanical workshop. Obviously “age” is an important explanatory variable of the children working in mechanical workshops. Although “age” is a quantitative factor, we may approximate it by a dummy variable as follows. We may divided the children in three age groups, each groups containing children with more or less similar condition and status (socio-eco) patterns.
Group-A = Children of 5 to 8 years of age.
Group-B = Children of 8 to 11 years of age.
Group-C = Children of 11 t o14 years of age.
On the assumption that children become more adult as they grow the dummy variable for “age” may be assigned as “for the 1st group – the value is zero, for the 2nd group, the value is 1 and for the 3rd group value is 2.
Now these three groups of children can be drawn in the following table for the purpose model specification.
| Age Group | Dummy variable for each groups | |
| 5 to 8 years of age | Zero (0) | Z1 |
| 8 to 11 years of age | 1 | Z2 |
| 11 to 14 year of age | 2 | Z3 |
Now we can plot a demand function for the purpose to know the increase in the child work over each group of age.
We have denote Z1(0) by b0, Z2 by b1 and Z3 by b2,
Thus the D = b0 +b1 +b2
We can write it, as
D = b0+b1X1+b2X2+UI
Where, 0, X1 and X2 are dummy variable for ages.
The following each diagram shows that demand for child work increase with the 2 to 3 year difference in each group of age. The each increase in each age of group indicated by the dummy variables i.e. A, B, C.
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