Abstract
It is known that the measurement of growth rate is ideal with time series panel data. However, it is also possible to measure the growth rate with cross-section data, provided the data are grouped appropriately. Along with calculating the growth rate if one wants to find the factors associated with the growth rates then one needs to group it more prudently. This paper illustrates how we can do so using data of primary school going children of age group 6–10 years. The data has been taken from students up to class four, from schools in Kolkata. We have taken Medium of instruction, Type of school, Sex of children, Household size and Per-capita expenditures as grouping criteria. Altogether we should have got 2\(^{5}\), i.e., 32 combinations. But in our case we have only 24 combinations, because the schools with the remaining 8 combinations are not found in Kolkata. Thus, though, we have a large number of students as sampled, we have essentially only 24 observations. We could have taken some more variables to increase the number of observations, but in that case the number of students in each combination (group) would have been very small and the mean values would not have been stable. Growth rates of height, weight, Mid-Upper Arm Circumference(MUAC) and body fat have been calculated. Childhood period is the period when there is maximum growth. Our data also shows the same for both boys and girls and for students when boys and girls are taken together. However, we do not get much association of the growth rate with medium of instruction, type of school, household size and percapita expenditure.
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Notes
- 1.
If x\(_{t+1}\) = x\(_{t}\)(1+\(\upalpha )\), then \(\upalpha \) is called the growth rate. It may also be expressed as percentage taking (100\({\times }{\upalpha }\)). It is clear that \(\upalpha \) is nothing but (\(\mathrm{x}_{t+1} - \mathrm{x}_{t})/x_{t}\).
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Bharati, S., Pal, M., Srivastava, M., Bharati, P. (2017). Growth Rate of Primary School Children in Kolkata, India. In: Dasgupta, R. (eds) Growth Curve Models and Applications. GCM 2016. Springer Proceedings in Mathematics & Statistics, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-319-63886-7_6
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