MSc

The study entitled “Statistical evaluation of socio economic development in Kerala”
was conducted to construct a composite index of socio economic development for each district
of Kerala, to classify the districts into homogeneous groups based on the development and to
identify major factors affecting socio economic development across districts of Kerala. Data
pertaining to the development of various sectors such as agriculture, demography, industry and
infrastructure were collected from various departments for a period of five years, 2015-16 to
2019-20 for the purpose of the study.
The average value of each indicator for the five year period was standardised and the
weightage was assigned for each indicator based on coefficient of variation to obtain the overall
composite indices of fourteen districts, and these districts were ranked based on the composite
indices. The district with a low composite index value was ranked first and the district with a
higher composite index value received a lower rank. Palakkad district (composite index of
0.34) was ranked first and Pathanamthitta (composite index of 0.63) was the last.
In order to identify the homogenous groups, the districts were classified into low,
medium and highly developed districts. The districts were categorised into three different
groups using mean ± 0.5 standard deviation value of the composite index, as the indices
followed a normal distribution pattern. The districts Pathanamthitta, Wayanad and Kasaragod
were low developed districts, whereas Palakkad, Thrissur and Ernakulam were highly
developed districts, and rest of the districts such as Thiruvananthapuram, Kollam, Alappuzha,
Kottayam, Idukki, Malappuram, Kozhikode, and Kannur were classified as medium developed
districts.
The significant variables that contribute to the socio economic development of the
districts were identified using principal component regression method. Principal component
analysis was performed and first five principal components were selected, as this explained
around 70 percentage variation of the total indicators. The multiple linear regression analysis
was performed using principal components as explanatory variables and the composite indices
as the dependent variable, the first three principal components were found to be significant and
the major contributing variables from these significant components were identified based on
the factor loadings obtained from the principal components. The variables such as number of
hospitals, number of banks, number of higher secondary schools, number of government
colleges, number of SHGs, per capita income, area under paddy and area under irrigation were
found to be the major contributing variables.
A correlation analysis was performed to study the inter-relationship between the various
sectors. Each sector were found to be significantly and positively correlated with socio
economic development. It was also observed that agriculture sector had the highest correlation
with socio economic development.
Model districts and the potential target variables were identified for each low developed
districts. The variables such as net sown area, birth rate, death rate, infant mortality rate, per
capita income, number of registered factories and number of banks were identified as potential
target variables. The study suggests that by bringing the values of the observed variables in the
low developed districts closer to that of the target variables in the model districts, would lead
to the improvement in socio economic development of the low developed districts.