CHAPTER 5 SOCIAL INCLUSION LEVEL

Transcription

1 CHAPTER 5 SOCIAL INCLUSION LEVEL Social Inclusion means involving everyone in the society, making sure all have equal opportunities in work or to take part in social activities. It means that no one should be excluded from any of the processes, or not discriminating against anyone for any reason, be it social, cultural or socio economic. 5.1 MEANING OF SOCIAL INCLUSION A socially inclusive society is defined as one where all people feel valued, their differences are respected, and their basic needs are met so they can live in dignity (Cappo 2002). The coin of social inclusion terminology has been so often used in public media in various contexts that it has become defaced to some extent. For instance, social inclusion of women in politics, means more representation of women in the Indian Legislature. Social inclusion of women in Self Help Groups denotes more participation of women in the self-employment activities of the groups to attain financial freedom. Social inclusion of the poor in the GDP indicates that the income growth should not be loft-sided only on the part of the already rich people and the poor segments of the society should also be benefited by getting a share in the cake out of the increase in the income stream. Hence the meaning of the terminology varies according to the context in which it is used. Social exclusion is the process of being shut out from the social, economic, political and cultural systems which contribute to the integration of a person into the community (Cappo 2002). Normally Social Inclusion is related with women, on the basis of castes or religions who are discriminated against by the society. 5.2 SOCIAL INCLUSION IN THE CONTEXT OF THE STUDY Social Inclusion, another subjective and nebulous term, can be perceived as that feeling which makes a person to feel one among the society. As the migrants have come from far off places they may feel inferior to the locals due to various factors like language barrier, socio cultural differences, socio economic factor and many others may be the reasons for feeling out of place in a new area. For eg. A poor person living among rich neighbours or a rich person living among slum dwellers may feel socially excluded. Not only socio economic factor, but also cultural and social differences may also reflect in low social inclusion level. In the olden days

2 people of certain castes were kept socially and culturally outside the society. All these issues may reflect vis-à-vis their perception of acceptance by the society. 5.3 DETERMINANTS OF SOCIAL INCLUSION LEVEL (SIL) The independent variable for the second case is the social inclusion. In the present context Social Inclusion in the main stream may be measured by the following 10 aspects Social Inclusion Level (SIL) for migrants: SI1- I feel a sense of acceptance in the community activities. SI2- I ve love & respect for the local people. SI3- I would like to settle down in the place of current residence in old age. SI4- If I ve to go back to my native state for some reason I would miss this place. SI5- I feel loyal towards matters of migrated state. SI6- I would allow my family members to marry a person from the local community. SI7- I am here not only for income sake,but because I like it here. SI8- I have lot of friends from the local community. SI9- go out a lot with my friends. SI10- I take part in the local community activities. These were also measured by a 5- point ranking scale from Totally Agree to Totally Disagree. 5.4 CONSTRUCTION OF SOCIAL INCLUSION INDEX (SIINDEX) Social Inclusion Index to determine Level of social acceptance of a migrant in the society which was also extracted by Principle component Analysis Method by using SPSS is used for the present research. The above ten variables were included in the factor analysis. The results are presented in Table 5.2. The number of factors extracted can be defined by the user, and there are techniques available in SPSS that can be used to help decide the number of factors. One of the most commonly used techniques is Kaiser s criterion, or the eigenvalue rule. Under this rule, only those factors with an eigen value (the variances extracted by the factors) of 1.0 or more are retained. Using this criterion, our data revealed 3 factors.

3 Com pone nt The results of PCA using varimax rotation are presented in Table 5.2 Three factors accounted for per cent of the total variance in the data which is given in Table 5.1. For the first factor, SI2, SI3, SI4, SI5 and SI7 showed markedly higher positive loadings. Loading resulting from an orthogonal rotation are correlation coefficients of each variable with the factor, so they naturally range from -1 to +1. A negative loading simply means that the results need to be interpreted in the opposite direction from the way it is worded. The first factor accounted for 30.9 per cent of the total variation. This factor is a reasonable representation of the sense of loyalty towards the migrated place. For the second factor, SI1, SI8, SI9 and SI10 showed strong positive loadings. The second factor accounted for per cent of the variance. We may interpret this factor to measure of participation in community activities. For the third factor SI6 showed strong positive loadings. The third factor accounted for per cent of the variance. As in the third factor has SI6 which was a question posed Will you allow your family members to marry a person from the local community?, we may interpret this factor as a measure of orthodoxy and traditional approach towards casteism/community. Total Initial Eigenvalues % of Cumulative % Table Total Explained Total Extraction Sums of Squared Loadings % of Rotation Sums of Squared Loadings Cumulative % Total % of Cumulative % Extraction Method: Principal Component Analysis ; Source: Primary Data

4 Results of PCA Varimax Rotation factor matrix Table 5.2 Rotated Component Matrix a Component SI SI SI SI SI SI SI SI SI SI Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 5 iterations. As a first step in the computation of a single SIINDEX, factor score coefficients, also called component scores were estimated using regression method. Factor scores are the scores of each migrant, on each Social Inclusion factor. To compute the factor scores for a given migrant for a given factor, the migrant s score on each variable is multiplied by the corresponding factor loading of the variable for the given factor, and these products are summed. This calculation was carried out using SPSS procedure and factor scores were saved as variables in subsequent calculations involving factor scores (Krishnan 2010). The three factors explained per cent of the total variation, with the first per cent, second per cent and the third factor Therefore, the importance of the factors in measuring overall sense of Social Inclusion is not the same. Using these percentages as weights on the factor score coefficients, a SIINDEX was developed for each migrant, using the formula: SIINDEX = (30.92) x (Factor 1 score) + (24.04) x (Factor 2 score) + (11.07) x (Factor 3 score)

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