2019

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Browsing 2019 by Author "AZKA SHAMSHAD"

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    Exploring the Determinants of Education, Wealth Index and Dwelling Status of Household Heads in Punjab Multiple Indicators Cluster Survey (MICS)
    (UMT.Lahore, 2019) AZKA SHAMSHAD
    In our study, we explore various factors influencing the Education, Wealth Index and Dwelling Status of Household (HH) Heads in Punjab Multiple Indicators Cluster Survey (MICS) 2017-18. Two Ordinal Logistic Regression models and one Multinomial Logistic regression model are proposed to find the significant effect of independent variables on each of the response variables individually. It is concluded that as the education level of HH that belongs to rural area is significantly higher than the head of the household that belongs to the urban area. The education level of male HH head is significantly higher than the head of the female HH head. The education level of HH head who speaks Urdu is significantly higher than that of HH head who speaks other language; the education level of HH head who speaks Saraiki is also significantly higher than that of HH head who speaks other language. The education level of HH head that avail Inter-connected electricity facilities are significant than those of lower level of education of HH head and with off grid electricity facilities times more significant than those of lower level of education of HH head. The education levels of HH head that having internet facilities are significantly higher than those of without internet facilities. The education level of HH head that are having their own house are less significant than that of HH head who have other dwelling status and the education level of HH head that are having rented house are also less significant than that of HH head who have other dwelling status. The education level of HH head that belongs to the poor status is significantly lower than that of HH head that belongs to the richest category, the education level of HH head that belongs to the secondary level is significantly lower than that of HH head that belongs to the richest category, the education level of HH head that belongs to the middle level is significantly lower than that of HH head that belongs to the richest category and the education level of HH head having fourth level is significantly lower than that of HH head that belongs to the richest category. It is concluded that for 2nd model, the response variable Wealth Index Quintile conclude that number of HH members, Education level of HH, Area of HH, Sex of HH, Electricity facility, Internet access at home and the dwelling status of HH are significantly higher than their reference categories. In the variable Language of HH head, all categories except English Language of HH are significant. In Multinomial Logistic Regression model, the response variable has been divided into three categories. First half of the table describe the results of the Own category of Dwelling status while the 2nd half of the table describe the results of the Rent category of Dwelling status and the 3rd category other is taken as reference category. First half of the table displays that all of the independent variable i.e. number of HH members, Area of HH, Sex of HH, HH have electricity, Wealth Index Quintile showing significant results while the preschool and secondary level of Education of HH head are significant and in the variable Language of HH, only Saraiki is showing the significant result. Whereas 2nd portion shows that all the predictors’ i.e. number of HH members, Area of HH, Sex of HH, HH have electricity, Wealth Index Quintile showing significant results while the Language of HH, the category English is not showing the significant results. In Electricity, the category off-grid is showing significant result. Internet access at home is also not significant. Estimating response probabilities are also calculated to check the occurrence of dependent variable in specific category.