Although various models regarding corner solution data have been suggested, it is known that corner solution data is too complicated to predict the model. A corner solution is a unique solution in the sense that it is characterized by a mass-point at zero and a long right tail. For the present study, the data was taken from MICS collected by Punjab Bureau of Statistics (BOS) during 2011-2012. However, this study is based on the particular data. The child mortality within five-year interval was taken as a response variable. In the data obtained, 65% of the response variable observed to be zero. Tobit model reveals that it is the most accurate and widely used technique in this regard. The implementation of Tobit model shows that the significant predictor of child mortality in a family within five year are women age, postnatal checkups, antenatal care, total children ever born, and wealth index score. Since the majority (i.e. 74.4%) of mother age in the data is between 20 to 35 years. The results indicate that the chances of child survival are relatively higher for the mothers of age group of 20-35 years. Furthermore, Child mortality is highest for the families with low wealth index score. In addition, child mortality percentage tends to decrease with the improvement in postnatal and antenatal care facilities. The specification of Tobit model also used to verify the result of the Tobit model.
Amemiya, T. (1998). Advance Econometric(2nd Ed.). United states of America: Library of
congress publication data.
Austin, C. P., and Escobar, M. (2000). The use of the tobit model for analysing measures of health statues. Quality of life insurance , 5(1) ,901-910.
Dobson, A. J. (2002). An Introduction to Generalized Linear Models (2nd Ed.). Washington:
Chapman & Hall.
Fair, R. C. (1978). A Theory of Extramarital Affairs. Journal of Political Economy 86(1), 45-
61.
Greene, W. H. (2003). Econometric Analysis (5th Ed.). New York University: Prentice hall.
Goldberg, S. A. (1964). Econometric Theory (1st Ed.). John Wiley & Sons.
Kutner, H. M., Nachtsheim, J.C., and Neter, J. (2004). Applied Linear Regression Models(4th
Ed.). Boston: Mc Graw Hill.
Kaldewei, C., and Pitterle, I. (2011). Behavioural Factors as Emerging Main Determinants of Child Mortality in Middle-Income Countries: A Case Study of Jordan. Working Paper.
Madise, N.J (2003): Infant mortality in Zambia: Socioeconomic and demographic correlates.
Social Biology.
Montogomery, C. D., and Peck, A. E. (2001). Introduction to linear Regression analysis. New
york: John Wiley $ Sons, Inc.
Mondal, N. I., and Ali, K. M. (2009). Factors Influencing Infant and Child Mortality: A Case Study of Rajshahi District, Bangladesh. J Hum Ecol, 26(1), 31-39.
Mustafa, E. H., and Odimegwu. (2008). Socioeconomic Determinants of Infant Mortality in Kenya: Analysis of Kenya DHS. Humanities and Social sciences, 2(2).
Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica,
26, 24-36.
Wooldridge, J. M., and Schmidt, P. (1999). Efficient estimation of Panal data Models with
strictly Exogenous Explanatory variables. Journal of Econometric, 93, 177-20.
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