Differential Factors of Fertility in South India

Abstract

This paper has an attempt to analyze the fertility differentials in south India in terms of economic and social development. Regional variation of fertility comes from the multiplicity of economic, social and cultural factors. Among these factors, cultural factors have not been successful to make quantification. At the first stage, therefore, fur the analysis, the geographical areas which have homogeneous cultural conditions are selected. The subjected areas here are four southern states, Andra Pradesh, Kerala, Karnataka and Tamil Nadu which are based on the Dravidian kinship organization. First, we examined this region by the factor analysis, using 33 variables concerning the indicators of demographic structure of labour force, agricultural structure, education, medical care and religion. Five factors are extracted by this analysis. The first factor is labelled labour force characteristics, the second is level of social development, the third is family structure, the fourth is agricultural characteristics, and the fifth is urbanity. From these results the characteristics of this region are interpreted as that the industrial structure is of characteristic of labour intensive agriculture. We can also observe a higher rate of labour force participation of children and women in the agricultural field and most of them are less-educated. Secondly, we discuss the influences of such regional characteristics on the fertility differentials. Next analysis has been made by regression analysis, using variables which are extracted by the factor analysis. From the first factor a variable of child labour is selected. From the second, female literacy rate and population per doctor are selected. From the third, proportion of never married women is selected; from the fourth, population density per cultivated land ; from the fifth, the labour force participation rate in the primary industry and proportion of urban population. Taking the above indicators as independent variables, the regression equation for the child-woman ratio is estimated. In our model, 48.4% of the total variance in the child-woman ratio in the four states is explained. Among variables, child labour and population density per cultivated land are statistically significant. The estimated result shows that the necessity of labour force in agricultural sector makes the fertility rate higher. Thirdly, we estimate the regression equation including only variables those which were statistically significant in our model. They are child labour and population per cultivated land. Adding to these variables the level of education is included. Our model cannot explain the regional variances of child-woman ratio for Kerala and Tamil Nadu. In Andra Pradesh, the necessity of child labour in agricultural field makes fertility higher of the cause of high fertility. In Karnataka, educational level has a positive effect on the fertility decline.