**2. Previous study**

Golley and Toyer [1] suggested that China and India's demographic transitions timings and the implications of fertility developments were discovered using a global economic model and measures of dependency include the working overaged and working age. China's labor force to begin to diminish, whereas India will increase fertility rate faster than its present population. The population plays a significant role in defining the relative magnitudes of labor force growth to total population growth and the change in dependency ratios, with a significant impact on per capita income growth. India, the world's most populous country by 2030, and its population policy continue to be directed toward promoting fertility decline. The lower fertility reduces GDP and increases per capita income in both countries, India gains more per capita income than China per unit change in fertility, resulting in India's higher youth dependency [1].

Roy and Jones [2] developed a technique for the prediction of health indicators for all the districts of India and examine the correlations between health and development. The two fundamental indicators of this research are the levels of electrification and district domestic product (DDP). The data with health metrics and the information from two night time satellite images were used to propose the models. The predicted the health indicators with less than 7–10% errors were successfully. The health metrics, like crude birth rate and maternal mortality rate were mapped for the whole country at the area level. These metrics showed very strong correlation with development indicators. In a socio-economic study, using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery, the observation showed a higher DDP and level of electrification for better health conditions [2].

Maitra and Pal [3] emphasized that the estimates of birth spacing on child mortality are different when fertility selection are not considered. A comparison study of the fertility behavior of households in the Indian and the Pakistani Punjab highlighted the differential nature of institutions on demographic transition in these neighboring regions. The study involved reported birth interval and not inter-conception interval, which implies that there were some measurement errors associated with this particular variable. The miscarriages, stillbirths and also premature births were not measured for measurement. The study identifies the bivariate probit model that estimates mortality after correcting for the self-selection in fertility decisions [3].
