Modelling Fertility Schedules of India

Pandey, Ranjita; Kaur, Charanjit

doi:10.1007/s42650-019-00004-9

Cited by 1 publication

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to the previous studies 7 , 29 done on the modelling of Indian fertility schedule, our study paid more attention to model with the single-year ASFR of India and the six states. Our study is also distinct from the Pandey et al 28 , as it proposes four parametric models for single-year ASFR. Pandey et al 28 also fitted the data of single-year ASFR through polynomial functions.…”

Section: Discussionmentioning

confidence: 85%

“…Our study is also distinct from the Pandey et al 28 , as it proposes four parametric models for single-year ASFR. Pandey et al 28 also fitted the data of single-year ASFR through polynomial functions.…”

Section: Discussionmentioning

confidence: 85%

“…The other classes of fertility models are non-parametric. Polynomial model 28 – 30 , Spline model 31 – 35 and the other models 36 are non-parametric models of the age-specific fertility schedules. Although polynomial models are simple and easy to handle, they are not quite popular due to the non-interpretability of their parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In recent literature, there have been only a handful of studies 7 , 28 , 29 , that attempt to fit the age-specific fertility pattern in the Indian context. To the best of our knowledge, no study has ever tried to fit a single-year, age-specific Indian fertility pattern, through parametric models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experiments in modeling recent Indian fertility pattern

Srivastava

Singh

Pandey

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Modelling is a well-established concept for understanding the typical shape and pattern of age-specific fertility. The distribution of India’s age-specific fertility rate (ASFR) is unimodal and positively skewed and is distinct from the ASFR of the developed countries. The existing models (P-K model, Gompertz model, Skew-normal model and G-P model considered here) that were developed, based on the experiences of the developed countries, failed to fit the single-year age-specific fertility pattern for India as a whole and for the six selected states. Our study has proposed four flexible models, to capture the diverse age pattern of fertility, observed in the Indian states. The proposed models were compared in three ways; among themselves, with the original models and with the popular Hadwiger model. The parameters of these proposed models were estimated through the Non-Linear Least Squares Method. To find the model with best fit, we used the corrected version of Akaike’s Information Criterion (AICc). Optimization of the four original models was successfully done. When the model was fitted to the empirical data of the 4th round of the National Family Health Survey conducted in 2015–2016, the results of this study showed that all the four proposed models outperform their corresponding original models and the Hadwiger model. When comparison among the proposed models was done, the Modified Gompertz Model provided the best fit for India, Uttar Pradesh and Gujarat. Whereas, the Modified P-K model gave the best fit for West Bengal, Tripura and Karnataka. The Modified G-P model is the most suitable model for Punjab. Although our proposed models illustrated the fitting of ASFR for India as a whole and the selected six states only, it provides an important tool for the policymakers and the government authorities to project fertility rates and to understand the fertility transitions in India and various other states.

show abstract