Fertility change in India

Methodology

The model includes four sections on the systems: state, pressure, impact and response/steering. The state system encompasses the total fertility rate and its proximate determinants distinguished by Bongaarts and Potter: marriage, contraceptive use, induced abortion and postpartum infecundability. The pressure system describes the driving forces behind fertility changes, comprising the following variables: life expectancy, female education, gross national product and (current) population growth. The impact system describes the number of births, which together with the number of deaths from the health module, generates estimated population figures. The response/steering system includes: abortion policy, education policy, family planning allocation and mass communication.

Results

The scenarios applies a number of possible fertility trajectories within the fertility model, showing that

• Further fertility decline without explicit population policies is possible because of the assumed continuation of the diffusion process and changing son-preference: it takes longer. The decline is a consequence of changes at the micro-level: changes in reproductive attitudes in relation to son-preference and the demand for family planning methods.
• For the scenarios assuming less economic development, explicit policies are possible for increasing the possibilities of child spacing. There is a reduced use-effectiveness of child-spacing methods which can only partially be compensated by increased information and health education.
• Fertility decline is set in motion and is even accelerating in some areas. Some scenarios show that replacement levels can be reached within two decades. Under less favourable circumstances the desired number of children remains above replacement level.

Conclusions

The relation between the fertility levels and the proximate fertility determinants is relatively well-understood. Human development and family planning programmes may affect the behaviour of people only indirectly and with a time lag. The model illustrates how the effectiveness of population policies is influenced by variables at levels between the policies and individual decision-making.