The United Nations Population Division publishes fertility projections for all countries in the World Population Prospects (WPP). These are the most widely used projections for planning and policy implementation. Despite a substantial body of literature that suggests education has a significant impact on fertility, these projections do not incorporate changes in the composition of the population by level of education. We therefore propose and implement a method that incorporates education composition change in projecting fertility. We investigate fertility differentials by level of education, then evaluate how education influences fertility independently; and finally, a model is fitted to project fertility rates by education levels. In both cases, the fertility rates by education level are then weighted by the IIASA educational attainment distributions to get the national fertility rates. These national fertility rates are in turn validated against the WPP fertility rates to evaluate how good the proposed method works. Fertility is high among the less educated relative to educated women. Education proves to be an important driver of fertility decline in Southern Africa. The proposed model is a good fit for countries with sufficient DHS data. However, there are other sources of data that are available, for example, the census data but we could not rely on them since they only give summary information. Validation was done to evaluate how good the model is working. This exercise produced consistent results with the observed fertility estimates. The percentage difference between the projected and WPP fertility estimates varied from 1 to 5 percent in Lesotho, Namibia and Zimbabwe. In conclusion, the model can also be used for other countries. Furthermore, education composition change should be considered when projecting fertility since it has proven to be a significant driver of fertility change. Data quality and availability issues were a major limitation to our study and in future should be improved.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/28438 |
| Date | 16 August 2018 |
| Creators | Ncube, Presley |
| Contributors | Moultrie, Thomas |
| Publisher | University of Cape Town, Faculty of Commerce, Centre for Actuarial Research (CARE) |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MPhil |
| Format | application/pdf |
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