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An assessment of retrospective birth history reporting for the measurement of fertility in South Africa.

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Fertility is one of the major tenets of demography. Its importance lies in the determination of fertility trends in a country, in a specific time period. These statistical inferences of fertility play an imperative role in population policy formation and planning. Thus the importance of the measurement of fertility remains undisputed. Due to the significance of fertility, its measurement and its profound impact on societies, acknowledging and addressing the quality of fertility data is of great importance. This research study was conceived in response to the above concern. This study aims at addressing and providing insight into birth history data irregularities and determining interventions of working with this issue in the context of South Africa. Through secondary analysis (i.e. descriptive exploratory and comparative analysis) the study sought to firstly establish a demographic profile of women associated with inconsistent and inaccurate reporting of their birth histories. Secondly the research attempted to ascertain a relationship between the socio-economic statuses of individuals and retrospective reporting. A third objective was to note the sex-selectiveness of reporting (i.e. were more girls or boys reported or misreported on in the retrospective birth histories). The study has established that older, married women with some educational attainment, of rural areas from either the middle and lower income categories tend to misreport more frequently than their converse counterparts. Furthermore, a plausible relationship between the socio-economic statuses of individuals was observed. In terms of the sex-selectiveness of reporting, in general, boys were reported on more consistently than girls. However in certain cases, it was found that rural and middle income women reported accurately on girl children born alive and dead girl children. Recommendations made with respect to improve the quality of fertility data for include the proper training of enumerators and data capturers, quality control during data collection, testing of questionnaires, dealing with social, cultural and language barriers and the reinforcement of publicity campaigns for censuses and surveys.


Thesis (M.A.)-University of KwaZulu-Natal, Durban, 2010.


Fertility, Human--South Africa--Statistics., Childbirth--South Africa--Statistics., Theses--Population studies.