The overall objective of this research is to investigate whether using year of death data to produce estimates of time location is a better approach than the method developed by Brass and Bamgboye (1981) and whether estimates of mortality produced using year of death data are any better than those derived using the conventional orphanhood method.

The vital registration system in Zimbabwe is incomplete and mortality estimates produced from these data might not give a true representation of mortality in the population. However, it may be assumed that vital registration data for urban areas is more complete than for the country as a whole. This research was, therefore, conducted in an attempt to answer the question of whether vital registration data can be used to track the mortality of Zimbabwe's metropolitan populations. To answer this question, direct and indirect estimates from census and Demographic and Health Survey (DHS) data were used to decide on the viability of using these vital registration data to estimate mortality. Estimates of under-five mortality between 2001 and 2011 from vital registration data ranged from around 50 to 80 deaths per thousand for Harare while Bulawayo's estimates were generally between 55 and 105 deaths per thousand in the same period. Bulawayo's vital registration data appeared to produce reasonable estimates of under-five mortality, while Harare's vital registration data underestimated both infant and under-five mortality when compared to the other supporting estimates from the alternative data sources.

Zimbabwean-born migrants form the largest proportion of the foreign-born population in South Africa; however, relatively little is known about both the international and internal migration behaviour of this population group. This study reviews several migration theories, and existing evidence on Zimbabwean-born migration to South Africa and uses the 2011 Census and 2016 Community Survey data for South Africa to explore both provincial and metropolitan migration patterns of Zimbabwean migrants. The study also seeks to identify characteristics of migrant internal movers and how these are associated with their propensity to migrate. To accomplish this, two modelling approaches are used, namely: the multiplicative component model (TODA) and the logistic regression model. The first modelling approach describes the origin-destination specific migration patterns of this group of migrants. The results from this analytic model show that Zimbabwean migrants are concentrated in three provinces, namely, Gauteng, Western Cape, and Limpopo; particularly in respective metropolitan areas. Also, post-arrival migration is largely focused on these major receiving provinces. The findings suggest that this pattern of concentration is likely to continue. The second modelling approach examines the inter-provincial migration of Zimbabwean migrants. At the aggregate level, the study results show a degree of selectivity of Zimbabwean immigration by factors such as age, sex, marital status, and education. Also, the results show that post-immigration migration is not necessarily significant to migrants’ redistribution. However, for those who move, the logistic regression analysis reveals that province of residence, age, marital status, and sex significantly impact on migrants’ propensity to move from “pioneer settlements” and their effects are consistent over time. Finally, despite concerns about the quality of the data collected in the 2011 census and 2016 Community Survey, our results show that there is general consistency of migration 'patterns’ between the two datasets.

This research derives a set of full life tables for South Africa as a whole and by population group using vital registration data for the period 2006-2008. Given that not all deaths are registered, the research assesses the level of completeness of death registration for the national population and for all the population groups separately by using the deaths distribution methods.

Includes abstract. / Includes bibliographical references. / The main objective of this research is to analyse critically the methodology used by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) to estimate maternal mortality in countries with high HIV/AIDS prevalence. This study interrogates each of the assumptions (implicit and explicit) in the MMEIG method by reviewing literature/studies that investigated each assumption.

Includes bibliographical references / The production of mid-year population estimates is an important undertaking which informs various stakeholders in policy formation and decision making. For instance, national governments use mid-year estimates to allocate seats in parliament to various constituents and public health sectors use them to monitor and improve service delivery. Mid-year population estimates undoubtedly serve very important purposes that affect lives of many people. As such, national statistical offices in various countries are given the mandate to produce annual mid-year population estimates. Statistics South Africa (Stats SA) assumes the function of producing and publishing official mid-year estimates of the population in South Africa. Stats SA produces its mid-year estimates using DemProj, population projection software which is part of the SPECTRUM suite of policy models developed by the Futures Institute. However, Stats SA does not publish full details of its adaptation of DemProj when producing its mid-year estimates as it regards this as proprietary. Concerns have been raised about the accuracy of the official mid-year estimates in terms of age distribution, particularly for ages below 40 last birthday in 2011 (e.g. Dorrington 2013). As such, this research critically analyses the method used by Stats SA to produce mid-year estimates and assesses the feasibility of using administrative data to produce mid-year estimates for South Africa. The base population is adapted from the 2001 census population. Birth and death registration data are used in a cohort component approach to produce alternative mid-year estimates for South Africa for the years 2002-2011. Prior to using these data, they are adjusted for incompleteness of registration. Levels of completeness of birth and death registration are estimated by extrapolating earlier estimates of completeness from various researchers. The mid-year estimates obtained are compared with those published by Stats SA in order to assess the relative quality of the two series of mid-year estimates. The mid-year estimates for 2011 are also compared with the mid-year population estimated from the 2011 census. These comparisons help identify the mismatches to the census and their possible causes and as such, these may lead to improved population estimates in the future, and a viable alternative method to that currently being used by Stats SA.

Includes abstract. / Includes bibliographical references. / The objective of this study is to assess how well the projected parity progression ratio method works when applied to two successive censuses or Demographic Health Surveys. Four countries, namely Malawi, Zimbabwe, Cambodia and Panama, each with two recent censuses which are ten years apart, are used. Each of the census and survey used is taken through a data quality assessment process to check for inconsistencies. Using age-order specific fertility rates derived from births in the past year, parity progression ratios are projected to the next census.

Includes abstract. / Includes bibliographical references (leaves 122-124). / The relational Gompertz model is often used to obtain fertility estimates for sub-Saharan Africa populations. This indirect estimation technique is dependent on a fertility standard - the Booth standard. This standard was developed in 1979 using a selection of 33 Coale-Trussell schedules congruent with high fertility patterns. However, evidence from 61 Demographic and Health Surveys of sub-Saharan countries shows that fertility has decreased to levels that were considered medium fertility at the time the standard was developed. This raises concerns about the continued relevance of the (high fertility) Booth standard. In particular, the standard would appear to consistently underestimate fertility among African women aged 45-49.

Includes abstract. / Includes bibliographical references. / Mortality of children under the age of five has been the main target of public health policies (Gakusi and Garenne 2006). There has been a significant decline in under-five mortality in the twentieth century in almost all countries regardless of initial levels and socio-economic factors, although the rate of decline has been different in different regions (UNIGME 2012). Malawi, a country in the sub-Saharan region, is characterised by high infant and child mortality. Using data from 2010 Malawi Demographic and Health Survey, infant mortality in Malawi was estimated at 66 deaths per 1000 births while child mortality was at 50 deaths per 1000 births (NSO and ORC Macro 2011). Studies have been conducted to identify covariates of infant and child mortality in Malawi but none of these used recent data and none has included HIV/AIDS as a risk factor (Baker 1999; Bolstad and Manda 2001; Kalipeni 1992; Manda 1999). This study aims at examining bio-demographic, socio-economic and environmental factors associated with infant and child mortality in Malawi. Malawi Demographic and Health Survey (DHS) data for 2004 and 2010 are used.

The study utilises four Demographic and Health Surveys (DHSS) conducted in 1988, 1994, 1999, and 2005 in Zimbabwe to examine the socioeconomic differentials in fertility over time. The period fertility (age-specific and total fertility) rates, cohort-period fertility rates (CPFRs), projected parity progression ratios (projected PPRs), and logistic regression methods are used in the analysis, overall, to assess the nature of fertility transition. All the measures of fertility indicate an ongoing fertility decline in Zimbabwe among all parities and age groups mainly as a result of an increase in modern contraceptive uptake. Overall, fertility decline has been much more rapidly in the in the 1980 decade and slowed since 1990. The analysis shows an inverse association between urban residence, education and economic status, measured by ownership of household assets, and fertility based on the total fertility (TFR), CPFRs and projected PPRs estimates. Further analysis of the net effects of economic status and education using multivariate logistic regressions suggests the odds of having a child (not having a child) decreases (increases) with economic status and education. Overall, even after controlling for various socioeconomic variables fertility decreases with a rising level in education and/or economic status.