Harmonising international migration flow statistics in Africa

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There are 54 countries in Africa today (excluding the dependencies or other territories), which amounts to 1.26 billion people (US Census Bureau 2019). International migration plays a key role in population change and population geographical redistribution. According to Gallup's finding (2017), the percentage of people in Africa who desire to migrate is increasing. There are 13 African countries which potential migrants are above 30% of their population between 2013 and 2016. And 3 of them are even higher than 50%, while the percentage of potential migrants in Sierra Leone is up to 62%. Despite recent image of migration in Africa being directed mainly towards Europe and fuelled by conflict and climate change, it is a continent of predominantly intra-continental migration, as well as towards Gulf states and Americas (Flahaux and De Haas 2016). According to the Global Bilateral Migration Database, intra-continental migrant stocks amounted to 10.5 million people in 2000, while stocks of African-born migrants living outside Africa were 8.7 million. Also, despite a common belief, refugees and people in a 'refugee-like situations' represented 2.4 million or 14% of international migrants in Africa (UNHCR 2011).According to a worldwide Gallup survey, there are 14% of the world's adults (aged over 15) said that they would like to leave their own country if they could. However, only 3% of them who have migration intentions actually started to make preparations for their leaves (Esipova et al. 2011). Although there are a considerable proportion people desire to emigrate, there is only few persons made it because the decision for an individual to emigrate is complex and depends on their own situations, such as, if people have enough resource to support their emigration behaviour. Such as a multistage process cannot be reflected in the macro level, which severely limits the understanding of migration and leads to the inaccurate of aggregate migration statistics. So far, the literature on estimating African migration flows are based on macro models (Abel and Sander 2014). However, the missing values severely limits the accuracy of estimation values. And the existing migration data is usually inaccurate due to the inaccurate measurement. The accuracy can be improved if uncertainty is reduced. In this thesis, the microsimulation approach will be utilised to estimate bilateral international migration flows as well as the missing flows within African countries and reduce the uncertainty. The contribution of this thesis is to impute missing bilateral international migration flows and improve the measurement accuracy of migration data by reducing the uncertainty by microsimulation approach. To predict the international migration, measuring and modelling the uncertainty is necessary. To take this uncertainty into consideration, the leading method is the Bayesian inference that counts for the multiple uncertainty coherently (Raymer et.al 2013, Wisniowski 2017). Measure uncertainty is necessary, but reduce the uncertainty is more important. As indicated in Willekens (2018), the uncertainties should be reduced by taking the heterogeneity of migrants in consideration, (i.e. individuals' characteristics such as age, gender, education level, etc) and understand why these people decide to leave or stay in their own country. Thus, there is a need to study migration behaviour in a micro level. The model should be an individual-based model, the agents (individuals) should have different life courses due to the difference of their characteristics.