Internal migration and urbanization: special applications

In this paper we analyze migration in one well-known long-running surveillance program, the Agincourt HDSS in South Africa. Migration is a key demographic event for populations monitored with surveillance systems, yet we argue that it is underappreciated in current HDSS practice. We provide (1) analysis of secondary data predicting migration, and hence LTFU; and (2) results from a randomized study of migrant contact and follow-up, testing the efficacy of in-person versus face-to-face interviews in the South African setting. Our results are designed to help researchers and administrators connected to HDSS understand migration dynamics and its potential impact on population and health studies.

Despite being the wealthiest region of Belgium, the Brussels-Capital Region (BCR) is marked by widening social inequalities, resulting in a distinct pattern of socioeconomic segregation. This spatial division is reinforced through both selective migration and the process of class reproduction. It has been illustrated thoroughly that both processes are strongly determined by one’s educational attainment. This study reveals how educational attainment and the subsequent (lack off) opportunities for social upward mobility have an effect on people’s residential mobility and thus on the composition of the neighbourhood population. Analyses are drawn on the exhaustive and anonymous data from the Belgian censuses of 1991 and 2001, individually linked to the National Population Register (situation in 2006) to observe internal migration. First, binary logistic regression illustrates how social mobility is significantly related to the probability of moving: the odds are higher for socially immobile or downward mobile individuals than for the ones who climb up the social ladder. Second, the effect of social mobility on the direction of the observed relocation is elucidated through multinomial logistic regression.

Around two and half million people were attending an institute of higher education in the United Kingdom in the 2010/2011 academic year, which equates to around 4.1% of the total population. Surprisingly, given the importance of higher education very little work has been conducted on the migratory patterns of students attending institutes of higher education in the UK. This paper puts forward a typology that can be used to categorise the different migration transitions that a person can undertake in order to attend a higher educational institution. With the use of the student migration typology and the Student Record Dataset of the Higher Educational Statistics Agency, which contains detailed information on every student recorded as attending an institute of higher education in the UK, illustrations of the migratory patterns of those attending higher education in the UK during the 2010/11 academic year with a focus on the local authority (LA) and county level geography are provided. Using the typology of student migration the data indicate that around 37% of students are student migrants, 9% were local students, while 35% stayed in the LA but studied elsewhere suggesting they distance learn or commute. When analysing LAs; Oxford, Cambridge, Nottingham, Leeds and Sheffield all stood out as areas with large student populations.

It is not clear how economic crises or conflict influence the process of internal migration and urbanization. This paper uses data from the MAFE-Congo dataset to investigate internal migration to Kinshasa, the capital of the Democratic Republic of Congo (DRC), by examining the migration histories of 389 lifetime migrants to the city. The analysis considers whether most migration was rural-to-urban and if recent decades of civil conflict and economic crises in the DRC coincided with higher levels of in-migration to Kinshasa. Although the data shows an increase in the number of moves during heightened periods of conflict towards the end of the 1990s, this is not found to be an overall trend when cohort and age patterns of migration are accounted for. Additionally, the overwhelming majority of migrants to Kinshasa were born in other cities, not in rural areas as might be expected given DRC’s high rates of rural-to-urban migration, and most migrants were born in the provinces adjacent to Kinshasa. This implies that patterns of migration to Kinshasa reflect geographic proximity and urban-to-urban migration patterns more than direct efforts to flee the impacts of conflict, which in turn suggests that the end of the conflict in the DRC may not lead to decreases in migration to Kinshasa.