Sampling migrants from their social networks: The demography and social organization of Chinese migrants in Dar es Salaam, Tanzania

Abstract

The streams of Chinese migration to Africa are growing in tandem with rising Chinese investments and trade flows in and to the African continent. In spite of the high profile of this phenomenon in the media, there are few rich and broad descriptions of Chinese communities in Africa. Reasons for this include the rarity of official statistics on foreign-born populations in African censuses, the absence of predefined sampling frames required to draw representative samples with conventional survey methods and difficulties to reach certain segments of this population. Here, we use a novel network-based approach, Network Sampling with Memory, which overcomes the challenges of sampling ‘hidden’ populations in the absence of a sampling frame, to recruit a sample of recent Chinese immigrants in Dar es Salaam, Tanzania and collect information on the demographic characteristics, migration histories and social ties of members of this sample. These data reveal a heterogeneous Chinese community composed of “state-led” migrants who come to Africa to work on projects undertaken by large Chinese state-owned enterprises and “independent” migrants who come on their own accord to engage in various types of business ventures. They offer a rich description of the demographic profile and social organization of this community, highlight key differences between the two categories of migrants and map the structure of the social ties linking them. We highlight needs for future research on inter-group differences in individual motivations for migration, economic activities, migration outcomes, expectations about future residence in Africa, social integration and relations with local communities.

Communication flows and the durability of a transnational social field

“…We collected the NSIT data in three stages using a “link-tracing” sampling approach starting in the destination. Overall, the success of the NSIT in collecting network data from a hard to reach migrant population led to additional work on improving the precision and accuracy of network-based sampling methods (Mouw and Verdery 2012; Merli et al 2016). The first phase of the survey began in North Carolina in spring 2010 by interviewing 10 original “seeds” from the origin city in Guanajuato who were selected from contacts obtained during prior ethnographic fieldwork within this community.…”

Social Networks and Transnational Social Fields: A Review of Quantitative and Mixed-Methods Approaches

“…This approach is ideally suited for constructing partial representations of the whole TSF and is achieved by linking respondents together, as described below. The link-traced sample (Mouw and Verdery 2012; Merli et al 2016), drawn within a small, well-connected, and well-researched migrant community, guarantees that respondents ultimately form part of a single larger network.…”

Data Collection for Migrant Live-In Domestic Workers: A Three-Stage Cluster Sampling Method

“…It is because we usually do not have sufficient knowledge of an adequate sampling frame and it is difficult to reach them by visiting their workplace or place of residence. Studying migrant workers poses similar problems to researchers relying on routine survey methods because migrant workers represent a hidden population without a sampling frame (Agadjanian & Zotova, 2012; Merli et al, 2016). Due to the lack of a proper sampling frame, the usual standard procedures for drawing samples from a population would fail.…”

“…Thus, alternative methods for sampling and surveying migrant workers have been proposed. These include an origin-based snowballing method conducted in their home countries (Beauchemin & Gonzalez-Ferrer, 2011), a workplace-based stratified probability sampling design conducted in their host countries (Agadjanian & Zotova, 2012), sampling from migrant workers’ social networks (Merli et al, 2016), or using web-based social media advertisements (Potzschke & Braun, 2017).…”