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This article examines the influence of maternal status, socioeconomic status of the household, and household composition on the mobility of children aged 0-14 in Mpumalanga Province, South Africa, from 1999 to 2008. Using data from the Agincourt Health and Demographic Surveillance System, we found that children whose mothers were temporary migrants, living elsewhere, or dead had higher odds of moving than children whose mothers were coresident. Older children and children living in richer households faced lower odds of mobility. For children whose mothers were coresident, there was no effect of maternal substitutes on child mobility. However, among children whose mothers were temporary migrants or living elsewhere, the presence of prime-aged and elderly females lowered the odds of mobility. For maternal orphans, the presence of elderly women in the household lowered their odds of mobility. The results underscore the importance of examining the conditions under which children move in order to strengthen service delivery targeted at safeguarding children’s well-being. © 2012 Population Association of America.
Under apartheid, men and sometimes women were separated from their children for extended periods of time because labor migration necessitated foster care as a coping strategy (Murray 1981; Spiegel 1987). Children were moved between households as a means of coping with economic hardship (Jones 1993; Van der Waal 1996). This practice continues even today as men and increasingly women move away from rural households in search of work. The subdistrict of Agincourt, the site for the present analysis, is an area that was and continues to be a “sending” area for labor migrants. Located 500 km northeast of Johannesburg in Mpumalanga Province, this semirural area was part of a former homeland under apartheid. High population density and low rainfall make the area inadequate for subsistence farming and more suitable for cattle rearing. Although all villages have primary schools and attendance is near universal, school progress lags, with half of 20-year-olds still enrolled. Employment opportunities are scarce, made evident by unemployment rates of 29% for men and 46% for women (Collinson 2009). The province has an HIV-prevalence rate (based on antenatal survey data) of 32.1%, making it one of the worst-affected areas in the country (South Africa Department of Health 2007). This is an ideal setting to examine child mobility because (1) labor migration has always been high and increasingly involves women, (2) household SES varies, and (3) mortality from HIV has been increasing over time. The data for this analysis come from the Agincourt and Health Demographic Surveillance System (AHDSS) conducted in 25 villages covering 400 km2. The baseline census was conducted in 1992, followed by annual visits to each household in the site to update births, deaths, migration, and the individual status (e.g., residence, union status, relationship to household head, and education) of every household member. Household SES is based on ownership of assets (e.g., cattle, car, and cell phone), as well as access to amenities such as drinking water and sanitation. As in other HDSS sites, verbal autopsies are conducted to determine cause of death. Additional modules on labor force participation, social grants1 uptake, and temporary migration have been conducted at periodic intervals (Kahn et al. 2007). Migration, in the AHDSS, is classified into two categories. A permanent migrant is defined as a person moving into or out of a household with an intention to make this move permanent. Someone who leaves the household permanently after a census update will not appear on the subsequent household roster. A temporary migrant, on the other hand, is someone who is identified as a member of the household but has spent six or more months of the previous year residing elsewhere because of employment or some other reason. In this sense, the AHDSS employs a de jure definition of household. This distinction is important in assessing the strength of ties between migrants and their households. Temporary labor migrants are more likely to send remittances and to visit more frequently than permanent migrants (Collinson 2009). The migration definition requires crossing the field-site boundary, which acts as a proxy for distance and implies that the moves within a village and between villages are not considered migration. Four of the 25 villages are situated near the border of the field site, so a move from one of these villages to an adjacent village that lies just outside the border is defined as an out-migration. Therefore, out-migration from these villages may be slightly overestimated because of their geographical proximity. Considerable effort has gone into the collection of high-quality data on migration, including the training of fieldworkers, the cross-checking of data, and ongoing efforts at reconciling migration from one household into another in the site in order to minimize double counting of household members. In 2008, the total surveillance population was 81,147 living in 14,119 households. Despite a notable fertility decline from a TFR of 6.0 in 1979 to 2.3 in 2004, the population is relatively young, with 36% of the population under the age of 15 (Garenne et al. 2007). There has been an increase in mortality partly attributable to HIV/AIDS, which constituted 8% of all deaths for the 15–49 age group in the 1992–1994 period but jumped to 48% in the 2002–2005 period. The site has seen a change in the mortality profile from the early 1990s, when acute diarrhea and respiratory diseases dominated for children, road accidents dominated for adults, and noncommunicable diseases dominated for older adults; by contrast, in the 2002–2005 period, HIV/TB dominated in nearly all age groups, followed by assault and road accidents for adults and noncommunicable diseases for older adults (Tollman et al. 2008). Previous work on migration using the AHDSS data has found that population mobility in Agincourt has increased over time, particularly among young children and adult women. Each year, around 20% of children make a permanent or temporary move; mobility among children 0–4 increased between 1995–1999 and 2000–2004. Children accompanied only 4% of temporary migrant fathers and 10% of temporary migrant mothers when they moved. Most of the children who stay behind remain in the same household, usually under the care of the mother if the father is the temporary migrant or under the care of grandmothers if the mother is a temporary migrant (Collinson 2009). Recent ethnographic work shows that men manage to retain some form of social connection with their children even if they are not members of the same household (Madhavan et al. 2008). Seasonal return migration during the Christmas and Easter seasons is evident for men (Collinson et al. 2006), but no such patterns have been recorded for children. The child cohort is composed of children aged 0–14 who ever lived in the site in the period July 1, 1999–July 1, 2008.2 Children entered the cohort either through birth or in-migration. The event occurred when a child moved out of his/her household. Observations were right-censored if they turned age 15, died, or reached the end of the study before experiencing a move. The result is a child cohort over nine years, with a total of 197,970 child-years observed for 50,978 children younger than 15. A discrete-time event history analysis was conducted whereby each child’s exposure time was divided into child-years starting at birth or entry into the household and consisting of one-year intervals. For each year (July 1 to June 30 of the following calendar year), a dummy variable indicated whether or not the child made a move before the end of that year. All individual and household measures are taken at the beginning of each period; the event of interest, child move, can occur at any time before the next update. We restricted this analysis to the child’s first move after entering the AHDSS site and included only characteristics of the sending household. Although multiple moves are indeed common in this area, in the interest of clarity, we opted to focus on one set of household conditions rather than to model multiple household circumstances. In privileging the sending over the receiving households, we do not account for the contingent nature of mobility—namely, that conditions in a receiving household influence the decision to move a child from the sending household. Unfortunately, data restrictions do not permit this analysis to be carried out. However, our focus on sending households is a valuable first step in understanding a very complex process. Finally, we combined children’s temporary and permanent migration in creating the child mobility variable because it is important, as a first step, to better understand the determinants of mobility in general before proceeding to disaggregating types of mobility. We used multilevel mixed- and fixed-effects models in STATA. Mixed-effects models incorporate both fixed and random effects, with the fixed part estimated directly as in standard regression models and the random effects summarized using their variances. This procedure has the added advantage of addressing clustering of data at both the household and child levels by explicitly modeling the contribution of the grouping variables to the total variance and adjusting the standard errors of the coefficients accordingly (Stata 2009). In order to check for bias resulting from time-constant unobserved heterogeneity within households (e.g., treatment of children or family conflict), we ran a household fixed-effects model as well. We ran several logistic regression models. The first is a set of nested models estimating the odds of a child moving using both mixed- and fixed-effects specification. Because of our interest in maternal status, we ran a second set of fixed-effects models that include various interaction effects of maternal status and the presence of maternal substitutes. Finally, we ran an ordinary logistic model estimating the odds of a child moving using cross-sectional data from 2007 that included paternal status. We used the cluster command to adjust for nonindependence of observations from having more than one child per household. The key factors of interest are mother’s survival/residence status, presence of maternal substitutes, and SES. Maternal status is categorized as 1 for coresident member of household, 2 for temporary migrant, 3 for living elsewhere, and 4 for deceased. Maternal substitutes are measured by the presence of prime-aged females (15–59) other than the mother and by the presence of at least one elderly female (60+). Prime-aged female is a three-category variable categorized as 0 for none present, 1 for one present, and 2 for two or more present. Elderly female is a dichotomous variable categorized as 0 for none and as 1 for at least one. We treat these groups of women separately because they make different types of contributions to child care as a result of their physical status and, sometimes, financial status. In South Africa, women aged 60 and older receive a means-tested noncontributory state-funded pension, which has increasingly been used to sustain households without wage earners (Case and Deaton 1998; Schatz and Ogunmefun 2007). SES is measured by household asset ownership converted into wealth quintiles. Each asset variable was weighted equally and combined into five subindicators: modern assets, livestock assets, power supply, water and sanitation, and dwelling structure. These subindicators were then combined and standardized to produce an absolute SES indicator that could discriminate between the poverty levels of different households. The absolute SES, which ranged from 0.75 to 4, was then converted into quintiles. All these variables are treated as time varying. Control variables include the sex of child, age of child categorized into three groups (0–4, 5–9, and 10–14), and number of children (other than index child) aged 0–14 in the household; the number of adult male temporary migrants in the household at the start of the period is also included to capture the effects of other income sources. With the exception of children’s sex, all are treated as time varying, although age group does not necessarily change yearly. The effect of age is confounded by time, such that children who survive one year without an event are likely to survive the next, when they are another a year older. To address this potential selection bias, we also controlled for the number of years since the first observation. Because we found no effect of duration, we did not include it in the final models. As in most surveillance sites, data quality at this site has been steadily improving over time because of the rigorous cross checking of data, training of field staff, and intensified efforts to assure internal integrity. As a result, missing data issues are not significant for most measures; the exception is paternal status, for which we find 20% of child-years without any information on fathers. Father status was first collected in 2007 on all children aged 0–17 and has not been systematically updated, although efforts are underway to standardize this aspect of data collection. This means that children who moved out of the household before 2007 would be missing this information. Therefore, we could not include this variable in the multilevel models but instead included it in a cross-sectional logistic model for 2007. Paternal status is categorized as 1 for coresident, 2 for temporary migrant, 3 for living elsewhere, and 4 for deceased. SES data were first collected in 2001 and then updated in 2003, 2005, and 2007. However, we were able to interpolate the scores for the post-2001 years in which data were not collected, leaving only 3% of child-years with missing SES data.
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