We use longitudinal data from the National Income Dynamics Study (NIDS) to document the extent of recent short-term residential and household composition change in South Africa. We examine educational and labor market transitions among movers and the occurrence of the four main types of compositional change – births, additions of accessions, deaths and loss of leavers. Among non-movers, compositional change is more likely for blacks and people of color, young adults and children, women, urban individuals, and lower income individuals.
Under both apartheid and earlier policies of separate development, long-distance migrant labor played a central role in the process that trapped the majority of South Africa's population in remote, overcrowded pockets of the country. We use the first two waves of the National Income Dynamics Study (NIDS) (SALDRU, 2012a, 2012b) to detail housing dynamics and changes in household composition at the national level in South Africa. First, we explore the extent of residential and compositional change among NIDS respondents overall and separately by population group.
Changing composition is not that complicated because all members of a family must fall into the same category of composition changers or non-changers. Our analytic sample consists of individuals who were successfully interviewed in waves 1 and 2 of the NIDS study. Changing family composition in the short term is extremely common: 61.3% of our sample members changed family composition between waves.
Population group differences in residential changes were relatively small compared to the very large differences in compositional change.
Correlates of Residential Change
The coefficients in the second column of Table 3 illustrate the unconditional proportion of respondents who moved by age. This is not surprising, as young adulthood is traditionally the period in the life course when many individuals move out of their parental home. It is also not surprising that the elderly moved much less, as this is the period in the life course when many individuals are no longer as mobile as they might have been in their younger years.
The coefficients in the third column of Table 3 illustrate the unconditional proportion of respondents who moved by gender. The coefficients in the fourth column of Table 3 show that respondents living in urban areas were generally more likely to move than similar respondents in rural areas—and this is true after controlling for demographic characteristics and regardless of model specification (column 6 and 7). . The coefficients in the fifth column of Table 3 illustrate the unconditional proportion of respondents who moved by household income per capita (recorded) in Wave 1.
To better understand why these migrations may have occurred, we examine changes in the education and employment status of migrants between the two waves. Because educational and employment status varies according to the respondent's place in the life course, we stratified the transition matrices by age group, with one matrix for each of the following groups: age 13‐. This is not surprising given the country's very high school enrollment rate.
A smaller number (14.1%) indicated that they had no education and were not in the labor force – 'other' – while an even smaller number transitioned to the labor market (2.5% unemployed and 1.1% employed at wave 2). According to the first row of the table, of those in education at wave 1, only 33% were still in education at wave 2, while 26% were unemployed, 20% were employed and 25% were neither in education nor training. the labor market. About an equal percentage of movers who were unemployed during the first wave returned to education (22%) or were employed (20.8%).
The third row of Table 5 shows that those employed at wave 1 were slightly more stable. Finally, the fourth row of Table 5 shows that the majority of migrants (62.2%) who were neither in the labor market nor in training in the first wave have re-entered the labor force, although only about two-thirds (43 .5%) had a job. . The first row of Table 6 shows that only a very small proportion (2.6%) of movers in this age group received training in the first wave.
Correlates of Compositional Change
The coefficients in the second column of Table 7 illustrate the unconditional proportion of non-movers whose household composition changed by age. Overall, these estimates indicate that young adults (age) were most likely to experience compositional change, while older adults (age) were least likely to experience compositional change between waves. For example, the higher incidence of household composition change in young adults (18–25) is driven entirely by blacks, while the lower incidence in older adults (60+) is driven entirely by non-blacks.
The coefficients in the third column of Table 7 illustrate the unconditional proportion of non-movers who experienced a change in family composition by gender. However, this gender difference is completely driven by blacks (see Appendices 4-6), which is likely a result of the high prevalence of female-headed households in this subpopulation. The coefficients in the fourth column of Table 7 illustrate the unconditional proportion of non-movers who experienced a change in household composition by urban/rural household location.
While the estimate shows that rural non-movers were more likely to experience a change in household composition than urban non-movers, this difference was fully explained after controlling for other demographic characteristics. Further examination by population group (see Appendices 4–6) reveals that this urban–rural difference is only present among blacks, with no significant differences between blacks and whites in urban and rural areas. Finally, the coefficients in the sixth column of Table 7 illustrate the association between income (logged) and change in household composition for those who did not move.
We examine the extent of such changes in those who did not change their place of residence between the waves, but experienced a change in household composition. These results indicate that during the two years between waves, many blacks experienced several types of compositional change, with the addition of new members due to non-birth (other members) being the most prevalent type. According to the second row of Table 8, Coloreds experienced various types of compositional changes in similar proportions to Blacks, with a slightly higher percentage of Coloreds experiencing intra-household birth(s) and slightly lower percentages experiencing death(s) and the loss of one or several members for reasons other than death (other withdrawals).
Yet, as with blacks, the most common form of compositional change among coloreds is the addition of one or more new members for reasons other than birth (other joiners). It is also important to emphasize the fact that the sample for this analysis is limited to non-movers who changed household composition between waves. So while only a small proportion (20%) of whites did not move residence, but experienced a change in composition, the third row in Table 8 provides insight into the predominant types of.
Discussion
Summary of Findings
Implications, Limitations, and Future Directions
Revealing the full range of household experiences of HIV and AIDS in rural South Africa. Intra-Family Transfers and Income Pooling: A Study of Remittances in KwaZulu-Natal, South African Journal of Economics. Labor Migration and Households: Rethinking the Labor Supply Effects of the Social Pension in South Africa.
Appendix 1: Residential change among Africans
Appendix 2: Residential change among Coloureds
Appendix 3: Residential change among Whites
Appendix 4: Compositional change among African stayers
Appendix 5: Compositional change among Coloured stayers
Appendix 6: Compositional change among White stayers
