Effects of Spatial Segregation and Local Labor Markets on the Income and Employment Status of Black and White South Africans

Troy A. Powell, Duke University

This paper examines the multi-level determinants of income and employment status in South Africa in 1996, two years after the end of apartheid. In particular, it investigates whether spatial segregation and local labor markets influence the income and employment status of Black and White males. After analyzing the individual-level determinants of income and employment status, a multi-level analysis considers the effects of Black population concentration and local labor market structures on individuals’ outcomes and returns to individual-level factors across South African districts using data from the 1996 South Africa Census. Following the status attainment tradition, students of racial stratification have investigated the role of individual assets in determining inequalities in earnings and other labor market outcomes. Much research also acknowledges that structural factors such as discrimination and features of local labor markets shape the unequal labor market experiences of different racial groups. In the United States, a well-developed literature details the negative aspects of life in highly segregated areas with large concentrations of blacks (Wilson 1987; Lichter 1988; Massey and Denton 1993). These areas evolved in ways that served to isolate blacks and limit their economic opportunities in the larger society (Massey and Denton 1993). Segregation is said to create a “spatial mismatch” between black workers residing in inner cities and job opportunities, which are found increasingly in suburban areas (Lichter 1988; Kain 1992). Much research finds that the occupational and economic status of minority groups, especially blacks, declines as their concentration in the labor market increases (McCreary, England, and Farkas 1989; Burr, Galle, and Fossett 1991; Tigges and Tootle 1993; Beggs, Villemez, and Arnold 1997; Cohen 1998). On the other hand, a large literature analyzing immigrant populations finds that high levels of minority concentration can actually improve the economic status of immigrant groups (Waldinger 1986; Wilson and Portes 1980; Portes and Bach 1985; Zhou 1992). Some researchers have applied these arguments to blacks in the United States (Logan and Alba 1999; McCreary et al. 1989). This paper investigates these issues in South Africa – a society noted for extreme racial inequality and segregation that has experienced a rapid transformation to a non-racial multi-party democracy in the last decade. Specifically, it examines the effects of individual assets, including education and labor force experience, and structural factors, such as spatial segregation and local opportunity structures, on the income and employment status of Blacks and Whites two years after the demise of apartheid. It establishes an empirical foundation for the continuing study of social change and stratification processes in a society in flux. South Africa is an ideal case in which to examine the impact of spatial segregation on race-based inequality for several reasons. First, South Africa has the highest level of racial inequality in the world: the poorest 50% of the population is predominantly Black and holds less than 10% of total income; the richest 6% of the population, largely White, holds more than 40% of the country’s total income. Second, it is a society marked by extreme racial segregation. Under the guise of “separate development,” the apartheid state ensured a high degree of segregation between Blacks and Whites by forcibly relocating many Blacks to impoverished and remote “homelands.” The map in Figure 1 ( http://www.duke.edu/~tap5/figure1.pdf ) indicates the distribution of Blacks across magisterial districts with former homeland boundaries overlaid. A complex system of apartheid legislation further enforced the separation of racial groups in all aspects of life. Third, South Africa is different from most countries in that its most subordinated group, Blacks, constitutes the majority of the population. In 1996, 77% of the population was Black and only 11% was White; Coloureds and Asians comprised the remaining 9% and 3% of the population, respectively. These features of South African society make it well suited for examining how different theoretical perspectives regarding spatial segregation and racial stratification apply in diverse contexts. In a previous multi-level study of occupational status differentials in South Africa (Powell and Buchmann 2002) we find that Blacks in highly segregated labor markets, such as those designated as “homelands” during apartheid, enjoy higher occupational status than Blacks employed in the mainstream South African economy. We maintain that while the spatial segregation of Blacks in homelands effectively excluded them from economic and political opportunities on a national level, it also fostered the development of an enclave economy that benefited some Blacks by providing access to high status jobs free of competition from super-ordinate racial groups. These findings contrast with the occupational experiences of Blacks in the United States and conventional wisdom regarding detrimental effects of segregation for Blacks in South Africa, and they underscore the multiple and varied impacts of segregation on the social and economic outcomes of disadvantaged groups in multiracial societies. The current analysis will provide a more complete understanding of the effects of segregation on the labor market outcomes of Blacks in South Africa. While homeland labor markets may have enabled most employed Blacks to attain higher occupational status than they could in multi-racial labor markets, homeland labor markets may have had other detrimental effects on the labor market outcomes of Blacks. These labor markets were likely not well developed or large enough to offer jobs to the millions of Blacks forced to reside within homeland boundaries and, thus, larger proportions of Blacks were likely unemployed in these areas. It is also possible that the incomes of employed Blacks were lower in highly segregated districts. Descriptive statistics comparing Blacks and Whites residing in different types of districts provide some preliminary evidence for these possibilities. Table 1 ( http://www.duke.edu/~tap5/table1.pdf ) presents descriptive statistics for Black residing in former homelands districts, non-homeland districts that are greater than 95% Black, and all other districts; Whites living in former homelands are compared to Whites in other districts. The table indicates that labor markets in former homeland districts are weighted toward higher-status occupations, but they are simultaneously marked by higher rates of unemployment. To investigate these possibilities further, this study utilizes the 1996 ten percent sample of the South African Population Census – a representative national unit-level sample of all households and individuals totaling 3,621,201 respondents in 846,479 households. We aggregate census data in order to construct several district-level indicators of demographic and labor market characteristics. Magisterial districts in South Africa are roughly equivalent to counties in the United States; the 354 magisterial districts vary in geographic size but contain on average about 100,000 people. To distinguish adequately the effects of variables at both the individual and district level we use Hierarchical Linear Modeling (HLM) to investigate whether: 1) individual variations in human capital (education, labor force experience, English proficiency) account for variation in employment status and income within districts, 2) the racial composition and other attributes of districts (e.g., percent Black, percent urban, percent of professional, routine non-Manual, skilled or other manual, and agricultural workers in the district, and whether the district was a former homeland) relate to the mean employment status and income of districts, and 3) the characteristics of districts affect individuals’ employment status and income directly or indirectly through their effects on other individual factors. This study also utilizes Geographic Information System (GIS) software to visually represent our district-level finding. 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