What Mix Matters? Exploring the Relationships between Individuals' Incomes and Different Measures of their Neighbourhood Context

Abstract

There is substantial interest among policy makers in both Western Europe and North America in reducing concentrations of disadvantaged households through initiatives to enhance the ‘social mix’ of neighbourhoods. However, there is little consideration or understanding with regard to which mix of household characteristics matters most in influencing the socio-economic outcomes for individual residents. This paper explores the degree to which a wide variety of 1995 neighbourhood conditions in Sweden are statistically related to earnings for all adult metropolitan and non-metropolitan men and women during the 1996–99 period, controlling for a wide variety of personal characteristics. The paper finds that the extremes of the neighbourhood income distribution, operationalized by the percentages of adult males with earnings in the lowest 30th and the highest 30th percentiles, hold greater explanatory power than domains of household mix related to education, ethnicity or housing tenure. Separating the effects of having substantial shares of low and high income neighbours, it is found that it is the presence of the former that means most for the incomes of metropolitan and non-metropolitan men and women, with the largest effects for metropolitan men.

Keywords:

• Neighbourhood effects
• household mix
• longitudinal
• Sweden

Notes

¹ For example, see the two volume Neighbourhood Poverty, (Eds) Brooks-Gunn et al. (1997); the reviews in Dietz (2002); Duncan & Raudenbush (1999); Ellen & Turner (1997, 2003); Friedrichs (1998); Friedrichs et al. (2003); Leventhal & Brooks-Gunn (2000); Robert (1999); Sampson et al. (2002); and the special issue of Housing Studies, 18(6), 2003.

² In Western Europe the issue links up with ongoing discussions over ‘social exclusion’. Although not typically seen as a cause of social exclusion, it can serve as a mediator that can deepen or weaken it (Murie & Musterd, 2004). In the US the issue is linked with ‘concentrated poverty’, articulated most prominently by Wilson (1987) and Jargowsky (1997).

³ For example, see Van Kempen (1997) and Bauder (2001). For a recent Swedish contribution to the study of spatial mismatch, see Åslund et al. (2007).

⁴ The analysis intentionally excludes recent (after 1991) immigrants to Sweden because it is thought that their labour market experience is neither indicative of their longer-term economic value nor reflective of their initial neighbourhood environments when they enter Sweden. The authors are conducting a companion analysis that focuses on neighbourhood effects for immigrants.

⁵ The log-linear transformation is not only appropriate given the positive skew of the income distribution, but also has sound grounding in economic theory, implicitly suggesting that income is an interactive (not additive) function of personal, neighbourhood, municipality and labour market characteristics.

⁶ Formally, income from work is computed here as the sum of: cash salary payments, income from active businesses and tax-based benefits that employees accrue as terms of their employment (sick or parental leave, work-related injury or illness compensation, daily payments for temporary military service or giving assistance to a handicapped relative).

⁷ There remains some unavoidable inter-urban variation in SAMS scale nevertheless. At the extremes, the average SAMS in Gothenburg has a population of about 500 but in Stockholm it contains over 10 times as many people.

⁸ Public rental in Sweden means almost entirely multi-family dwellings owned by municipal housing companies. These companies have emerged over a period of 60 years and they now possess about 20 per cent of all dwelling units (local variations). Public rental is not means tested and allocation is normally arranged in the form of waiting lists. Sweden does not have condominiums so owner occupation means single housing. Close to 50 per cent of the population live in owner-occupied houses. Urban residential segregation processes primarily sort people between home ownership and rental housing. See for example Musterd & Andersson (2005, note (i)) for more information on housing and residential segregation in Sweden.

⁹ The control variables' coefficients vary little across the alternative neighbourhood mix specifications.

¹⁰ R-squared value from a model with the control variables but without any neighbourhood variables is 0.455 for metropolitan males, 0.452 for metropolitan females, 0.459 for non-metropolitan males and 0.458 for non-metropolitan females.