Neighbourhood effects studies typically investigate the negative effects on individual outcomes of living in areas with concentrated poverty. The literature rarely pays attention to the potential beneficial effects of living in areas with concentrated affluence. This poverty paradigm might hinder our understanding of spatial context effects. Our paper uses individual geocoded data from the Netherlands to compare the effects of exposure to neighbourhood affluence and poverty on educational attainment within the same statistical models. Using bespoke neighbourhoods, we create individual neighbourhood histories which allow us to distinguish exposure effects from early childhood and adolescence. We follow an entire cohort born in 1995 and we measure their educational level in 2018. The results show that, in the Netherlands, neighbourhood affluence has a stronger effect on educational attainment than neighbourhood poverty for all the time periods studied. Additionally, interactions with parental education indicate that children with higher educated parents are not affected by neighbourhood poverty. These results highlight the need for more studies on the effects of concentrated affluence and can inspire anti-segregation policies.

The non-random selection of people into neighbourhoods complicates the estimation of causal neighbourhood effects on individual outcomes. Measured neighbourhood effects could be the result of characteristics of the neighbourhood context, but they could also result from people selecting into neighbourhoods based on their preferences, income, and the availability of alternative housing. This paper examines how the neighbourhood effect on individual income is altered when geographic selection correction terms are added as controls, and how these results vary across three Dutch urban regions. We use a two-step approach in which we first model neighbourhood selection, and then include neighbourhood choice correction components in a model estimating neighbourhood effects on individual income. Using longitudinal register datasets for three major Dutch cities: Amsterdam, Utrecht and Rotterdam, and multilevel models, we analysed the effects for individuals who moved during a 5-year period. We show that in all cities, the effect of average neighbourhood income on individual income becomes much smaller after controlling for explicitly modelled neighbourhood selection. This suggests that studies that do not control for neighbourhood selection most likely overestimate the size of neighbourhood effects. For all models, the effects of neighbourhood income are strongest in Rotterdam, followed by Amsterdam and Utrecht.

Studies of neighbourhood effects increasingly research the neighbourhood histories of individuals. It is difficult to compare the outcomes of these studies as they all use different datasets, conceptualisations and operationalisations of neighbourhood characteristics and outcome variables. This paper contributes to the literature by studying educational attainment and comparing the effects of the timing, accumulation, duration and sequencing of exposure to neighbourhood poverty. We use longitudinal register data to study the population of children born in the Netherlands in 1995 and follow them until the age of 23. Our findings show that it is important to separate the early adult years (age 18–22) when constructing individual histories of exposure to neighbourhood poverty. We find that the effect of exposure to neighbourhood deprivation on educational attainment during adolescence is slightly stronger than the effect of exposure during childhood. We conclude that the observed relationship between neighbourhood poverty and educational attainment depends on how exposure to the neighbourhood effect is conceptualised and measured; choosing just one dimension could lead to under- or overestimation of the importance of exposure to neighbourhood poverty.

The hype and hope surrounding big data has resulted in articles warning the scientific community about their pitfalls: problems surrounding subjects’ privacy, difficult access reducing reproducibility, non-representative sampling, lack of theory, and the acceptance of accidental results as substantively significant. Still, these articles remain undecided in their conclusions about what big data actually mean for sociology. Could they change the process of doing research, tempting the scientists to depart from the current advocated practices of their field? This paper seeks to answer this question by describing the current main scientific paradigm in the field of sociology and using the issues of access, privacy, sampling, theory and multiple testing to observe the extent of this departure, defined as scholarly negligence. We analyse sociological papers applying big data published from 2008 until March 2017, identified through a systematic literature review. A growing popularity of big data within sociology and most of its subfields is observed (52 articles, 0.7% of all in sociology, used big data as of 2016), together with a positive association between researchers’ experience with big data and lower levels of scholarly negligence for all the issues, except for multiple testing. We also find an association between scholarly negligence and articles being published more recently.