Tell me who’s your neighbour and I’ll tell you how much time you’ve got: The spatiotemporal consequences of residential segregation

Bó, B., & Dukhovnov, D. (2022). Population, Space and Place, e61: 1-18. doi:10.1002/psp.2561

https://onlinelibrary.wiley.com/doi/full/10.1002/psp.2561

1 INTRODUCTION

Social scientists have studied the relationships between ethnic residential segregation and socioeconomic inequality for over 100 years (DuBois, 1899; Massey & Denton, 1988). We continue to unpack how inequality clusters spatially, contributing to the intergenerational transmission of disadvantage. Yet, despite our calls for action, the United States is still a highly ethnically segregated society. For example, although the Latinx population is projected to double in the next 40 years—forming one-third of the total population of the country and the largest ethnic minority group in the United States—their population growth has been accompanied by increased segregation from other ethnic groups (Bernstein, 2013; Lichter et al., 2015; Rugh & Massey, 2014). These rapidly shifting patterns in Latinx geographic segregation point to widening economic, cultural and social distance from other ethnic groups (Rumbaut, 2011; Tienda & Fuentes, 2014). Recent research has also found that residential segregation has negative economic effects for this population, likely influencing their population-level time use profiles (Bernstein, 2013; De la Roca et al., 2018; Hamermesh, 2019). This has both micro- and macro-level consequences: segregation shapes individual life chances, influences local opportunity structures and the characteristics of neighbourhoods (Alba & Foner, 2015, Crowder & South, 2008).

This study seeks to reorient current theorizing on Latinx residential segregation by centering sociotemporal inequalities. We introduce the idea that it is imperative to consider time when theorizing about how ethnic residential segregation affects needed resources. Focusing on per capita discretionary time left over after meeting daily survival needs, we examine the puzzle of ethnic residential segregation and discretionary time availability. Though we know that neighbourhood-level sociodemographic characteristics are central for the social processes that drive stratification (Castañeda et al., 2015; Harding, 2007), the spatial distribution of time availability is still understudied (Castañeda, 2018). Methodological complexities and data limitations have thus far prevented us from concretely examining how ethnic segregation shapes time use disparities (Castañeda, 2018). This is unfortunate, since discretionary time is necessary for combating entrenched inequalities, garnering socioeconomic resources, and for all aspects of individual and community well-being (Giurge et al., 2020; Goodin et al., 2008; Kalenkoski & Hamrick, 2013; Massey & Fischer, 2000; Williams et al., 2016).

Furthermore, neighbourhoods are not isolated islands. They are influenced by each other: neighbourhood mobility and sociodemographic patterns are altered by the ethnic compositions of adjacent neighbourhoods (Crowder & South, 2008; Wilson & Taub, 2007). Yet, we still do not know how ethnic segregation in adjacent neighbourhoods may shape discretionary time availability in a nearby neighbourhood. Ethnographic research suggests that segregation matters for between-neighbourhood time use, influencing the well-being of residents (e.g., commuting patterns and waiting times) (Castañeda, 2018; Edwards, 2017). However, until now, we have been unable to examine this systematically via large-scale quantitative data. Thus, our understanding is incomplete when it comes to how the interplay between ethnic segregation and neighbourhood-level sociodemographic conditions shapes discretionary time availability.

We begin by putting prevalent theoretical perspectives from the neighbourhood effects literature in conversation with theories on segregation and from the sociology of time, explicitly touching on the hypotheses undergirding our study. Next, we provide an overview of the data, followed by our downscaling method producing local estimates of discretionary time, then review and compute our segregation measures. We ask three related questions:

1.Does per capita discretionary time availability vary spatially, and if so, does residential ethnic segregation matter for discretionary time availability?
2.How do sociodemographic characteristics influence the above?
3.Do the characteristics of adjacent neighbourhoods affect the relationship between segregation and time availability?

2 DISCRETIONARY TIME AND NEIGHBOURHOOD EFFECTS

While no one has more than 24 h in a day, not everyone has the same amount of discretionary time. We offer a more precise operationalization of discretionary time in our methods section, but the construct can be efficiently summarized as the time left over for discretionary activities after the minimal amount of time needed to satisfy bodily, financial and household needs has been spent. Discretionary time is imperative for individual well-being and for the maintenance of a functioning society (Goodin et al., 2008; Hamermesh, 2019; Rosa, 2013; Whillans, 2019; Williams et al., 2016). A growing body of literature notes that contextual factors and sociodemographic variables influence discretionary time availability (Giurge et al., 2020; Hamermesh, 2019; Kalenkoski & Hamrick, 2013). Overall, those with more economic and cultural resources have more control over their time and more autonomy over their discretionary time. Those with less resources spend extra time to make enough money. Those with higher status can purchase time by outsourcing menial tasks (Hamermesh, 2019). Thus, discretionary time is intimately intertwined with social inequalities. Per capita discretionary time availability is a highly responsive measure to sociodemographic constraints and opportunities, while also being sensitive to historical and structural conditions: when structural inequality impedes on discretionary time availability, this has both individual and societal ripple effects (Goodin et al., 2008; Hamermesh, 2019; Williams et al., 2016).