Elizabeth E. Bruch

NEIGHBORHOOD CHOICE AND NEIGHBORHOOD CHANGE

This article examines the relationships between the residential choices of individuals and aggregate segregation patterns. Analyses based on computational models show that high levels of segregation occur only when individuals’ preferences follow a threshold function. If individuals make finer‐grained distinctions among neighborhoods that vary in racial composition, preferences alone do not lead to segregation. Vignette data indicate that individuals respond in a continuous way to variations in the racial makeup of neighborhoods rather than to a threshold. Race preferences alone may be insufficient to account for the high levels of segregation observed in American cities.

Agent-Based Models in Empirical Social Research.

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research.

METHODOLOGICAL ISSUES IN THE ANALYSIS OF RESIDENTIAL PREFERENCES, RESIDENTIAL MOBILITY, AND NEIGHBORHOOD CHANGE.

This paper reviews methods for analyzing both individual preferences and choices about where to live, and the implications of these choices for residential patterns. Although these methods are discussed in the context of residential choice, they can be applied more broadly to individual choices in a range of social contexts where behavior is interdependent. We also discuss specific problems with residential mobility data, including the treatment of one’s current location as a potential choice, the aggregation of units and the need to take into account variations in neighborhood size, the problem of very large choice sets, and the link between residential mobility and patterns of neighborhood change.

HOW POPULATION STRUCTURE SHAPES NEIGHBORHOOD SEGREGATION

This study provides a framework for understanding how population composition conditions the relationship between individuals’ choices about group affiliation and aggregate patterns of social separation or integration. The substantive focus is the role of income inequality in racial residential segregation. The author identifies three population parameters—between-group inequality, within-group inequality, and relative group size—that determine how income inequality between race groups affects racial segregation. She uses data from the Panel Study of Income Dynamics to estimate models of individual-level residential mobility and incorporates these estimates into agent-based models. She then simulates segregation dynamics under alternative assumptions about (1) the relative size of minority groups and (2) the degree of correlation between race and income among individuals. The author finds that income inequality can have offsetting effects at the high and low ends of the income distribution. She demonstrates the empirical relevance of the simulation results using fixed-effects, metro-level regressions applied to 1980–2000 U.S. census data.

PREFERENCES AND PATHWAYS TO SEGREGATION: REPLY TO VAN DE RIJT, SIEGEL, AND MACY.

We are pleased that Van de Rijt, Siegel, and Macy have taken an interest in our work. Since the publication of our article (Bruch and Mare 2006) we too have examined the role of random error in segregation dynamics and formally examined the relationship between residential preferences and segregation (Mare 2007; Tuljapurkar, Bruch, and Mare 2008). We welcome the opportunity to discuss these issues, compare our conclusions to those of Van de Rijt et al., and extend our 2006 argument regarding the preferences of individuals and the dynamics of residential segregation. We also acknowledge and present corrections of errors in our 2006 article. In preparing our software for public release, we found an error in our computer code. Our corrected results show that, as Van de Rijt et al. point out, some continuous functions for individuals’ decisions about whether and where to move that we originally claimed would generate integration in fact lead to segregation. Our original findings regarding continuous functions with varying β parameters (Bruch and Mare 2006, p. 692) were wrong. This reply to Van de Rijt et al. includes corrected versions of our simulations. However, the error in our code notwithstanding, we believe that our original conclusions regarding the effects of the form of individual preferences on segregation dynamics are correct. Our corrected software—including an executable file, the open-source Java code, and a suite of testing software for verifying key features of agent-based models—is publicly available.2 Our software can be extended to look at various dynamic processes (e.g., marriage markets, peer effects, and the spread of innovation), and we encourage interested researchers to build upon our source code. Our 2006 article reported an investigation of the links between how people evaluate neighborhoods and aggregate segregation dynamics. We emphasized that the shape of residential choice functions (i.e., how individuals evaluate and choose neighborhoods) has important implications for segregation dynamics. Simulations based on our corrected code show that, as Van de Rijt et al. report, hypothetical monotonic, continuous functions with a sufficiently strong response to the racial makeup of a neighborhood (the coefficient β in the functions that describe residential choice) can generate segregation and that empirical preference functions based on Detroit Area Study (DAS) data are consistent with high segregation. However, our argument that “regions of indifference” across neighborhoods with varying ethnic composition (a key feature of threshold functions) play an important role in segregation dynamics still holds. Moreover, as we show below, the shape of residential preference functions affects segregation dynamics through other pathways as well. Our reply first summarizes our ideas about preference functions and the effect of random variation on segregation dynamics and then responds more directly to Van de Rijt et al.’s comment. We review the ways that randomness enters into choice processes and argue that the shape of residential choice functions—for example, whether preferences follow a continuous or a threshold function—affects segregation dynamics through three pathways: the baseline level of randomness in the choice process, how random error fades out or cumulates over time, and the speed with which integrated communities converge to an equilibrium level of segregation. Our corrected agent-based models produce the same patterns as the ones shown by Van de Rijt et al., and we agree with them that continuous functions with a sufficiently high β can produce segregation. However, we believe that their claim that “sensitivity to chance” matters more than “sensitivity to change” is misleading because these are not separable dimensions of the choice function. Rather, these factors interact in a complex yet interpretable way. We also show that, contrary to Van de Rijt et al.’s claims, continuous functions with a sufficiently low randomness do not result in higher levels of segregation than threshold functions, although continuous functions do reach equilibrium more rapidly. We also explain an important feature of Van de Rijt’s figures C1 and C2, namely that, above a certain β value, the threshold functions appear less responsive than continuous functions to increases in β. We then address the issue, raised by Van de Rijt et al., of how preferences for racial integration affect segregation. We find their argument regarding “the paradox of strong versus weak preferences” unpersuasive. Van de Rijt et al. argue that stronger preferences for integration result in higher segregation when residential choice follows a continuous rather than a threshold function, but we show that this conclusion is an artifact of their highly stylized specification of these functions. Van de Rijt et al.’s “paradox” occurs only under a narrow set of assumptions. We show that preferences for integration expressed by black respondents in the DAS are consistent with very low segregation and offer a more plausible statement about the link between preferences for integration and segregation dynamics.

Decision-Making Processes in Social Contexts

Over the past half-century, scholars in the interdisciplinary field of Judgment and Decision Making have amassed a trove of findings, theories, and prescriptions regarding the processes ordinary people enact when making choices. But this body of knowledge has had little influence on sociology. Sociological research on choice emphasizes how features of the social environment shape individual behavior, not peoples underlying decision processes. Our aim in this article is to provide an overview of selected ideas, models, and data sources from decision research that can fuel new lines of inquiry on how socially situated actors navigate both everyday and major life choices. We also highlight opportunities and challenges for cross-fertilization between sociology and decision research that can allow the methods, findings, and contexts of each field to expand their joint range of inquiry.

Extracting multistage screening rules from online dating activity data

Significance Online activity data—for example, from dating, housing search, or social networking websites—make it possible to study human behavior with unparalleled richness and granularity. However, researchers typically rely on statistical models that emphasize associations among variables rather than behavior of human actors. Harnessing the full informatory power of activity data requires models that capture decision-making processes and other features of human behavior. Our model aims to describe mate choice as it unfolds online. It allows for exploratory behavior and multiple decision stages, with the possibility of distinct evaluation rules at each stage. This framework is flexible and extendable, and it can be applied in other substantive domains where decision makers identify viable options from a larger set of possibilities. This paper presents a statistical framework for harnessing online activity data to better understand how people make decisions. Building on insights from cognitive science and decision theory, we develop a discrete choice model that allows for exploratory behavior and multiple stages of decision making, with different rules enacted at each stage. Critically, the approach can identify if and when people invoke noncompensatory screeners that eliminate large swaths of alternatives from detailed consideration. The model is estimated using deidentified activity data on 1.1 million browsing and writing decisions observed on an online dating site. We find that mate seekers enact screeners (“deal breakers”) that encode acceptability cutoffs. A nonparametric account of heterogeneity reveals that, even after controlling for a host of observable attributes, mate evaluation differs across decision stages as well as across identified groupings of men and women. Our statistical framework can be widely applied in analyzing large-scale data on multistage choices, which typify searches for “big ticket” items.

Aspirational pursuit of mates in online dating markets

We present an empirical analysis of heterosexual dating markets in four U.S. cities using data from a popular online dating service. Romantic courtship is often described as taking place in a dating market where men and women compete for mates, but the detailed structure and dynamics of dating markets have historically been difficult to quantify for lack of suitable data. In recent years, however, the advent and vigorous growth of the online dating industry has provided a rich new source of information on mate pursuit. We present an empirical analysis of heterosexual dating markets in four large U.S. cities using data from a popular, free online dating service. We show that competition for mates creates a pronounced hierarchy of desirability that correlates strongly with user demographics and is remarkably consistent across cities. We find that both men and women pursue partners who are on average about 25% more desirable than themselves by our measures and that they use different messaging strategies with partners of different desirability. We also find that the probability of receiving a response to an advance drops markedly with increasing difference in desirability between the pursuer and the pursued. Strategic behaviors can improve one’s chances of attracting a more desirable mate, although the effects are modest.

Housing Market Activity is Associated with Disparities in Urban and Metropolitan Vegetation

In urban areas, the consistent and positive association between vegetation density and household income has been explained historically by either the capitalization of larger lawns and lower housing densities or landscaping and lifestyle districts that convey prestige. Yet cities with shrinking populations and rising land burdens often exhibit high vegetation density in declining neighborhoods. Because the observed associations do not directly address the causal connection between measures of social privilege and vegetation in urban landscapes, it is difficult to understand the forces that maintain them. Here, we compare patterns of household income with new measures derived from housing market data and other parcel-level sources—sale prices, tax foreclosures, new housing construction, demolitions, and the balance of construction and demolition. Our aim is to evaluate whether these spatially, temporally and semantically finer measures of neighborhood social conditions are better predictors of the distribution of urban vegetation. Furthermore, we examine how these relationships differ at two scales: within the City of Detroit and across the Detroit metropolitan area. We demonstrate, first, that linear relationships between income or home values and urban vegetation, though evident at broad metropolitan scales, do not explain recent variations in vegetation density within the City of Detroit. Second, we find that the real estate and demolition records demonstrate a stronger relationship with changes in vegetation density than corresponding changes in US Census measures like income, which suggests they hold at least as much interest for understanding how the relationships between biophysical changes and neighborhood change processes come about.

Individuals' Decisions in the Presence of Multiple Goals

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research.