Previous research has shown that parents often have strong ethnicity-related school preferences, and it has been suggested that these preferences are consequential for the ethnic segregation of schools. In this article, we study all students enrolled in compulsory schooling in the Stockholm region during the years 2008 to 2017. Using a combination of statistical analyses of school choices and large-scale, empirically calibrated simulations, we investigate how preferences and opportunities jointly influence the students' mobility between schools and the school segregation that their mobility or lack thereof gave rise to. Our main finding is that opportunities generally outweigh preferences. While ethnicity-related school preferences exist, they have little impact on ethnic segregation because the schools that students move between tend to have similar ethnic compositions.

Social-influence processes not only affect the rate at which behaviors spread but can also decouple adoption behavior from individual preferences, and thereby bring about unexpected collective outcomes that cannot be predicted on the basis of the initial likes and dislikes of the individuals involved. However, the conditions under which social influence can lead to such decoupling are not well understood. We identify a social-influence mechanism that widens individuals’ behavioral repertoires and breaks the link between individuals’ initial preferences and the collective outcomes they jointly bring about. We test the micro-level assumptions of the mechanism in the context of cultural choices on Spotify, combining topic modeling with traditional statistical matching to cultural change. agent-based simulation estimate peer-to-peer influence effects from digital trace data. We then use agent-based simulations to examine the macro-level consequences of “wide” social influence and its importance for explaining cultural change.

Analytical sociology is beginning to embrace a digital revolution in the collection and analysis of social data and is increasingly drawing on tools from computational social science (CSS) to pursue its goals of mechanism-based explanation of aggregate outcomes. In this chapter, we highlight the ways in which analytical sociologists are using CSS tools to further social research. Using agent-based modeling, large-scale online experiments, digital trace data, and natural language processing, analytical sociologists are identifying how large-scale properties of social systems emerge from the complex interactions of networked actors at lower scales. At the same time, we provide a perspective on how CSS techniques can be successfully deployed in social research, including ways in which they can be productively combined. Computational tools, when applied using a theory-grounded approach, offer sociologists a chance to transcend the limitations of the dominant survey-research paradigm and finally address “big” sociological questions about, for example, the nature of culture, the emergence of inequality, and the dynamics of segregation. We also discuss how computational social scientists can take cues from analytical sociology to further hone their own research and methods in the service of theoretically grounded, mechanism-based explanations, moving beyond theoretically thin descriptions or predictions of micro- and macro-level outcomes.

In this chapter, I reflect on how the field of analytical sociology has developed since the late 1990s. I discuss briefly the birth of analytical sociology, what motivated its emergence, and what its most essential characteristics are. Thereafter, and in more detail, I consider a set of topics that I believe to be of crucial importance for the future of analytical sociology. In particular, I focus on the role of action-based explanations of macro-level change. I argue that analytical sociologists should be less wedded to intentional explanations, and instead focus their efforts on constructing empirically plausible and empirically verifiable models of action and interaction.  

The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse mechanism, which, in contradiction to the main tenet of previous research, suggests that network-based recruitment reduce rather than increase segregation levels. We identify the conditions under which networks are desegregating, and using unique data on all individuals and all workplaces located in the Stockholm region during the years 2000-2017, we find strong empirical evidence for the Trojan-horse mechanism and its role in the gender segregation of labor markets.

Kin ties are all but ubiquitous in new firms. However, their effects on performance are not straightforward, as they can provide new firms with advantages (enhanced coordination and cooperation) as well as disadvantages (reduced diversity, nepotism concerns, and the possible spillover of personal conflict). As kin ties may have both positive and negative implications for performance, a contingency approach to the performance of new firms is valuable. We develop such an approach by relating different structural configurations of kin ties-whether they are between founders, between founders and employees, or between employees-to the performance of new firms. We test our predictions using data on 4,967 new firms founded in Stockholm between 1998 and 2003. Our theory deepens our understanding of why kin ties have heterogeneous effects on the performance of new firms.

Superlinear growth in cities has been explained as an emergent consequence of increased social interactions in dense urban environments. Using geocoded microdata from Swedish population registers, we remove population composition effects from the scaling relation of wage income to test how much of the previously reported superlinear scaling is truly attributable to increased social interconnectivity in cities. The Swedish data confirm the previously reported scaling relations on the aggregate level, but they provide better information on the micromechanisms responsible for them. We find that the standard interpretation of urban scaling is incomplete as social interactions only explain about half of the scaling parameter of wage income and that scaling relations substantively reflect differences in cities sociodemographic composition. Those differences are generated by selective migration of highly productive individuals into larger cities. Big cities grow through their attraction of talent from their hinterlands and the already-privileged benefit disproportionally from urban agglomeration.

Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, machine learning, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology’s core questions, computational social science has the potential to advance sociology in a similar way that the introduction of econometrics advanced economics during the last half century. Computational social scientists from computer science and physics often see as their main task to establish empirical regularities which they view as “social laws.” From the perspective of the social sciences, references to social laws appear unfounded and misplaced, however, and in this article we outline how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.

It is widely recognized that segregation processes are often the result of complex nonlinear dynamics. Empirical analyses of complex dynamics are however rare, because there is a lack of appropriate empirical modeling techniques that are capable of capturing complex patterns and nonlinearities. At the same time, we know that many social phenomena display nonlinearities. In this article, we introduce a new modeling tool in order to partly fill this void in the literature. Using data of all secondary schools in Stockholm county during the years 1990 to 2002, we demonstrate how the methodology can be applied to identify complex dynamic patterns like tipping points and multiple phase transitions with respect to segregation. We establish critical thresholds in schools’ ethnic compositions, in general, and in relation to various factors such as school quality and parents’ income, at which the schools are likely to tip and become increasingly segregated.