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DTSTART;TZID=Europe/Amsterdam:20220718T230000
DTEND;TZID=Europe/Amsterdam:20220722T225959
DTSTAMP:20260504T040858
CREATED:20220704T111256Z
LAST-MODIFIED:20220711T113451Z
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SUMMARY:Capturing the social fabric: population-scale socio-economic segregation patterns
DESCRIPTION:Conference poster presentation by Yuliia Kazmina at Harper Center of the Booth School of Business at the University of Chicago (online talk) \n\n\n\nAuthors: Yuliia Kazmina\, Eszter Bokanyi\, Eelke Heemskerk and Frank Takes \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups\, defined by individual attributes such as race\, religion or social class. Instead\, in this work we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are in fact networked phenomena and should thus be studied as such [1\,2]. We present a methodological contribution that moves away from a traditional spatial understanding of segregation\, and instead considers segregation measurement within the direct social network of individuals.  \n\n\n\nThe study is based on Dutch population register data sourced from multiple existing registers that contain information on formal ties of ~17 million residents. Data covers multiple social contexts (layers): kinship\, household\, neighborhood\, school\, and work. With the multilayer network of geospatially embedded formal ties in hand\, we study to what extent social segregation is clustered in social networks\, and how each network layer contributes to it. Specifically\, we measure to what extent people are exposed to individuals of different socio-economic statuses (SES) for each of the social contexts. Moreover\, we look at what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, inversely\, what social contexts play a role in sustaining so-called  “socio-economic bubbles”.  \n\n\n\nTo capture socio-economic segregation patterns on a population scale\, we introduce a concept of “social opportunity structures” that builds upon the Opportunity Structure Theory proposed by Ken Roberts [3]. Individual ego networks we observe in this study are assumed to be a realization of a particular aspect of Roberts’ opportunity structures – they represent the anatomy and composition of social circles within which individuals exist\, evolve\, and are required to make successive choices. We aggregate household-level social opportunity structures in each of the selected contexts to the level of a municipality to measure to what extent households of a certain socio-economic status (captured by the equivalised household income) are\, on average\, exposed to households across all income deciles. In this abstract\, we focus on the municipality of Amsterdam. \n\n\n\nEstimated social opportunity structures for each of the selected contexts are represented by what we call social opportunity matrices\, in which the vertical axis represents analyzed households divided into ten income deciles\, sorted in ascending order. Then\, the horizontal axis indicates income deciles of connected households in the increasing order. Each cell at the intersection of two income deciles displays the share of contacts a household of a certain income bracket (on the vertical axis) shares with the households in the income decile on the horizontal axis. Values are normalized by row. The diagonal elements represent the share of contacts each income decile has within its own income bracket. To capture the overall segregation for a particular context\, we measure the extent of link assortativity [4]  with respect to income.  \n\n\n\nFigure 1 presents social opportunity structures with respect to income for the households in the city of Amsterdam (~460k households) in the kinship\, school\, work\, and neighborhood (both administrative neighborhoods typically containing several hundred to thousands of households as well as the ten closest neighbors) contexts. The estimated social opportunity matrices present a number of interesting findings.  \n\n\n\nFirst\, in Fig. 1a we see that all income brackets are highly exposed to the neighbors that belong to the two lowest income deciles in the context of being in the same administrative neighborhood. Second\, once the context is narrowed down to the subset of the ten closest neighboring households only (Fig. 1b)\, the matrix reveals a significantly different pattern: close neighborhood social context is much more assortative with respect to income\, as evidenced by the assortativity value of  0.12 vs 0.04 in the case of the administrative neighborhood.  \n\n\n\nThird\, the family layer (Fig. 1c) exhibits similar income assortativity pattern\, with a high prevalence of within income bracket connectivity with 25-30% of family members living separately from an observed household belonging to the same income bracket.  \n\n\n\nAlthough the overall assortativity in the school layer is again comparable\, the distribution of the preference for the own income class along income range is significantly dissimilar: the strongest preference to be classmates with children and adolescents that belong to the same socio-economic class is observed in the lowest income decile as well as in the richest 10% of the households. Finally\, the workplaces’ (Fig. 1d) assortativity is relatively high\, however\, we do not observe an apparent prevalence of diagonal elements\, likely due to several very large workplaces being present in the data.  \n\n\n\nConcluding\, we find that the analyzed social contexts are highly dissimilar in terms of socio-economic assortativity. The most assortative layer is the family network. Other layers\, while being less assortative overall\, reveal interesting patterns. Close neighbors and small workplaces exhibit highly assortative mixing patterns with respect to income that limits the exposure to individuals from different socio-economic backgrounds. On the other hand\, school networks display relatively lower income assortativity and provide individuals with diverse social contact opportunities.  \n\n\n\nThe broad implication of the present study is the potential to capture and quantify social segregation patterns on a large scale with the ability to distinguish between different social contexts\, advocating the study of multi-layer administrative data for the purposes of obtaining a more global policy-relevant insight into population-scale social cohesion. \n\n\n\nReferences\n\n\n\nFreeman\, L. C. (1978). Segregation in social networks. Sociological Methods & Research 6 (1978): 411 – 429.Dimaggio\, P.\, & Garip\, F. (2012). Network effects and social inequality. Annual Review of Sociology 38:1 (2012): 93-118.Roberts\, K. (1977). The Social Conditions\, Consequences and Limitations of Careers Guidance. British Journal of Guidance & Counselling 5:1 (1977): 1-9.Newman\, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters Vol. 89 (20): 208701.\n\n\n\nFigure 1. Social opportunity structures of the households in Amsterdam\, each subfigure displaying a different context: \n\n\n\na) administrative neighborhood  (assortativity: 0.035)               \n\n\n\n\n\n\n\nb) close neighbors (assortativity: 0.118) \n\n\n\n\n\n\n\nc) family (assortativity: 0.124) \n\n\n\n\n\n\n\nd) school (assortativity: 0.114)                                                   \n\n\n\n\n\n\n\ne) workplace  (assortativity: 0.123)
URL:https://www.popnet.io/events/capturing-the-social-fabric-population-scale-socio-economic-segregation-patterns/
CATEGORIES:Conference talk
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DTSTART;TZID=Europe/Amsterdam:20220721T190000
DTEND;TZID=Europe/Amsterdam:20220721T200000
DTSTAMP:20260504T040858
CREATED:20220704T090827Z
LAST-MODIFIED:20220704T112430Z
UID:892-1658430000-1658433600@www.popnet.io
SUMMARY:The anatomy of a population-scale social network
DESCRIPTION:Conference poster presentation by Eszter Bokányi at IC2S2 at the Harper Center of the Booth School of Business at the University of Chicago.  \n\n\n\nAuthors: Eszter Bokanyi\, Yuliia Kazmina\, Rachel de Jong\, Frank Takes and Eelke Heemskerk \n\n\n\nThe analysis of large-scale societal networks has recently seen tremendous growth\, in part because of the relative abundance of digital data sources such as online social networks or mobile communication datasets1–3. However\, most of these data sources lack demographic data on users or are uncertain with respect to the representativity of the user sample. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings4. \n\n\n\nWe overcome a number of these drawbacks by presenting a thorough overview of the structure of a 17M node population-scale social network of a European country containing roughly 1.6B edges. This network is derived from highly curated official data sources of the country’s national statistics institute. As such\, it includes every resident registered on a certain day in 2018. In addition\, rich individual-level demographic and socio-economic attributes on the nodes are  available alongside the network structure\, as well as the precise type of each social relationship we observe: family\, household\, work\, school\, or neighbor relationship\, each extracted from country-level register data. Just as a typical (online) social network data may suffer from missing connections\, the studied population-scale social network data may miss informal friendship connections not captured in the formal ties in this network. However\, we know that we have precisely all nodes (people)\, and we know that for the types of connections that we have\, data is very complete\, which is a unique setting in social network analysis research. In this work\, we present first results of how such a high quality population scale social network is markedly different from many of the large scale social networks we typically study. Below\, we in particular do so by revisiting the well-known concept of closure in a population-scale social network context. \n\n\n\nFirst\, we show how the degree distribution of this network is a composition of the degree distributions of the different types of edges. In the overall degree distribution\, we find a characteristic value that is in sharp contrast to the scale-free or other fat-tailed distributions found in online social networks or communication networks5. Second\, we discuss different types of clustering in this multilayer network\, and show how closed or open network structures emerge for people of certain ages. In particular\, we introduce a normalized multilayer clustering coefficient that we call excess closure\, that captures the fraction of triangles in people’s social circles that span across multiple types of relationships.  \n\n\n\nFigure 1 shows how degree and excess closure change with age (a demographic attribute) in the population. Young children have low degrees and very high excess closure since they are only part of family\, neighborhood\, and household structures. Subsequent levels of education paired with working opportunities come with both an increasing median degree\, and decreasing excess closure\, reaching its minimum around the university age. Working years are characterized by a slight increase in closure\, and gradually decreasing degree\, giving place to low degrees and increased closure in retirement years. Finally\, we find that long-range ties that span large distances are very scarce in this network\, only 0.02% of all edges not being part of any triangles\, which is in contrast to findings in online social networks\, and does not promote fast and efficient diffusion processes over this structure. \n\n\n\n\n\n\n\nFigure 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain  age. Shaded areas correspond to the 25th and 75th percentiles for each age year. \n\n\n\nConcluding\, our results show a sharp transition from closed to open network structures as young adults engage in higher levels of education\, and a reverse process as people retire. The findings empirically confirm using large-scale data that individuals have very different resource structures throughout their lives\, which affects their access to opportunities and information6. Our measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets. \n\n\n\nReferences\n\n\n\n1. Eagle\, N.\, Pentland\, A. S. & Lazer\, D. Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. U. S. A. 106\, 15274–15278 (2009). \n\n\n\n2. Park\, P. S.\, Blumenstock\, J. E. & Macy\, M. W. The strength of long-range ties in population-scale social networks. Science 362\, 1410–1413 (2018). \n\n\n\n3. Bailey\, M.\, Cao\, R.\, Kuchler\, T.\, Stroebel\, J. & Wong\, A. Social Connectedness: Measurement\, Determinants\, and Effects. J. Econ. Perspect. 32\, 259–280 (2018). \n\n\n\n4. Lazer\, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595\, 189–196 (2021). \n\n\n\n5. Onnela\, J.-P. et al. Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. 104\, 7332–7336 (2007). \n\n\n\n6. Tóth\, G. et al. Inequality is rising where social network segregation interacts with urban topology. Nat. Commun. 12\, 1143 (2021).
URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-2/
CATEGORIES:Conference talk
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