The Closing Window? Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network

On October 18, Yuliia Kazmina will present her work on “The Closing Window? Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network” at the Dutch NetSci Symposium, which will take place at Eindhoven University of Technology.

Abstract

This work presents a comprehensive longitudinal exploration of trends of socio- economic segregation and inequalities across a span of a recent decade, aiming to reveal their impact on the prospects for upward mobility.

Utilizing population-scale social network register data on all residents of the Netherlands, the research delves into the shifting patterns of income and wealth distributions, offering an in-depth overview of their longitudinal trends and interplay. We juxtapose the evolving dynamics of income and wealth accumulation against a specific aspect of social network structure: segregation patterns along the socio-economic status dimension. To unravel the drivers behind observed macro-trends of segregation and inequality, we distinguish various relevant subgroups within the population as defined by their socio-economic background and social network position. Through comparative analysis across diverse socio-economic strata, we identify the key social network determinants contributing to individuals’ economic prosperity and explore how these influences vary across the spectrum of income and wealth distribution.

The study leverages register data from the Netherlands covering the country’s whole population and providing information on one’s socio-economic standing as well as comprehensive mapping of social networks. The relational aspect of register data sheds light on formal links such as kinship, neighbors, classmates, colleagues, and household members. Methodologically, the study employs a mix of supervised and unsupervised machine learning to analyze longitudinal patterns of segregation and economic inequalities. We first employ clustering analysis to define common trajectories of economic development expressed through a combination of income change as well as accumulation of wealth and debt. We then relate these trajectories to the socioeconomic network composition of these individuals with a particular focus on income and wealth assortativity as well as average income and wealth distance to alters while controlling for the size of the social network.

In contrast to recent work on social capital by Chetty et al., we contend that the key factor for improving economic performance among the lower end of the prosperity distribution isn’t merely low segregation and high economic connectedness. Rather, it lies in having a significant proportion of more affluent contacts, while ensuring that the prosperity gap is within an optimal range. While having somewhat wealthier contacts can be advantageous, once the prosperity gap becomes too wide, its predictive power may diminish or even have a negative impact. Therefore, we anticipate a concave relationship between the positive prosperity gap with alters and enhanced economic performance,
especially for those at the lower end of the prosperity distribution.

Our contribution lies not only in comprehensively mapping dynamics of segregation vs economic inequalities on the country level but also in providing micro-analysis of the drivers that contribute to the observed macro outcomes. This interdisciplinary approach enables a comprehensive perspective on how individual pathways to economic success
are embedded within a broader structural framework.

References
1. Chetty, R., Jackson, M.O., Kuchler, T. et al. Social capital I: measurement and associations
with economic mobility. Nature 608, 108–121 (2022). https://doi.org/10.1038/s41586-022-
04996-4
2. Chetty, R., Jackson, M.O., Kuchler, T. et al. Social capital II: determinants of economic
connectedness. Nature 608, 122–134 (2022). https://doi.org/10.1038/s41586-022-04997-3