The role of tech workers in ethnicity- and class-based urban segregation

Large cities are often segregated by both ethnicity and class, and these dimensions frequently overlap because minorities are over-represented in lower-paid occupations. We examine how these patterns shift during the course of digital-transition-driven professionalization by focusing on tech workers, a fast-growing professional group. Higher earnings and broad scope for remote work expand minority tech workers’ residential options relative to other minorities facing tighter housing constraints. What remains unclear is whether their residential sorting is driven primarily by ethnicity or by class. Our empirical evidence comes from Tallinn, the capital of Estonia—a country known for its digital advancement. On the basis of population-wide register data, we find that the residential outcomes of minority tech workers align with class position, paralleled by the increasing spatial isolation of non-tech minorities as upward occupational mobility draws minority professionals out of lower-income co-ethnic areas. These results underscore the need to design urban policy for digitally transforming and professionalizing cities, with explicit attention to class-based segregation risks among minority communities who gain less from new opportunities. Cities are often segregated by class and ethnicity, with work often playing a key role. Focusing on digitally advanced Tallinn, Estonia, this study finds that minority tech workers are less segregated from the majority whereas non-tech workers are more isolated, deepening class-based segregation.

Authors

Institutions

Publication Details

Journal
Nature Cities
Published
2026-04-03
DOI
https://doi.org/10.1038/s44284-026-00420-4
Primary Topic
Urban, Neighborhood, and Segregation Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The role of tech workers in ethnicity- and class-based urban segregation

Kadri Leetmaa, Jānis Zālīte, Kadi Kalm, Tiit Tammaru
Nature Cities
Urban, Neighborhood, and Segregation Studies
article

The role of tech workers in ethnicity- and class-based urban segregation

Kadri Leetmaa, Jānis Zālīte, Kadi Kalm, Tiit Tammaru
article en

Abstract

Large cities are often segregated by both ethnicity and class, and these dimensions frequently overlap because minorities are over-represented in lower-paid occupations. We examine how these patterns shift during the course of digital-transition-driven professionalization by focusing on tech workers, a fast-growing professional group. Higher earnings and broad scope for remote work expand minority tech workers’ residential options relative to other minorities facing tighter housing constraints. What remains unclear is whether their residential sorting is driven primarily by ethnicity or by class. Our empirical evidence comes from Tallinn, the capital of Estonia—a country known for its digital advancement. On the basis of population-wide register data, we find that the residential outcomes of minority tech workers align with class position, paralleled by the increasing spatial isolation of non-tech minorities as upward occupational mobility draws minority professionals out of lower-income co-ethnic areas. These results underscore the need to design urban policy for digitally transforming and professionalizing cities, with explicit attention to class-based segregation risks among minority communities who gain less from new opportunities. Cities are often segregated by class and ethnicity, with work often playing a key role. Focusing on digitally advanced Tallinn, Estonia, this study finds that minority tech workers are less segregated from the majority whereas non-tech workers are more isolated, deepening class-based segregation.

Nature CitiesVol. 3(4)
Estonian Academy of Sciences (EE), University of Tartu (EE)
Eesti Teaduste Akadeemia, Eesti Teadusagentuur
Openalex Percentile: Top 28%
Urban, Neighborhood, and Segregation Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.