Digital transition and cities: How tech workers reshape ethnic and class-based segregation

Digital transition and cities: How tech workers reshape ethnic and class-based segregation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Digital transition and cities: How tech workers reshape ethnic and class-based segregation Jānis Zālīte, Kadi Kalm, Kadri Leetmaa, Tiit Tammaru This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6321132/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Abstract The digital transition is reshaping cities, transforming both labor and housing markets. While its impact on labor markets is increasingly well documented, its consequences for residential choices and segregation remain poorly understood. New jobs—especially in the tech sector—concentrated into cities, potentially reinforcing the residential clustering of highly skilled workers. At the same time, higher incomes and the flexibility of remote work provide tech workers with greater freedom to choose where to live, both within and beyond urban areas. It is unknown whether the residential decisions of minority tech workers are primarily ethnicity-based, reinforcing clustering and segregation, or class-based, facilitating dispersal and ethnic desegregation. Here we show that minority tech workers increasingly cluster in large cities, where they contribute to ethnic desegregation. However, this trend simultaneously intensifies class-based residential segregation, as the spatial isolation of non-tech minority workers becomes more pronounced. This dual dynamic underscores the need for urban policy responses that address not only ethnic integration but also the deepening class-based inequalities emerging from the digital transition. Social science/Geography Business and commerce/Information systems and information technology Scientific community and society/Geography Social science/Sociology digital transition tech workers class-based segregation ethnic segregation ethnic desegregation Figures Figure 1 Figure 2 Figure 3 MAIN The rapid development and use of information and communication technologies (ICT), often referred to as digital transition, was significantly accelerated by the outbreak of the global coronavirus pandemic in 2020. This transition has profoundly impacted cities, reshaping the organization of space and time—including work and daily activity patterns—driven by the opportunities created by new technologies and the unequal ways different population groups benefit from them 1 . One of the most significant opportunities arising from the digital transition is the expansion of remote working 2 , 3 . While large cities remain the most dynamic labor markets and engines of economic growth, remote work contributes to the decoupling of residence from workplace 4 , enabling individuals to choose housing based more on lifestyle preferences than on proximity to their place of employment. ICT sector or tech workers together with other high-skilled professionals working in offices have gained the most from remote working opportunities, while manual workers employed in factories or in essential services have gained the least 5 – 7 . The residential preferences of high-skilled professionals have played a critical role in rising levels of residential segregation both between occupational and ethnic groups in large cities around the world 8 . Remote work, or more broadly, new non-traditional forms of working (such as teleworking, hybrid working, virtual working) may be performed in different settings, but it mainly takes place from home or nearby co-working spaces 2 , 9 . Both the ability to work remotely along with higher incomes compared to all other occupational groups give tech workers more freedom in their housing choices, allowing them to seek residential locations that best align with their lifestyle preferences (Fig. 1 ). However, we know little about what these preferences are, whether these preferences lean towards city life with its urban amenities 10 , or towards suburban and counter-urban environs with spacious homes and environmental considerations 11 , and how these preferences affect cities and reshape the patterns of residential segregation both at national and urban scales. In this context, the overarching goal of this paper is to offer the first in-depth investigation into whether the residential mobility of minority and majority tech workers reinforces the high levels of segregation common in many large cities, or fosters a shift toward desegregation and the decoupling of ethno-occupational or ‘eth-class’ segregation. Given that remote work enables relocation across greater distances, we analyze these dynamics at both national and urban scales. FIGURE 1 ABOUT HERE On the one hand, existing research highlights that tech workers may exhibit high levels of residential clustering at both the national and urban scales 12 , 13 . On the national scale, technological innovations and highly paying jobs are increasingly concentrated in large “superstar” cities 14 , 15 that offer a growing segment of hyperexpensive upscale housing and many other urban amenities, thus attracting tech workers similar to other higher-income groups 16 . The concentration of tech workers in large cities further elevate the house prices and rents in these cities, making lower-income households seek more affordable housing elsewhere 17 , 18 . As money buys choice on the housing market 19 , and upscale housing and urban amenities are unevenly distributed across metropolitan areas, tech workers may cluster in the most attractive neighborhoods within cities. This residential clustering may also be driven by class-based preferences, where tech workers prioritize networking and residential proximity with others who share a similar professional background 20 , 21 . Opportunities for socializing in amenity-rich neighborhoods allow tech workers to balance professional networking with leisure activities, ensuring their lives are not solely defined by work and income 22 . While remote work offers flexibility, the preference for hybrid work keeps proximity to the workplace an important residential factor 4 , 6 , 23 . Moreover, greater distance from work can limit access to professional networks, reinforcing the appeal of urban locations that offer both career opportunities and a vibrant social environment 24 . Similarly to other high-skilled professionals, this may imply that tech workers contribute to the continued gentrification of inner-city neighborhoods that provide a rich variety of urban amenities, and opportunities for networking and socializing in various professional and leisure time settings, reinforcing their residential clustering and segregation from other occupational groups 8 , 10 . On the other hand, residential clustering is not necessarily the only outcome of the residential mobility of tech workers who can take advantage of the spread of remote working opportunities 3 , 25 . Remote working, even when done a few days a week, combined with a higher degree of financial freedom allows tech workers to broaden their lifestyle choices, balance work and personal life more effectively, and consider a bigger variety of places where to live 22 , 26 , 27 . Tech workers tend to be overrepresented in newly-built houses 28 . As these houses are often built outside the inner cities, tech worker disperse towards suburban areas and beyond, to counter-urban areas, too 29 – 31 . Hence, a combination of remote and on-site work may significantly reshape the daily activity patterns and the geography of housing demand 32 . The housing demand could reflect life course changes, e.g. with young people being lured by inner-city environs with an abundance of urban amenities, and families by environs with spacious homes and an abundance of family-friendly amenities 33 . Thus, socialization and agglomerative forces may drive residential clustering and home searches in large cities for tech workers, while housing and environmental considerations could encourage residential dispersal and home searches in suburban and counter-urban areas, and these preferences may change over the life course. We know less about how the digital transition and the rise of tech workers may reshape the high levels of ethnic residential segregation. Existing research shows that socioeconomic and ethnic segregation are often closely linked in large cities—a phenomenon known as ‘eth-class’ segregation 34 . Hence it is crucial to learn whether the residential preferences of minority and majority populations working in the tech sector are ethnicity-based, reinforcing ‘eth-class’ segregation, or class-based, contributing to its decoupling. Ethnicity is considered one of the key dimensions of inequality in digitally transforming societies 35 , and, consequently, the digital transition may further exacerbate existing patterns of ethnic residential segregation. However, the global talent hunt in the tech sector has led to the emergence of highly paid migrant and minority populations in the ICT sector who have relatively greater freedom in choosing their place of residence due to high demand in the labor market. Their residential decisions thus allow us to learn to what such relative freedom to choose a home shifts residential mobility decisions from ethnicity-based to class-based considerations. RESULTS Our empirical evidence comes from Estonia, a country known for its advanced digitalization and complex majority-minority relations 36 , 37 . Here we study tech workers in managerial and professional positions to capture a group likely to possess the greatest financial freedom in their residential choices. We compare them with different ‘eth-class’ combinations, including the majority tech workers in managerial and professional positions, non-tech workers in similarly high-level occupations, as well as those in middle- and lower-status occupations. The bulk of the Estonian minority population primarily formed between 1944 and 1991, when Estonia was occupied by and part of the Soviet Union. Minorities form approximately one-third of Estonia’s population and about half of the population of its capital, Tallinn, with Russians and other Russian-speaking minorities making up 90% of ethnic minorities in Estonia. Most of them are second or third-generation minorities who arrived before 1991 when Estonia was part of the Soviet Union. Since Estonia joined European Union in 2004, a new wave of migration started, with tech workers being exempt from annual migration quota. Ethnic segregation has been an important feature of Tallinn and other major Estonian cities since the start of large-scale migration, and workplaces, residential neighborhoods, and schools are all segregated along ethnolinguistic lines 37 . Changing nation-wide residential patterns of tech workers in Estonia The number of tech workers in Estonia doubled between the 2011 and 2021 census rounds. The tech sector is also predominantly urban-oriented, with the highest concentration of tech workers living in the capital city Tallinn, followed by its suburbs and the rest of the country. However, there are important differences between the Estonian ethnic majority and Russian ethnic minority tech workers in their national changes of residential distribution, with majority tech workers becoming more dispersed across the country and minority tech workers getting increasingly clustered in the capital (Fig. 2 ). FIGURE 2 ABOUT HERE Next, we analyse changes in dissimilarity index values between ethnic and occupational or ‘eth-class’ groups. We find that ethnic majority and minority tech workers have experienced a five-point increase in their dissimilarity index (Table 1 ), from 34 to 39 (Online Appendix 3). The segregation of ethnic minority tech workers also increased in relation to all non-tech ethnic majority occupational groups (Table 1 ), being most segregated (dissimilarity index value 70) from majority non-tech workers working in lower occupations (Online Appendix 3). Ethnic majority tech workers are consistently less residentially segregated from the ethnic minority than from non-tech majority workers at the national scale, with the lowest dissimilarity index value being 38 with minority non-tech workers working in lower occupations (Online Appendix 3). Table 1 Segregation and desegregation between ethnic and occupational groups across municipalities of Estonia, change of dissimilarity index values between 2011 and 2021. Tech minority High minority Middle minority Low minority Tech majority 5 2 1 -3 High majority 2 0 0 2 Middle majority 3 0 0 1 Low majority 2 -1 -1 0 Tech minority 3 -3 4 High minority 3 1 1 Middle minority -3 1 6 Low minority 4 1 6 Table 1 ABOUT HERE Compositional differences—including family composition, income, or other personal characteristics—between ethnic majority and minority tech workers may influence nationwide residential distribution patterns. To account for these differences, we fit a set of multinomial regression models to control for relevant personal characteristics in determining whether tech workers reside in the capital city, its suburbs, or elsewhere in the country. The results confirm significant ethnic differences in national-scale residential distribution among tech workers. Specifically, minority tech workers have significantly lower odds of living in both the suburbs of the capital city and other regions of the country compared to their majority counterparts (Table 2 , overall model). Among the control variables, having children in the household and belonging to the highest income bracket increases the likelihood of residing in the suburbs of the capital city. This pattern remains consistent when the models are split by ethnicity, indicating that wealthier tech workers with children show the strongest preference for suburban living, regardless of whether they are an ethnic majority or minority group. Table 2 Multinomial regression of nationwide residential distribution of Estonian and Russian tech workers in 2021. Suburbs Rest of Estonia Overall Majority Minority 1 Minority 2 Overall Majority Minority 1 Minority 2 Ethnicity (ref.: majority) Minority 0.30*** 0.19*** Age (ref.: 54 1.32 1.33 1.3 1.55 0.67*** 0.63*** 1.08 1.48 Gender (ref.: female) Male 0.97 0.99 0.8 0.82 1.45*** 1.49*** 1.44** 1.44** Children in household (ref.: no) Yes 2.48*** 2.46*** 2.63*** 2.56*** 1.18*** 1.18*** 1.12 1.09 Education (ref.: non-academic higher or other) Academic higher 0.80*** 0.82*** 0.75 0.82 0.80*** 0.82*** 0.70** 0.78 Monthly income (ref.: <2000€) 2000–2999€ 1.21** 1.25** 1.06 1.04 0.88** 0.93 0.64*** 0.64*** 3000–3999€ 1.15 1.11 1.64** 1.67** 0.61*** 0.68*** 0.26*** 0.27*** 4000–4999€ 1.18 1.26* 1.03 1.08 0.48*** 0.54*** 0.22*** 0.24*** > 4999€ 1.33*** 1.29** 1.79** 1.87** 0.49*** 0.55*** 0.19*** 0.20*** Country of birth (ref.: native-born) Foreign-born 0.59*** 0.50*** N 11415 8931 2484 2484 Nagelkerke R 2 0.16 0.07 0.13 0.14 Log likelihood -1089.55 -706.62 -339.31 -451.66 Chi-Square (df) 1678.08 (24) 594.51 (22) 227.77 (22) 253.21 (24) Ref.: Tallinn Significance levels: *p < 0.10; **p < 0.05; ***p < 0.01 Table 2 ABOUT HERE When comparing tech workers living in the capital city versus those residing in other parts of the country, we find that only the youngest generation—both majority and minority ethnic groups—are more likely to live in the capital city than elsewhere. This suggests that life course dynamics play a crucial role in shaping the residential choices of tech workers, with a clear preference for urban living observed only among the youngest age group. Additionally, while higher income is associated with a greater likelihood of living in the suburbs rather than in the capital, the opposite trend is observed when considering residence in the rest of the country. In other words, increased income is associated with higher odds of living in the suburbs and lower odds of living in the rest of the country compared to living in Tallinn. Finally, native-born minority tech workers have higher odds of living in the suburbs and in the rest of the country compared to the foreign-born tech workers. To conclude, ethnic majority tech workers have become more geographically dispersed across the country, while ethnic minority tech workers have increasingly clustered in the capital city—a trend further intensified by those with a migration background. In the next section, we focus on the capital city to examine the intraurban patterns and geographies of ethnic segregation among the tech workers. Changes in ethnic residential segregation of tech workers in the capital city The findings reveal significant ethnic desegregation among minority tech workers in the capital city, with the dissimilarity index decreasing by 13 points—from 49 in 2011 to 36 in 2021 (Table 3 ; Online Appendix 4). Co-residence in the same neighborhood, or residential desegregation, of minority tech workers also increased with members of the majority population working in higher, middle, and lower occupations, following a clear descending order. Conversely, the residential segregation of minority tech workers increased with co-ethnics working in higher, middle, and lower occupations, following a clear ascending order. This suggests desegregation between minority and majority tech workers, but increased class-based segregation between tech and non-tech workers. More specifically, minorities employed in non-tech occupations—particularly those in lower-tier jobs—become increasingly residentially isolated. Table 3 Segregation and desegregation between ethnic and occupational groups in Tallinn urban region, change of dissimilarity index values between 2011 and 2021. Tech minority High minority Middle minority Low minority Tech majority -13 -3 -2 -3 High majority -13 -1 -2 -2 Middle majority -9 -2 -2 -1 Low majority -7 -1 -1 -1 Tech minority 1 4 6 High minority 1 -1 -1 Middle minority 4 -1 0 Low minority 6 -1 0 Table 3 ABOUT HERE As the next step, we employed a linear regression model to assess the extent of co-residence between minority and majority tech workers in the capital city. While our index-based analysis indicates that ethnic majority and minority tech workers are increasingly co-living in the same neighborhoods, the regression analysis reveals that differences in ethnic residential contexts still persist. More specifically, we find that minority tech workers continue to reside in neighborhoods with a lower share of Estonians compared to majority tech workers (Table 4 , overall model). Regarding the control variables, tech workers with children, those with higher levels of education, and those earning higher incomes are more likely to live in neighborhoods with a higher share of Estonians. Higher education levels and higher incomes are both associated with living in neighborhoods with a greater share of members of the majority population. This suggests that for both ethnic groups, greater income and higher education correlate with a higher presence of ethnic majority neighbors in their residential neighborhoods. Table 4 Linear regression of co-residence with ethnic majority members of Estonian and Russian tech workers living in Tallinn in 2021. Overall Majority Minority 1 Minority 2 Ethnicity (ref.: majority) Minority -13.37*** Age (ref.: 54 1.26 4.23** -6.33* -8.38*** Gender (ref.: female) Male -0.30 -0.01 -1.13 -1.34 Children in household (ref.: no) Yes 1.98*** 3.33*** -0.73 -0.45 Education (ref.: non-academic higher or other) Academic higher 3.33*** 2.83*** 4.32*** 2.88*** Monthly income (ref.: 4999€ 5.65*** 4.14*** 8.57*** 7.53*** Country of birth (ref.: native-born) Foreign-born 6.63*** (Constant) 58.44*** 57.84*** 46.08*** 45.35*** N 6335 4316 2019 2019 Adjusted R 2 0.14 0.04 0.04 0.07 Dependent variable: share of ethnic Estonians in neighborhood of residence (%) Significance levels: *p < 0.10; **p < 0.05; ***p < 0.01 Table 4 ABOUT HERE Age-wise, older minority tech workers tend to reside in neighborhoods with the lowest share of majority neighbors, whereas older majority tech workers are more likely to live in areas with a higher share of majority neighbors. Thus, ethnic desegregation appears to involve primarily the younger and middle-aged tech workers, which may be an effect of differences in residential mobility levels across age groups and in different life course stages. Majority tech workers with children live in neighborhoods with a higher share of Estonians, which also points to the self-segregation into co-ethnic urban environments. The residential pattern of minority tech workers with and without children does not differ, but the differences between native-born and foreign-born minority tech workers yield somewhat unexpected results. While our nationwide analysis indicated that desegregation is more common among native-born tech workers, the city-level analysis presents the opposite pattern, with foreign-born Russian tech workers residing in neighborhoods with a higher share of majority neighbors (Online Appendix 5). As a final step, we aim to identify the geography of segregation by detecting the neighborhoods where majority and minority tech workers co-reside. In 2011, majority tech workers were over-represented in most inner-city neighborhoods with the most upscale housing (Kesklinn, see Online Appendix 1) as well as in attractive detached housing areas in the outer suburbs (Nõmme, located in the southwestern part of the city, colored blue on Fig. 3 ). In contrast, minority tech workers were over-represented in the largest high-rise housing estate with the most affordable housing and highest presence of the minority population (Lasnamäe in the eastern part of the city, coloured yellow). Mixed-ethnic neighborhoods (coloured white), where neither majority nor minority tech workers were dominant, were relatively few in 2011. Likewise, only a few neighborhoods are characterised by the over-representation (coloured violet) of both majority and minority tech workers. In contrast, many outer city neighborhoods are characterised by the under-representation (coloured red) of both majority and minority tech workers. FIGURE 3 ABOUT HERE By 2021, the most significant change in the intra-urban ethnic geography of tech workers was the rise in the number of mixed-ethnic neighborhoods where tech workers from neither ethnic groups were over-represented or under-represented. These neighborhoods are chatacterised with the most expensive housing, being located in the inner-city (Kesklinn), in detached housing neighborhoods in the northeastern (Pirita) and southwestern (Nõmme) parts of the city, as well as in one high-rise housing estate in the inner suburb (Mustamäe), where Tallinn University of Technology is situated. The over-representation of minority tech workers in the more distant eastern neighborhoods switched to under-representation. Although these neighborhoods cover only a small area, they are among the most densely populated in the city and offer the most affordable housing. Minority tech workers reside also near the airport and Technopolis—a hub of tech employers. This indicates that tech workers are clustering into four types of neighborhoods: upscale housing areas in the inner-city, attractive low-rise detached housing areas, and neighborhoods located near tech universities and tech companies. DISCUSSION OF THE MAIN FINDINGS Our first key finding shows that at the national level, segregation between majority and minority tech workers has increased with a clear ethnic gradient. The capital city’s attractiveness as a place of residence is the highest for foreign-born tech workers, gradually decreasing for native-born ethnic minority tech workers and ethnic majority tech workers. As high-skilled tech workers earn the highest incomes and enjoy greater residential choice, such gradient may reflect the degree of knowledge of available nation-wide residential options. The share of majority tech workers living outside the capital city and also its urban regions has been especially impressive, rising from 14 per cent in 2011 to 22 per cent in 2021. While tech jobs concentrate in “supercities” 15 , a sizeable share of majority-group tech workers opt for diverse residential settings—including suburban and counter-urban areas—often due to inherited property or past familiarity with these locations. In line with the spatial assimilation framework 38 , these results may also reflect varying degrees of integration among the minority tech workers, with native-born tech workers being more integrated than foreign-born tech workers. Being more integrated and with greater familiarity and lived experiences with the nation-wide opportunities on the housing market, native-born minority tech workers may have a larger consideration set to choose from 39 , facilitating relocations from the capital to other parts of the country where the majority population is overrepresented. Hence, ‘white flight’ explanations 40 do not fully account for these moves, as the urban exodus of well-integrated minority tech workers may also be driven by considerations related to residential environs, similar to those of the majority population 41 . Although these differences exist within the minority population, our findings still show that the vast majority of both foreign-born and native-born minority tech workers continue to reside in the capital city. As jobs suitable for tech workers remain concentrated in urban centers 7 , 24 and ethnic networks hold value even for well-integrated individuals, these findings support that “supercities” are highly attractive to minority tech workers. As the share of minority populations increases in large cities, the potential for residential dispersal, even with the expansion of remote work, may therefore be more limited than previously expected 4 , 26 , 27 . However, the life course stage does play a role in residential decision-making for minorities similarly to the majority population, as having children in the household increases the preference for suburban environments, while older age is associated with a stronger tendency for counter-urbanization 41 , 42 . Hence, despite their relatively small numbers, future research should further examine the residential trajectories of suburbanizing and counter-urbanizing tech workers, particularly in relation to family dynamics, individual characteristics, and destination environments. Our second key finding shows that within the capital city, minority tech workers are residentially less segregated from the majority population compared to minority non-tech workers, including those in the highest occupational groups, such as managers and top professionals. Ethnic minority tech workers may be motivated to reinforce their social status 20 by applying the additional resources gained in tech jobs and moving neighborhoods with higher presence of the members of the majority population. In this way, ethnic minority tech workers may consider their profession and its associated benefits as a tool for integration 22 . From a spatial perspective, the residential presence of minority tech workers has declined in neighborhoods where ethnic minorities have been traditionally overrepresented and where many of them grew up—particularly in urban fringe areas dominated by high-rise buildings and characterized by the most affordable housing options. Conversely, their presence has increased in inner-city neighborhoods where members of the ethnic majority population are overrepresented, featuring predominantly low-rise up-scale apartment buildings. This shift suggests that the residential preferences of minority tech workers are more class-based than ethnicity-based, especially when compared to other highly paid minority professionals in non-tech occupations. As tech companies are hunting for talent globally, this may increase ethnic tolerance as predicted by the contact theory 43 . This residential preference of the tech workers is one of the important factors behind the inner-city gentrification witnessed in large cities around the world, driving up house-prices there 8 . However, beyond this broad shift towards ethnic desegregation of the tech workers in the capital city, several important nuances emerged from our study as well. More specifically, we find that while minority tech workers seek ethnically more mixed neighborhoods compared to co-ethnics, the opposite is true for majority tech workers who self-select in more homogenous co-ethnic neighborhoods. In other words, the residential preferences of the majority tech workers are more ethnicity-based compared to minority tech workers. These differences remain intact after controlling for relevant individual characteristics, including life course stage and income. These findings align with earlier studies on ethnic residential segregation, which often highlight that the residential sorting of the majority population is the primary driver of segregation 44 , and majority tech workers are no different in this regard. Our findings further indicate that foreign-born tech workers tend to reside in neighborhoods with a higher share of ethnic majority residents than native-born minority tech workers. It may be that native-born minority tech workers have a stronger attachment to local co-ethnic communities in which they have grown up. For example, previous research has demonstrated a strong intergenerational transmission of residential outcomes of the native-born minorities 37 . The ethnic neighborhoods of large cities are often well-equipped with ethnic infrastructure—such as schools, churches, and cultural institutions—that foster vibrant ethnic communities with which native-born minorities tend to identify. Newly arrived tech workers, by contrast, belong to the pool of ‘global talent’ seeking professional networks rather than connections to local ethnic communities. Like in many countries around the world, Estonia’s migration policies are favorable towards tech workers 45 . Employer recruitment is therefore based less on ethnic networks and more on professional skills, while newcomers seek quality of life by moving to urban neighborhoods with greater amenities and a higher share of the majority population 46 , 47 . This serves as residential integration from the beginning rather than a slow process of spatial assimilation related to improved incomes and cultural adaptation of the native-born minorities, shaped by a shift from ethnic to professional considerations and networks in residential decision-making 20 , 21 . Taken together, the residential preferences of foreign-born tech workers tend to be more class-based, while the residential preferences of native-born minorities are stronger ethnicity-based. Our third key finding reveals that while minority tech workers in the capital city are less segregated from the majority population, facilitating city-level ethnic desegregation, they simultaneously become more segregated from co-ethnic non-tech workers, reinforcing class-based residential segregation. In other words, while existing research reveals a strong pattern of ‘eth-class’ segregation as ethnic and class-based segregation are closely related 34 , we find that the residential mobility of the tech workers contributes to the decoupling of these segregation patterns within the urban areas. However, the unintended outcome of this process is the residualization of segregation as minorities working in non-tech sector get residentially more isolated. In many large cities, the sequence seems to evolve as follows. Firstly, members of the majority population leave the most affordable housing areas. Secondly, our study of the tech workers shows that this may be followed by the members of the minority population earning higher incomes as predicted by the spatial assimilation framework 48 . This would lead to the triple disadvantage as ethnic minorities with low incomes get isolated in lower-cost neighborhoods, thus amplifying one of the core segregation-related concerns in major cities 49 , 50 . Thus, digital transition is reshaping intra-urban segregation patterns. Minorities who gain from the digital transition—those earning higher-incomes and are recent arrival attracted by talent hunt of the tech sector—become residentially more integrated. Minorities who gain less from the digital transition process stay put, reproduce existing patterns of residential segregation also across generations 37 and, as consequence, get residentially increasingly isolated. This study has important societal and policy implications. The growth of the tech sector can offer new opportunities for ethnic desegregation, particularly among high-skilled, mobile populations with greater freedom of residential choice. However, urban policy must also address the growing spatial isolation of native non-tech minority workers—especially those in lower-status occupations who remain closely tied to local ethnic networks. This dual dynamic highlights the need for comprehensive policy responses that promote ethnic inclusion while also tackling emerging class-based divides that exclude those lacking access to high-paying jobs, remote work, or flexible housing options. Crucially, preventing residential segregation from being reproduced across generations—through school segregation and unequal future opportunities in the labor and housing markets—should be a policy priority 37 , 39 , 51 . Supporting the rise of the tech sector simultaneously addressing both ethnic and class-based segregation is essential for creating a more equitable, inclusive, and socially cohesive digital future in cities. CONCLUSIONS This research contributes to the understanding of how residential segregation patterns evolve alongside the digital transition. We focused on tech workers on professional and managerial positions as they represent a minority group with greater financial freedom in housing choices, and we aimed to determine whether their decisions are more strongly shaped by ethnicity-based or class-based considerations. We find, firstly, that residential patterns among tech workers indicate growing levels of ethnic segregation at the national level, while at the urban scale, we detect a trend towards desegregation. Secondly, the ethnic desegregation of tech workers in cities is accompanied by increasing class-based segregation, leading to the decoupling of ‘eth-class’ segregation among minority tech workers. This process simultaneously elevates the residential isolation of non-tech minority workers, who remain concentrated in high-density, low-cost neighborhoods on the urban fringe. Thirdly, these changes in residential segregation remain robust even after controlling for key background factors, such as income and life course stage. Overall, our findings highlight that while the digital transition may contribute to ethnic integration in urban areas, it also reinforces nationwide ethnic segregation and intensifies spatial divisions along class lines in cities. DATA AND METHODS We employed individual-level Estonian Population and Housing Censuses from 2011 and 2021 in our study. Based on International Standard Classification of Occupations ISCO-08, we classified the major occupations 1 to 9 into high (1–2), middle (3–4) or low (5–9) occupational status group. We then distinguished between tech and non-tech workers within the high occupation category. The occupational group of main interest for this study are tech sector workers, who belong to high occupational group (ISCO 1–2) in the field of “programming, consultancy and related activities”, which is code 62 of the statistical classification of economic activities/ nomenclature statistique des activités économiques dans la Communauté européenne or NACE. Hence, our study includes four occupational groups, including high-skilled tech workers, other high occupations, medium and low occupations (whereas non-high-skilled tech workers belong to the ‘low occupations’ group in this analysis). We focused on residential segregation between eight different ‘eth-class’ groups that include four occupations and two ethnicities, Estonians (ethnic majority) and Russians (ethnic minority). The ethnic groups are based on self-defined ethnicity. Russians are the biggest minority group, accounting for about 80% of all minorities living in Estonia. We excluded other ethnicities from the study since they are too small for a detailed neighborhood-level study of segregation. We started our analysis by detecting nationwide changes in the residential distribution of tech workers. As most tech workers live in major cities, we divide Estonia into Tallinn, Tallinn suburbs, and the rest of Estonia. We proceeded with the analysis of ethnic segregation of the tech workers in the Tallinn urban region (Online Appendix 1). The smallest spatial units include 44 neighborhoods ( kant ) in the suburbs and 230 neighborhoods ( asum ) in Tallinn. For ease of following the presentation of the findings, we provided also a map with the eight districts and three residential zones of Tallinn that include the inner city, inner suburbs with high-rise housing estates, and outer suburbs with mostly low-rise housing. The high-rise housing estates include Väike-Õismäe in the Haabersti district, and Lasnamäe and Mustamäe districts. We calculated dissimilarity indices for Estonian municipalities and for Tallinn urban region using the neighborhood spatial units across various combinations of occupations and ethnicities. Our focus was on the change in dissimilarity indices between 2011 and 2021 to identify which ethno-occupational or ‘eth-class’ groups have become more segregated and which have become less segregated. Our focus was on comparing Estonian majority and Russian minority tech workers with other ‘eth-class’ groups. Following Marcińczak et al. 52 , we interpreted dissimilarity index values of less than 20 as low, 20–39 as medium, and greater than 40 as high levels of segregation. Next, we identified the neighborhood-level geographies of segregation for minority and majority tech and non-tech workers using location quotients (LQ). Following Brown and Chung 53 , we interpreted LQ values greater than 1.2 as indicating a high residential concentration of the given group, and LQ values less than 0.85 as indicating a low residential concentration. We classified each neighborhood into one of five categories by combining the high and low concentrations of majority and minority residents (see Online Appendix 2). In our calculations, we excluded neighborhoods with fewer than 50 employed residents. To calculate dissimilarity indices and location quotients, we used the Geo-Segregation Analyzer 54 , and for mapping, we used QGIS software. Finally, we fitted a set of multinomial and linear regression models to provide deeper insights into both nationwide and intra-urban patterns of residential segregation among tech workers. We began by fitting multinomial models to explore the nationwide differences in the distribution of Estonian and Russian tech workers. The model takes the following form: P (Y = j) denotes the three outcome categories of living in Tallinn (1, reference category), suburbs of Tallinn (2) and in the rest of Estonia (3); ß0j is the intercept for category j ; ß1j , …, ßkj are the coefficients for the predictors X1 , …, Xk ​, associated with category j , and k is the number of variables in each model. Model 1 covers both majority and minority tech workers and includes three categories of variables. The first is our key variable of interest, which is ethnicity or being an Estonian or Russian based on the ethnic self-identification of people. The second group are demographic variables to control for different life course and family-related characteristics, including age, gender, and presence of under-aged children in the family. The third group are socio-economic variables that reflect education and income. The income variable was sourced from Statistics Estonia and is based on individuals’ yearly income from all sources, divided by 12 to represent the average monthly income. We then splitted models by ethnicity to learn whether the demographic and socio-economic variables act in a similar way among members of the majority and minority populations. Finally, we ran an additional Model 4 for minorities to account for the role of personal-level integration, by adding country of birth variable. We proceeded by fitting a set of linear regression models to clarify the extent of ethnic co-residence members in the same neighborhoods in the capital city urban region among majority and minority tech workers. The model can be formalised as follows: y is the dependent variable that measures the share of Estonians in the neighborhood of residence to learn whether Estonian and Russian tech workers are equally likely or not to live in the neighborhoods with a high share of the majority population. β0 is the intercept, ß1 , …, ßk are the coefficients for the predictors X1 , …, Xk ​ and k is the number of variables in each model; and ε is the error term. Model 1 includes both Estonian and Russian tech workers and incorporates three groups of variables. The first group captures ethnicity. The second consists of demographic variables to examine how different life course and family-related characteristics—age, gender, and the presence of underage children in the family—are associated with co-residence with ethnic majority members. The third group includes socio-economic variables, which help to understand the role of education and income in living in the same neighborhoods as members of the ethnic majority population. We then splitted the model again by ethnicity to learn what is the probability of living together with Estonians among tech workers of each ethnicity. Finally, we ran an additional Model 4 for minorities to account for the role of personal-level integration, using country of birth variable. Declarations DATA AVAILABILITY The individual-level census and income data that support the findings of this study are not publicly available due to confidentiality agreements with the data provider – Statistics Estonia. Access to these data is limited to authorized personnel and cannot be shared outside the approved research team. For further inquiries, please contact the corresponding author. ETHICAL COMPLIANCE This research complies with all relevant ethical regulations. Approval for conducting studies involving human research participants and using the census variable of ethnicity under the framework of the project “Living segregated lives: Exploring changes in spatial inequalities in digitally transforming societies” was granted by the University of Tartu Research Ethics Committee (387/T-35). ACKNOWLEDGEMENTS We would like to thank Statistics Estonia for providing the Population and Housing Census datasets. This work was supported by the Estonian Research Council [PRG1996 “Living segregated lives: Exploring changes in spatial inequalities in digitally transforming societies” and PRG1919 “Rethinking smartification from the margins: Co-creating Smart Rurality with and for an Aging Population”], the Estonian Ministry of Education and Science [Centre of Excellence in Energy Efficiency, Centre of Excellence for Well-Being Sciences, and the Infotechnological Mobility Observatory (https://www.imo.ut.ee/en)], and Estonian Academy of Sciences [Research Professorship of Tiit Tammaru]. References Małkowska, A., Urbaniec, M. & Kosała, M. The impact of digital transformation on European countries: insights from a comparative analysis. Econ. Q. 16 , 325–355 (2021). Leonardi, P.M., Parker, S.H. & Shen, R. How remote work changes the world of work. Annu. Rev. Organ. Psychol. Organ. Behav. 11 , 193–219 (2024). Milasi, S., González-Vázquez, I. & Fernández-Macías, E. 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Boterman, W.R., Manting, D. & Musterd, S. Understanding the social geographies of urban regions through the socio-economic and cultural dimension of class. Popul. Space Place 24 , e2130 (2018). Boterman, W.R. & Bontje, M.A. ‘The’ creative class does not exist: contrasting the residential preferences of creative and technical workers in Amsterdam and Eindhoven. In: Musterd, S., Bontje, M.A. & Rouwendal, J. (eds) Skills and Cities: Implications of Location Preferences of Highly Educated Workers for Spatial Development of Metropolitan Areas , 63–85 (Routledge, 2016). Musterd, S. Segregation, urban space and the resurgent city. Urban Stud. 43 , 1325–1340 (2006). van Oort, F., Weterings, A. & Verlinde, H. Residential amenities of knowledge workers and the location of ICT firms in the Netherlands. Tijdschr. Econ. Soc. Geogr. 94 , 516–523 (2003). Ahrend, R. et al. Expanding the Doughnut? How the Geography of Housing Demand Has Changed since the Rise of Remote Work with COVID-19. OECD (2023). Available at: https://www.oecd.org/en/publications/expanding-the-doughnut-how-the-geography-of-housing-demand-has-changed-since-the-rise-of-remote-work-with-covid-19_cf591216-en.html. Rossi, P. H. Why Families Move: A Study in the Social Psychology of Urban Residential Mobility . (The Free Press, Glencoe, 1955). Andersson, R. & Kährik, A. Widening gaps: segregation dynamics during two decades of economic and institutional change in Stockholm. In: Housing Estates in the Baltic Countries: The Legacy of Central Planning in Estonia, Latvia and Lithuania (2015). Available at: https://www.taylorfrancis.com/chapters/edit/10.4324/9781315758879-13 Robinson, L. et al. Digital inequalities and why they matter. Inf. Commun. Soc. 18 , 569–582 (2015). Espinosa, V. I. & Pino, A. E-Government as a Development Strategy: The Case of Estonia. Int. J. Public Adm. 48 , 86–99 (2025). Kalm, K., Leonard Knapp, D., Kährik, A., Leetmaa, K. & Tammaru, T. Minorities moving out from minority-rich neighbourhoods: does school ethnic context matter in inter-generational residential desegregation? Eur. Sociol. Rev. 40 , 208–225 (2024). Alba, R.D. & Logan, J.R. Variations on two themes: racial and ethnic patterns in the attainment of suburban residence. Demography 28 , 431–453 (1991). Krysan, M. & Crowder, K. Cycle of Segregation: Social Processes and Residential Stratification (Russell Sage Foundation, 2017). Available at: http://www.jstor.org/stable/10.7758/9781610448697 Frey, W.H. Central city white flight: racial and nonracial causes. Am. Sociol. Rev. 44 , 425 (1979). Geyer, H.S. & Kontuly, T. A theoretical foundation for the concept of differential urbanization. Int. Reg. Sci. Rev. 15 , 157–177 (1993) Mulder, C.H. & Hooimeijer, P. Residential relocations in the life course. In: Wissen, L.J.G. & Dykstra, P.A. (eds) Population Issues , 159–186 (Springer Netherlands, 1999). Available at: http://link.springer.com/10.1007/978-94-011-4389-9_6 Pettigrew, T.F., Tropp, L.R., Wagner, U. & Christ, O. Recent advances in intergroup contact theory. Int. J. Intercult. Relat. 35 , 271–280 (2011). Leetmaa, K., Tammaru, T. & Hess, D.B. Preferences toward neighbor ethnicity and affluence: evidence from an inherited dual ethnic context in post-Soviet Tartu, Estonia. Ann. Assoc. Am. Geogr. 105 , 162–182 (2015). European Migration Network. Annual Report 2022 on Migration and Asylum in Estonia. National Report (Part 2). (2023). Jansen, S.J.T. Urban, suburban or rural? Understanding preferences for the residential environment. J. Urbanism 13 , 213–235 (2020). Ragnedda, M. The Third Digital Divide: A Weberian Approach to Digital Inequalities . (Routledge, 2017). Available at: https://www.taylorfrancis.com/books/9781317064336 Massey, D.S. & Denton, N.A. Spatial assimilation as a socioeconomic outcome. Am. Sociol. Rev. 50 , 94 (1985). Goel, N. Residential segregation and inequality: considering barriers to choice in Toronto. Can. Geogr. 67 , 380–393 (2023). Hess, D.B., Tammaru, T. & van Ham, M. (eds) Housing Estates in Europe: Poverty, Ethnic Segregation and Policy Challenges . (Springer, 2018). Available at: http://link.springer.com/10.1007/978-3-319-92813-5 Tammaru, T., Knapp, D., Silm, S., van Ham, M. & Witlox, F. Spatial underpinnings of social inequalities: a vicious circles of segregation approach. Soc. Inclusion 9 , 65–76 (2021). Marcińczak, S. et al. Patterns of Socioeconomic Segregation in the Capital Cities of Fast-Track Reforming Postsocialist Countries. Ann. Am. Assoc. Geogr. 105 , 183–202 (2015). Brown, L.A. & Chung, S. Spatial segregation, segregation indices and the geographical perspective. Popul. Space Place 12 , 125–143 (2006). Apparicio, P., Fournier, É. & Apparicio, D. Geo-Segregation Analyzer: a multiplatform application (version 1.1). Montreal: Spatial Analysis and Regional Economics Laboratory (2013). Additional Declarations There is NO Competing Interest. Supplementary Files EXTENDEDDATA.docx Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6321132","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447731162,"identity":"b72e31f9-c824-4470-b630-6f9dd7240688","order_by":0,"name":"Jānis Zālīte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie2RoQ7CMBCGr7mkMw2zW0i2V+hShVj2KiPYgSSoZWqYJbN7nDWzgMYBwU4MtwQErUNR5hD9xOXEffn/pgAWyx9CCoBWLzNUY5ikUEQgDfAJcRQQkP2i4P4o5Qh5SB1X3uPyBa5TGIpVm7RjQKMSEcW65OBXrUEpMt4BMKIUOtcKP6cGpe65KuYlSnGeC6Ukl6tBaTLeqlcvdQoSneJ9N5TS847xdKUU4VcnwbyDoVhUZ+Ix7vK4duVtGLdB4O5bg1Lo+fEbzFALIDReWCwWi+UNABI3NH4R/SgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8839-3771","institution":"University of Tartu","correspondingAuthor":true,"prefix":"","firstName":"Jānis","middleName":"","lastName":"Zālīte","suffix":""},{"id":447731163,"identity":"ce63f3cc-6d1b-46a6-8ac3-0a05f9c3a914","order_by":1,"name":"Kadi Kalm","email":"","orcid":"","institution":"University of 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13:50:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6321132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6321132/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-026-00420-4","type":"published","date":"2026-04-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81520401,"identity":"5a088112-33fd-402a-ae9b-40aa87d7b294","added_by":"auto","created_at":"2025-04-28 08:01:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85217,"visible":true,"origin":"","legend":"\u003cp\u003eA conceptual framework for understanding residential segregation of minority and majority tech workers.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6321132/v1/4de253958b5c245e117467df.png"},{"id":81520403,"identity":"b982aa9d-a322-4d95-b33f-72915a43f537","added_by":"auto","created_at":"2025-04-28 08:01:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61619,"visible":true,"origin":"","legend":"\u003cp\u003eNation-wide changes in the residential distribution of Estonian majority and Russian minority tech workers.\u003c/p\u003e\n\u003cp\u003e(a) Change in the number of tech workers, 2011–2021, 2011=100%\u003c/p\u003e\n\u003cp\u003e(b) Nationwide distribution of tech workers across different regions (%), 2021\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6321132/v1/d595cd951373c1381fc16fe3.png"},{"id":81522026,"identity":"6b99b39e-5453-4600-8d8e-eb8fa6c29618","added_by":"auto","created_at":"2025-04-28 08:09:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":473674,"visible":true,"origin":"","legend":"\u003cp\u003eNeighborhoods where Estonian majority and Russian minority tech workers are over-represented and under-represented, 2011 and 2021\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6321132/v1/9ecbb45b7863d155b7bdb3b0.png"},{"id":106138061,"identity":"2bdf89b6-c6fc-42b1-89a7-439290bfbf31","added_by":"auto","created_at":"2026-04-04 07:06:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1471221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6321132/v1/d350fb4e-4a27-4334-8f27-dc15f7494e84.pdf"},{"id":81520405,"identity":"e674daef-7510-490f-9ca8-19582e6d4e2f","added_by":"auto","created_at":"2025-04-28 08:01:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":290497,"visible":true,"origin":"","legend":"","description":"","filename":"EXTENDEDDATA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6321132/v1/e2cc0228e5778ce8249cd454.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Digital transition and cities: How tech workers reshape ethnic and class-based segregation","fulltext":[{"header":"MAIN","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003cp\u003eThe rapid development and use of information and communication technologies (ICT), often referred to as digital transition, was significantly accelerated by the outbreak of the global coronavirus pandemic in 2020. This transition has profoundly impacted cities, reshaping the organization of space and time\u0026mdash;including work and daily activity patterns\u0026mdash;driven by the opportunities created by new technologies and the unequal ways different population groups benefit from them\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. One of the most significant opportunities arising from the digital transition is the expansion of remote working\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. While large cities remain the most dynamic labor markets and engines of economic growth, remote work contributes to the decoupling of residence from workplace\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, enabling individuals to choose housing based more on lifestyle preferences than on proximity to their place of employment. ICT sector or tech workers together with other high-skilled professionals working in offices have gained the most from remote working opportunities, while manual workers employed in factories or in essential services have gained the least\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe residential preferences of high-skilled professionals have played a critical role in rising levels of residential segregation both between occupational and ethnic groups in large cities around the world\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Remote work, or more broadly, new non-traditional forms of working (such as teleworking, hybrid working, virtual working) may be performed in different settings, but it mainly takes place from home or nearby co-working spaces\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Both the ability to work remotely along with higher incomes compared to all other occupational groups give tech workers more freedom in their housing choices, allowing them to seek residential locations that best align with their lifestyle preferences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, we know little about what these preferences are, whether these preferences lean towards city life with its urban amenities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, or towards suburban and counter-urban environs with spacious homes and environmental considerations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and how these preferences affect cities and reshape the patterns of residential segregation both at national and urban scales. In this context, the overarching goal of this paper is to offer the first in-depth investigation into whether the residential mobility of minority and majority tech workers reinforces the high levels of segregation common in many large cities, or fosters a shift toward desegregation and the decoupling of ethno-occupational or \u0026lsquo;eth-class\u0026rsquo; segregation. Given that remote work enables relocation across greater distances, we analyze these dynamics at both national and urban scales.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eOn the one hand, existing research highlights that tech workers may exhibit high levels of residential clustering at both the national and urban scales\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. On the national scale, technological innovations and highly paying jobs are increasingly concentrated in large \u0026ldquo;superstar\u0026rdquo; cities\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e that offer a growing segment of hyperexpensive upscale housing and many other urban amenities, thus attracting tech workers similar to other higher-income groups\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The concentration of tech workers in large cities further elevate the house prices and rents in these cities, making lower-income households seek more affordable housing elsewhere\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. As money buys choice on the housing market\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and upscale housing and urban amenities are unevenly distributed across metropolitan areas, tech workers may cluster in the most attractive neighborhoods within cities. This residential clustering may also be driven by class-based preferences, where tech workers prioritize networking and residential proximity with others who share a similar professional background\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Opportunities for socializing in amenity-rich neighborhoods allow tech workers to balance professional networking with leisure activities, ensuring their lives are not solely defined by work and income\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While remote work offers flexibility, the preference for hybrid work keeps proximity to the workplace an important residential factor\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moreover, greater distance from work can limit access to professional networks, reinforcing the appeal of urban locations that offer both career opportunities and a vibrant social environment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Similarly to other high-skilled professionals, this may imply that tech workers contribute to the continued gentrification of inner-city neighborhoods that provide a rich variety of urban amenities, and opportunities for networking and socializing in various professional and leisure time settings, reinforcing their residential clustering and segregation from other occupational groups\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, residential clustering is not necessarily the only outcome of the residential mobility of tech workers who can take advantage of the spread of remote working opportunities\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Remote working, even when done a few days a week, combined with a higher degree of financial freedom allows tech workers to broaden their lifestyle choices, balance work and personal life more effectively, and consider a bigger variety of places where to live\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Tech workers tend to be overrepresented in newly-built houses\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. As these houses are often built outside the inner cities, tech worker disperse towards suburban areas and beyond, to counter-urban areas, too\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Hence, a combination of remote and on-site work may significantly reshape the daily activity patterns and the geography of housing demand\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The housing demand could reflect life course changes, e.g. with young people being lured by inner-city environs with an abundance of urban amenities, and families by environs with spacious homes and an abundance of family-friendly amenities\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Thus, socialization and agglomerative forces may drive residential clustering and home searches in large cities for tech workers, while housing and environmental considerations could encourage residential dispersal and home searches in suburban and counter-urban areas, and these preferences may change over the life course.\u003c/p\u003e \u003cp\u003eWe know less about how the digital transition and the rise of tech workers may reshape the high levels of ethnic residential segregation. Existing research shows that socioeconomic and ethnic segregation are often closely linked in large cities\u0026mdash;a phenomenon known as \u0026lsquo;eth-class\u0026rsquo; segregation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Hence it is crucial to learn whether the residential preferences of minority and majority populations working in the tech sector are ethnicity-based, reinforcing \u0026lsquo;eth-class\u0026rsquo; segregation, or class-based, contributing to its decoupling. Ethnicity is considered one of the key dimensions of inequality in digitally transforming societies\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and, consequently, the digital transition may further exacerbate existing patterns of ethnic residential segregation. However, the global talent hunt in the tech sector has led to the emergence of highly paid migrant and minority populations in the ICT sector who have relatively greater freedom in choosing their place of residence due to high demand in the labor market. Their residential decisions thus allow us to learn to what such relative freedom to choose a home shifts residential mobility decisions from ethnicity-based to class-based considerations.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOur empirical evidence comes from Estonia, a country known for its advanced digitalization and complex majority-minority relations\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Here we study tech workers in managerial and professional positions to capture a group likely to possess the greatest financial freedom in their residential choices. We compare them with different \u0026lsquo;eth-class\u0026rsquo; combinations, including the majority tech workers in managerial and professional positions, non-tech workers in similarly high-level occupations, as well as those in middle- and lower-status occupations. The bulk of the Estonian minority population primarily formed between 1944 and 1991, when Estonia was occupied by and part of the Soviet Union. Minorities form approximately one-third of Estonia\u0026rsquo;s population and about half of the population of its capital, Tallinn, with Russians and other Russian-speaking minorities making up 90% of ethnic minorities in Estonia. Most of them are second or third-generation minorities who arrived before 1991 when Estonia was part of the Soviet Union. Since Estonia joined European Union in 2004, a new wave of migration started, with tech workers being exempt from annual migration quota. Ethnic segregation has been an important feature of Tallinn and other major Estonian cities since the start of large-scale migration, and workplaces, residential neighborhoods, and schools are all segregated along ethnolinguistic lines\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eChanging nation-wide residential patterns of tech workers in Estonia\u003c/h3\u003e\n\u003cp\u003eThe number of tech workers in Estonia doubled between the 2011 and 2021 census rounds. The tech sector is also predominantly urban-oriented, with the highest concentration of tech workers living in the capital city Tallinn, followed by its suburbs and the rest of the country. However, there are important differences between the Estonian ethnic majority and Russian ethnic minority tech workers in their national changes of residential distribution, with majority tech workers becoming more dispersed across the country and minority tech workers getting increasingly clustered in the capital (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eNext, we analyse changes in dissimilarity index values between ethnic and occupational or \u0026lsquo;eth-class\u0026rsquo; groups. We find that ethnic majority and minority tech workers have experienced a five-point increase in their dissimilarity index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), from 34 to 39 (Online Appendix 3). The segregation of ethnic minority tech workers also increased in relation to all non-tech ethnic majority occupational groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), being most segregated (dissimilarity index value 70) from majority non-tech workers working in lower occupations (Online Appendix 3). Ethnic majority tech workers are consistently less residentially segregated from the ethnic minority than from non-tech majority workers at the national scale, with the lowest dissimilarity index value being 38 with minority non-tech workers working in lower occupations (Online Appendix 3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSegregation and desegregation between ethnic and occupational groups across municipalities of Estonia, change of dissimilarity index values between 2011 and 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTech majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTech minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eCompositional differences\u0026mdash;including family composition, income, or other personal characteristics\u0026mdash;between ethnic majority and minority tech workers may influence nationwide residential distribution patterns. To account for these differences, we fit a set of multinomial regression models to control for relevant personal characteristics in determining whether tech workers reside in the capital city, its suburbs, or elsewhere in the country. The results confirm significant ethnic differences in national-scale residential distribution among tech workers. Specifically, minority tech workers have significantly lower odds of living in both the suburbs of the capital city and other regions of the country compared to their majority counterparts (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, overall model). Among the control variables, having children in the household and belonging to the highest income bracket increases the likelihood of residing in the suburbs of the capital city. This pattern remains consistent when the models are split by ethnicity, indicating that wealthier tech workers with children show the strongest preference for suburban living, regardless of whether they are an ethnic majority or minority group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial regression of nationwide residential distribution of Estonian and Russian tech workers in 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSuburbs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eRest of Estonia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinority 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinority 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMajority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMinority 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMinority 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (ref.: majority)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (ref.:\u0026lt;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.74***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.56*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.67***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (ref.: female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren in household (ref.: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (ref.: non-academic higher or other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income (ref.: \u0026lt;2000\u0026euro;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.64***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;3999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000\u0026ndash;4999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of birth (ref.: native-born)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNagelkerke R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1089.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-706.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-339.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-451.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-Square (df)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1678.08 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e594.51 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227.77 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253.21 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRef.: Tallinn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eSignificance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eWhen comparing tech workers living in the capital city versus those residing in other parts of the country, we find that only the youngest generation\u0026mdash;both majority and minority ethnic groups\u0026mdash;are more likely to live in the capital city than elsewhere. This suggests that life course dynamics play a crucial role in shaping the residential choices of tech workers, with a clear preference for urban living observed only among the youngest age group. Additionally, while higher income is associated with a greater likelihood of living in the suburbs rather than in the capital, the opposite trend is observed when considering residence in the rest of the country. In other words, increased income is associated with higher odds of living in the suburbs and lower odds of living in the rest of the country compared to living in Tallinn. Finally, native-born minority tech workers have higher odds of living in the suburbs and in the rest of the country compared to the foreign-born tech workers.\u003c/p\u003e \u003cp\u003eTo conclude, ethnic majority tech workers have become more geographically dispersed across the country, while ethnic minority tech workers have increasingly clustered in the capital city\u0026mdash;a trend further intensified by those with a migration background. In the next section, we focus on the capital city to examine the intraurban patterns and geographies of ethnic segregation among the tech workers.\u003c/p\u003e\n\u003ch3\u003eChanges in ethnic residential segregation of tech workers in the capital city\u003c/h3\u003e\n\u003cp\u003eThe findings reveal significant ethnic desegregation among minority tech workers in the capital city, with the dissimilarity index decreasing by 13 points\u0026mdash;from 49 in 2011 to 36 in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Online Appendix 4). Co-residence in the same neighborhood, or residential desegregation, of minority tech workers also increased with members of the majority population working in higher, middle, and lower occupations, following a clear descending order. Conversely, the residential segregation of minority tech workers increased with co-ethnics working in higher, middle, and lower occupations, following a clear ascending order. This suggests desegregation between minority and majority tech workers, but increased class-based segregation between tech and non-tech workers. More specifically, minorities employed in non-tech occupations\u0026mdash;particularly those in lower-tier jobs\u0026mdash;become increasingly residentially isolated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSegregation and desegregation between ethnic and occupational groups in Tallinn urban region, change of dissimilarity index values between 2011 and 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow minority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTech majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow majority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTech minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eAs the next step, we employed a linear regression model to assess the extent of co-residence between minority and majority tech workers in the capital city. While our index-based analysis indicates that ethnic majority and minority tech workers are increasingly co-living in the same neighborhoods, the regression analysis reveals that differences in ethnic residential contexts still persist. More specifically, we find that minority tech workers continue to reside in neighborhoods with a lower share of Estonians compared to majority tech workers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, overall model). Regarding the control variables, tech workers with children, those with higher levels of education, and those earning higher incomes are more likely to live in neighborhoods with a higher share of Estonians. Higher education levels and higher incomes are both associated with living in neighborhoods with a greater share of members of the majority population. This suggests that for both ethnic groups, greater income and higher education correlate with a higher presence of ethnic majority neighbors in their residential neighborhoods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression of co-residence with ethnic majority members of Estonian and Russian tech workers living in Tallinn in 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinority 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinority 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (ref.: majority)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (ref.:\u0026lt;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.38***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (ref.: female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren in household (ref.: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (ref.: non-academic higher or other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income (ref.: \u0026lt;2000\u0026euro;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;3999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000\u0026ndash;4999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.73***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4999\u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.57***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.53***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of birth (ref.: native-born)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.63***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.84***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.35***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDependent variable: share of ethnic Estonians in neighborhood of residence (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSignificance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eAge-wise, older minority tech workers tend to reside in neighborhoods with the lowest share of majority neighbors, whereas older majority tech workers are more likely to live in areas with a higher share of majority neighbors. Thus, ethnic desegregation appears to involve primarily the younger and middle-aged tech workers, which may be an effect of differences in residential mobility levels across age groups and in different life course stages. Majority tech workers with children live in neighborhoods with a higher share of Estonians, which also points to the self-segregation into co-ethnic urban environments. The residential pattern of minority tech workers with and without children does not differ, but the differences between native-born and foreign-born minority tech workers yield somewhat unexpected results. While our nationwide analysis indicated that desegregation is more common among native-born tech workers, the city-level analysis presents the opposite pattern, with foreign-born Russian tech workers residing in neighborhoods with a higher share of majority neighbors (Online Appendix 5).\u003c/p\u003e \u003cp\u003eAs a final step, we aim to identify the geography of segregation by detecting the neighborhoods where majority and minority tech workers co-reside. In 2011, majority tech workers were over-represented in most inner-city neighborhoods with the most upscale housing (Kesklinn, see Online Appendix 1) as well as in attractive detached housing areas in the outer suburbs (N\u0026otilde;mme, located in the southwestern part of the city, colored blue on Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, minority tech workers were over-represented in the largest high-rise housing estate with the most affordable housing and highest presence of the minority population (Lasnam\u0026auml;e in the eastern part of the city, coloured yellow). Mixed-ethnic neighborhoods (coloured white), where neither majority nor minority tech workers were dominant, were relatively few in 2011. Likewise, only a few neighborhoods are characterised by the over-representation (coloured violet) of both majority and minority tech workers. In contrast, many outer city neighborhoods are characterised by the under-representation (coloured red) of both majority and minority tech workers.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ABOUT HERE\u003c/p\u003e \u003cp\u003eBy 2021, the most significant change in the intra-urban ethnic geography of tech workers was the rise in the number of mixed-ethnic neighborhoods where tech workers from neither ethnic groups were over-represented or under-represented. These neighborhoods are chatacterised with the most expensive housing, being located in the inner-city (Kesklinn), in detached housing neighborhoods in the northeastern (Pirita) and southwestern (N\u0026otilde;mme) parts of the city, as well as in one high-rise housing estate in the inner suburb (Mustam\u0026auml;e), where Tallinn University of Technology is situated. The over-representation of minority tech workers in the more distant eastern neighborhoods switched to under-representation. Although these neighborhoods cover only a small area, they are among the most densely populated in the city and offer the most affordable housing. Minority tech workers reside also near the airport and Technopolis\u0026mdash;a hub of tech employers. This indicates that tech workers are clustering into four types of neighborhoods: upscale housing areas in the inner-city, attractive low-rise detached housing areas, and neighborhoods located near tech universities and tech companies.\u003c/p\u003e"},{"header":"DISCUSSION OF THE MAIN FINDINGS","content":"\u003cp\u003eOur first key finding shows that at the national level, segregation between majority and minority tech workers has increased with a clear ethnic gradient. The capital city\u0026rsquo;s attractiveness as a place of residence is the highest for foreign-born tech workers, gradually decreasing for native-born ethnic minority tech workers and ethnic majority tech workers. As high-skilled tech workers earn the highest incomes and enjoy greater residential choice, such gradient may reflect the degree of knowledge of available nation-wide residential options. The share of majority tech workers living outside the capital city and also its urban regions has been especially impressive, rising from 14 per cent in 2011 to 22 per cent in 2021. While tech jobs concentrate in \u0026ldquo;supercities\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, a sizeable share of majority-group tech workers opt for diverse residential settings\u0026mdash;including suburban and counter-urban areas\u0026mdash;often due to inherited property or past familiarity with these locations. In line with the spatial assimilation framework\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, these results may also reflect varying degrees of integration among the minority tech workers, with native-born tech workers being more integrated than foreign-born tech workers. Being more integrated and with greater familiarity and lived experiences with the nation-wide opportunities on the housing market, native-born minority tech workers may have a larger consideration set to choose from\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, facilitating relocations from the capital to other parts of the country where the majority population is overrepresented. Hence, \u0026lsquo;white flight\u0026rsquo; explanations\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e do not fully account for these moves, as the urban exodus of well-integrated minority tech workers may also be driven by considerations related to residential environs, similar to those of the majority population\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough these differences exist within the minority population, our findings still show that the vast majority of both foreign-born and native-born minority tech workers continue to reside in the capital city. As jobs suitable for tech workers remain concentrated in urban centers\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and ethnic networks hold value even for well-integrated individuals, these findings support that \u0026ldquo;supercities\u0026rdquo; are highly attractive to minority tech workers. As the share of minority populations increases in large cities, the potential for residential dispersal, even with the expansion of remote work, may therefore be more limited than previously expected\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, the life course stage does play a role in residential decision-making for minorities similarly to the majority population, as having children in the household increases the preference for suburban environments, while older age is associated with a stronger tendency for counter-urbanization\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Hence, despite their relatively small numbers, future research should further examine the residential trajectories of suburbanizing and counter-urbanizing tech workers, particularly in relation to family dynamics, individual characteristics, and destination environments.\u003c/p\u003e \u003cp\u003eOur second key finding shows that within the capital city, minority tech workers are residentially less segregated from the majority population compared to minority non-tech workers, including those in the highest occupational groups, such as managers and top professionals. Ethnic minority tech workers may be motivated to reinforce their social status\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e by applying the additional resources gained in tech jobs and moving neighborhoods with higher presence of the members of the majority population. In this way, ethnic minority tech workers may consider their profession and its associated benefits as a tool for integration\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. From a spatial perspective, the residential presence of minority tech workers has declined in neighborhoods where ethnic minorities have been traditionally overrepresented and where many of them grew up\u0026mdash;particularly in urban fringe areas dominated by high-rise buildings and characterized by the most affordable housing options. Conversely, their presence has increased in inner-city neighborhoods where members of the ethnic majority population are overrepresented, featuring predominantly low-rise up-scale apartment buildings. This shift suggests that the residential preferences of minority tech workers are more class-based than ethnicity-based, especially when compared to other highly paid minority professionals in non-tech occupations. As tech companies are hunting for talent globally, this may increase ethnic tolerance as predicted by the contact theory\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This residential preference of the tech workers is one of the important factors behind the inner-city gentrification witnessed in large cities around the world, driving up house-prices there\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, beyond this broad shift towards ethnic desegregation of the tech workers in the capital city, several important nuances emerged from our study as well. More specifically, we find that while minority tech workers seek ethnically more mixed neighborhoods compared to co-ethnics, the opposite is true for majority tech workers who self-select in more homogenous co-ethnic neighborhoods. In other words, the residential preferences of the majority tech workers are more ethnicity-based compared to minority tech workers. These differences remain intact after controlling for relevant individual characteristics, including life course stage and income. These findings align with earlier studies on ethnic residential segregation, which often highlight that the residential sorting of the majority population is the primary driver of segregation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and majority tech workers are no different in this regard.\u003c/p\u003e \u003cp\u003eOur findings further indicate that foreign-born tech workers tend to reside in neighborhoods with a higher share of ethnic majority residents than native-born minority tech workers. It may be that native-born minority tech workers have a stronger attachment to local co-ethnic communities in which they have grown up. For example, previous research has demonstrated a strong intergenerational transmission of residential outcomes of the native-born minorities\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The ethnic neighborhoods of large cities are often well-equipped with ethnic infrastructure\u0026mdash;such as schools, churches, and cultural institutions\u0026mdash;that foster vibrant ethnic communities with which native-born minorities tend to identify. Newly arrived tech workers, by contrast, belong to the pool of \u0026lsquo;global talent\u0026rsquo; seeking professional networks rather than connections to local ethnic communities. Like in many countries around the world, Estonia\u0026rsquo;s migration policies are favorable towards tech workers\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Employer recruitment is therefore based less on ethnic networks and more on professional skills, while newcomers seek quality of life by moving to urban neighborhoods with greater amenities and a higher share of the majority population\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This serves as residential integration from the beginning rather than a slow process of spatial assimilation related to improved incomes and cultural adaptation of the native-born minorities, shaped by a shift from ethnic to professional considerations and networks in residential decision-making\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Taken together, the residential preferences of foreign-born tech workers tend to be more class-based, while the residential preferences of native-born minorities are stronger ethnicity-based.\u003c/p\u003e \u003cp\u003eOur third key finding reveals that while minority tech workers in the capital city are less segregated from the majority population, facilitating city-level ethnic desegregation, they simultaneously become more segregated from co-ethnic non-tech workers, reinforcing class-based residential segregation. In other words, while existing research reveals a strong pattern of \u0026lsquo;eth-class\u0026rsquo; segregation as ethnic and class-based segregation are closely related\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, we find that the residential mobility of the tech workers contributes to the decoupling of these segregation patterns within the urban areas. However, the unintended outcome of this process is the residualization of segregation as minorities working in non-tech sector get residentially more isolated. In many large cities, the sequence seems to evolve as follows. Firstly, members of the majority population leave the most affordable housing areas. Secondly, our study of the tech workers shows that this may be followed by the members of the minority population earning higher incomes as predicted by the spatial assimilation framework\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This would lead to the triple disadvantage as ethnic minorities with low incomes get isolated in lower-cost neighborhoods, thus amplifying one of the core segregation-related concerns in major cities\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Thus, digital transition is reshaping intra-urban segregation patterns. Minorities who gain from the digital transition\u0026mdash;those earning higher-incomes and are recent arrival attracted by talent hunt of the tech sector\u0026mdash;become residentially more integrated. Minorities who gain less from the digital transition process stay put, reproduce existing patterns of residential segregation also across generations\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and, as consequence, get residentially increasingly isolated.\u003c/p\u003e \u003cp\u003eThis study has important societal and policy implications. The growth of the tech sector can offer new opportunities for ethnic desegregation, particularly among high-skilled, mobile populations with greater freedom of residential choice. However, urban policy must also address the growing spatial isolation of native non-tech minority workers\u0026mdash;especially those in lower-status occupations who remain closely tied to local ethnic networks. This dual dynamic highlights the need for comprehensive policy responses that promote ethnic inclusion while also tackling emerging class-based divides that exclude those lacking access to high-paying jobs, remote work, or flexible housing options. Crucially, preventing residential segregation from being reproduced across generations\u0026mdash;through school segregation and unequal future opportunities in the labor and housing markets\u0026mdash;should be a policy priority\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Supporting the rise of the tech sector simultaneously addressing both ethnic and class-based segregation is essential for creating a more equitable, inclusive, and socially cohesive digital future in cities.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis research contributes to the understanding of how residential segregation patterns evolve alongside the digital transition. We focused on tech workers on professional and managerial positions as they represent a minority group with greater financial freedom in housing choices, and we aimed to determine whether their decisions are more strongly shaped by ethnicity-based or class-based considerations. We find, firstly, that residential patterns among tech workers indicate growing levels of ethnic segregation at the national level, while at the urban scale, we detect a trend towards desegregation. Secondly, the ethnic desegregation of tech workers in cities is accompanied by increasing class-based segregation, leading to the decoupling of ‘eth-class’ segregation among minority tech workers. This process simultaneously elevates the residential isolation of non-tech minority workers, who remain concentrated in high-density, low-cost neighborhoods on the urban fringe. Thirdly, these changes in residential segregation remain robust even after controlling for key background factors, such as income and life course stage. Overall, our findings highlight that while the digital transition may contribute to ethnic integration in urban areas, it also reinforces nationwide ethnic segregation and intensifies spatial divisions along class lines in cities.\u003c/p\u003e\u003c/div\u003e"},{"header":"DATA AND METHODS","content":"\u003cp\u003eWe employed individual-level Estonian Population and Housing Censuses from 2011 and 2021 in our study. Based on International Standard Classification of Occupations ISCO-08, we classified the major occupations 1 to 9 into high (1–2), middle (3–4) or low (5–9) occupational status group. We then distinguished between tech and non-tech workers within the high occupation category. The occupational group of main interest for this study are tech sector workers, who belong to high occupational group (ISCO 1–2) in the field of “programming, consultancy and related activities”, which is code 62 of the statistical classification of economic activities/ nomenclature statistique des activités économiques dans la Communauté européenne or NACE. Hence, our study includes four occupational groups, including high-skilled tech workers, other high occupations, medium and low occupations (whereas non-high-skilled tech workers belong to the ‘low occupations’ group in this analysis). We focused on residential segregation between eight different ‘eth-class’ groups that include four occupations and two ethnicities, Estonians (ethnic majority) and Russians (ethnic minority). The ethnic groups are based on self-defined ethnicity. Russians are the biggest minority group, accounting for about 80% of all minorities living in Estonia. We excluded other ethnicities from the study since they are too small for a detailed neighborhood-level study of segregation.\u003c/p\u003e\u003cp\u003eWe started our analysis by detecting nationwide changes in the residential distribution of tech workers. As most tech workers live in major cities, we divide Estonia into Tallinn, Tallinn suburbs, and the rest of Estonia. We proceeded with the analysis of ethnic segregation of the tech workers in the Tallinn urban region (Online Appendix 1). The smallest spatial units include 44 neighborhoods (\u003cem\u003ekant\u003c/em\u003e) in the suburbs and 230 neighborhoods (\u003cem\u003easum\u003c/em\u003e) in Tallinn. For ease of following the presentation of the findings, we provided also a map with the eight districts and three residential zones of Tallinn that include the inner city, inner suburbs with high-rise housing estates, and outer suburbs with mostly low-rise housing. The high-rise housing estates include Väike-Õismäe in the Haabersti district, and Lasnamäe and Mustamäe districts.\u003c/p\u003e\u003cp\u003eWe calculated dissimilarity indices for Estonian municipalities and for Tallinn urban region using the neighborhood spatial units across various combinations of occupations and ethnicities. Our focus was on the change in dissimilarity indices between 2011 and 2021 to identify which ethno-occupational or ‘eth-class’ groups have become more segregated and which have become less segregated. Our focus was on comparing Estonian majority and Russian minority tech workers with other ‘eth-class’ groups. Following Marcińczak et al.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, we interpreted dissimilarity index values of less than 20 as low, 20–39 as medium, and greater than 40 as high levels of segregation. Next, we identified the neighborhood-level geographies of segregation for minority and majority tech and non-tech workers using location quotients (LQ). Following Brown and Chung\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, we interpreted LQ values greater than 1.2 as indicating a high residential concentration of the given group, and LQ values less than 0.85 as indicating a low residential concentration. We classified each neighborhood into one of five categories by combining the high and low concentrations of majority and minority residents (see Online Appendix 2). In our calculations, we excluded neighborhoods with fewer than 50 employed residents. To calculate dissimilarity indices and location quotients, we used the Geo-Segregation Analyzer\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, and for mapping, we used QGIS software.\u003c/p\u003e\u003cp\u003eFinally, we fitted a set of multinomial and linear regression models to provide deeper insights into both nationwide and intra-urban patterns of residential segregation among tech workers. We began by fitting multinomial models to explore the nationwide differences in the distribution of Estonian and Russian tech workers. The model takes the following form:\u003c/p\u003e\u003cp\u003e \u003cem\u003eP (Y = j)\u003c/em\u003e denotes the three outcome categories of living in Tallinn (1, reference category), suburbs of Tallinn (2) and in the rest of Estonia (3); \u003cem\u003eß0j\u003c/em\u003e is the intercept for category \u003cem\u003ej\u003c/em\u003e; \u003cem\u003eß1j\u003c/em\u003e, …, \u003cem\u003eßkj\u003c/em\u003e are the coefficients for the predictors \u003cem\u003eX1\u003c/em\u003e, …, \u003cem\u003eXk\u003c/em\u003e ​, associated with category \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003ek\u003c/em\u003e is the number of variables in each model. Model 1 covers both majority and minority tech workers and includes three categories of variables. The first is our key variable of interest, which is ethnicity or being an Estonian or Russian based on the ethnic self-identification of people. The second group are demographic variables to control for different life course and family-related characteristics, including age, gender, and presence of under-aged children in the family. The third group are socio-economic variables that reflect education and income. The income variable was sourced from Statistics Estonia and is based on individuals’ yearly income from all sources, divided by 12 to represent the average monthly income. We then splitted models by ethnicity to learn whether the demographic and socio-economic variables act in a similar way among members of the majority and minority populations. Finally, we ran an additional Model 4 for minorities to account for the role of personal-level integration, by adding country of birth variable.\u003c/p\u003e\u003cp\u003eWe proceeded by fitting a set of linear regression models to clarify the extent of ethnic co-residence members in the same neighborhoods in the capital city urban region among majority and minority tech workers. The model can be formalised as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e \u003cem\u003ey\u003c/em\u003e is the dependent variable that measures the share of Estonians in the neighborhood of residence to learn whether Estonian and Russian tech workers are equally likely or not to live in the neighborhoods with a high share of the majority population. \u003cem\u003eβ0\u003c/em\u003e is the intercept, \u003cem\u003eß1\u003c/em\u003e, …, \u003cem\u003eßk\u003c/em\u003e are the coefficients for the predictors \u003cem\u003eX1\u003c/em\u003e, …, \u003cem\u003eXk\u003c/em\u003e ​ and \u003cem\u003ek\u003c/em\u003e is the number of variables in each model; and \u003cem\u003eε\u003c/em\u003e is the error term. Model 1 includes both Estonian and Russian tech workers and incorporates three groups of variables. The first group captures ethnicity. The second consists of demographic variables to examine how different life course and family-related characteristics—age, gender, and the presence of underage children in the family—are associated with co-residence with ethnic majority members. The third group includes socio-economic variables, which help to understand the role of education and income in living in the same neighborhoods as members of the ethnic majority population. We then splitted the model again by ethnicity to learn what is the probability of living together with Estonians among tech workers of each ethnicity. Finally, we ran an additional Model 4 for minorities to account for the role of personal-level integration, using country of birth variable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe individual-level census and income data that support the findings of this study are not publicly available due to confidentiality agreements with the data provider \u0026ndash; Statistics Estonia. Access to these data is limited to authorized personnel and cannot be shared outside the approved research team. For further inquiries, please contact the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eETHICAL COMPLIANCE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This research complies with all relevant ethical regulations. Approval for conducting studies involving human research participants and using the census variable of ethnicity under the framework of the project \u0026ldquo;Living segregated lives: Exploring changes in spatial inequalities in digitally transforming societies\u0026rdquo; was granted by the University of Tartu Research Ethics Committee (387/T-35).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe would like to thank Statistics Estonia for providing the Population and Housing Census datasets. This work was supported by the Estonian Research Council [PRG1996 \u0026ldquo;Living segregated lives: Exploring changes in spatial inequalities in digitally transforming societies\u0026rdquo; and PRG1919 \u0026ldquo;Rethinking smartification from the margins: Co-creating Smart Rurality with and for an Aging Population\u0026rdquo;], the Estonian Ministry of Education and Science [Centre of Excellence in Energy Efficiency, Centre of Excellence for Well-Being Sciences, and the Infotechnological Mobility Observatory (https://www.imo.ut.ee/en)], and Estonian Academy of Sciences [Research Professorship of Tiit Tammaru].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMałkowska, A., Urbaniec, M. \u0026amp; Kosała, M. The impact of digital transformation on European countries: insights from a comparative analysis. \u003cem\u003eEcon. 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Montreal: Spatial Analysis and Regional Economics Laboratory (2013).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"digital transition, tech workers, class-based segregation, ethnic segregation, ethnic desegregation","lastPublishedDoi":"10.21203/rs.3.rs-6321132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6321132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digital transition is reshaping cities, transforming both labor and housing markets. While its impact on labor markets is increasingly well documented, its consequences for residential choices and segregation remain poorly understood. New jobs\u0026mdash;especially in the tech sector\u0026mdash;concentrated into cities, potentially reinforcing the residential clustering of highly skilled workers. At the same time, higher incomes and the flexibility of remote work provide tech workers with greater freedom to choose where to live, both within and beyond urban areas. It is unknown whether the residential decisions of minority tech workers are primarily ethnicity-based, reinforcing clustering and segregation, or class-based, facilitating dispersal and ethnic desegregation. Here we show that minority tech workers increasingly cluster in large cities, where they contribute to ethnic desegregation. However, this trend simultaneously intensifies class-based residential segregation, as the spatial isolation of non-tech minority workers becomes more pronounced. This dual dynamic underscores the need for urban policy responses that address not only ethnic integration but also the deepening class-based inequalities emerging from the digital transition.\u003c/p\u003e","manuscriptTitle":"Digital transition and cities: How tech workers reshape ethnic and class-based segregation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 08:00:59","doi":"10.21203/rs.3.rs-6321132/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"10166f71-6a42-4e03-8e62-d438e5ee6c5a","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47657034,"name":"Social science/Geography"},{"id":47657035,"name":"Business and commerce/Information systems and information technology"},{"id":47657036,"name":"Scientific community and society/Geography"},{"id":47657037,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-04-04T07:06:49+00:00","versionOfRecord":{"articleIdentity":"rs-6321132","link":"https://doi.org/10.1038/s44284-026-00420-4","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2026-04-03 04:00:00","publishedOnDateReadable":"April 3rd, 2026"},"versionCreatedAt":"2025-04-28 08:00:59","video":"","vorDoi":"10.1038/s44284-026-00420-4","vorDoiUrl":"https://doi.org/10.1038/s44284-026-00420-4","workflowStages":[]},"version":"v1","identity":"rs-6321132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6321132","identity":"rs-6321132","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}