Beyond the Metropolis: Neighborhood Effects and Educational Inequality in a Mid-Sized Dutch City

Beyond the Metropolis: Neighborhood Effects and Educational Inequality in a Mid-Sized Dutch City | 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 Research Article Beyond the Metropolis: Neighborhood Effects and Educational Inequality in a Mid-Sized Dutch City Orhun KAPTAN, Sui Lin Goei, Evert-Jan Velzing, Mithat Korumaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9270679/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the impact of urban indicators at the neighborhood level and school-level characteristics on academic performance in Zwolle, a mid-sized city in the Netherlands. Addressing a gap in existing research, which often focuses on large metropolitan areas or national datasets, this study applies stepwise and hierarchical regression analyses to data from 42 primary schools located in 26 neighborhoods. The data used in the study were obtained from the official platforms of Dutch educational and statistical institutions, namely Scholen op de Kaart and AlleCijfers. Two outcome measures were evaluated: the percentage of students who met the fundamental and the target academic benchmarks. Neighborhood indicators, categorized into six groups (economy, security, health, demography, education, and energy consumption), were represented by 25 variables. Simultaneously, school-level characteristics were captured by 13 variables concerning student demographics and staffing. The findings show that migrant concentration and household density are significant negative predictors of academic achievement. School-level factors, such as vocational tracking and grade repetition, also played a role but lost significance when analyzed alongside neighborhood variables, indicating that institutional disparities may be embedded within broader spatial dynamics. Spatial mapping revealed a clear north–south divide in school performance, even in a city generally perceived as spatially balanced. This study contributes to neighborhood effects theory by demonstrating how structural and institutional mechanisms reproduce inequality in a mid-sized urban setting. This underscores the need for policies that monitor the spatial distribution of disadvantage, promote more balanced school compositions, and provide targeted support for students from marginalized neighborhoods. Neighborhood effect spatial inequality urban segregation gentrification academic achievement Figures Figure 1 Introduction Efforts to enhance educational quality and reduce inequality consistently return to a central question: To what extent are educational outcomes structured by school-level dynamics or by the broader urban environments in which schools are situated? While both domains exert influence, existing research has disproportionately focused on large metropolitan areas or national aggregates, leaving the spatial dynamics of mid-sized cities comparatively under-theorised. Building on neighbourhood effects theory, this study examines how place-based configurations mediate access to opportunity structures and shape educational trajectories (Dietz, 2002 ; Galster et al., 2007 ). Prior research demonstrates that income distribution, housing quality, and residential mobility condition school choice and academic achievement (Ainsworth, 2002 ; Andersson and Subramanian, 2006 ; Bauder, 2001 ; Crowder and South, 2003 ; Massey and Fischer, 2006 ; Warrington, 2005 ). Yet such processes are not limited to metropolitan settings. In gentrifying contexts, middle-class families often bypass local schools in favour of socially homogeneous institutions, thereby sustaining segregation despite residential diversity (Boterman, 2022 ). Similarly, the settlement of refugees and low-income households in deprived neighbourhoods reinforces cumulative disadvantages across educational and social domains (Phillimore and Goodson, 2006 ). In the Netherlands, school segregation correlates more strongly with socioeconomic composition than with ethnicity (Sykes, 2011 ), and residential stratification continues to influence parental choice despite policies of formal freedom (Boterman, 2019 ; Boterman et al., 2019 ). At the institutional level, processes such as early tracking, school composition, and staff stability can either amplify or mitigate neighbourhood disadvantage (Berliner, 2013 ; Kaptan and Kocabaş, 2025 ; van de Werfhorst, 2019 ). Three interrelated gaps motivate this study. First, the specific dynamics of mid-sized cities, characterised by relative spatial coherence yet pronounced internal heterogeneity, remain empirically neglected in urban research. Second, neighbourhood effects are often operationalised through narrow socioeconomic or ethnic proxies, overlooking multidimensional configurations that span economy, security, health, demography, education, and energy. Third, educational achievement is typically treated as a single aggregate measure, concealing how contextual mechanisms operate differently across performance thresholds. In the Dutch education system, these thresholds are distinguished as fundamental achievement (the minimum proficiency expected of all students) and target achievement (a higher-level benchmark indicating advanced performance). To address these gaps, this study examines Zwolle, a mid-sized Dutch city, to disentangle the relative and combined influence of neighbourhood- and school-level factors on educational outcomes. It asks: Which neighbourhood indicators predict fundamental and target achievement? Which school-level characteristics predict student performance? When both domains are analysed jointly, which remain significant? How do housing conditions and demographic transitions shape enrolment patterns and spatial disparities? By disaggregating achievement outcomes and adopting a multidomain urban framework, this study extends neighbourhood effects theory to a non-metropolitan setting. It contributes to Urban Studies debates on spatial inequality, segregation, and the governance of educational space, illustrating how socio-spatial and institutional mechanisms intersect to reproduce inequality in mid-sized European cities. These considerations provide the entry point into the theoretical framework discussed in the following section. Urban Inequality and Education: Neighborhood Effects through the Lens of Segregation, Gentrification, and Urbanization Urbanization, segregation, and gentrification redistribute opportunities, risks, and resources in profoundly uneven ways, creating spatial mosaics of privilege and deprivation (Logan, 2016 ). These interlocking processes constitute what Harvey ( 2012 ) terms the urbanization of inequality—a socio-spatial regime in which access to education, housing, and labour markets becomes unevenly distributed across urban territories. Following Massey’s ( 1994 ) understanding of spatial divisions of labor, these processes are not merely economic but also social mechanisms that reproduce inequality through spatial organization. Neighbourhood-effects theory provides a conceptual anchor for this study, positing that spatial configurations mediate educational trajectories and social mobility (Dietz, 2002 ; Galster et al., 2007 ). Translating these abstract mechanisms into measurable indicators, this research operationalises a multidomain dataset encompassing economy, demography, health, security, education, and energy, thereby connecting urban theory to empirical analysis. Urbanization, driven by migration and economic restructuring, simultaneously fuels growth and entrenches inequality. It generates segregated districts with stratified access to public services, particularly education (Havighurst, 1967 ; Jenkins, 2013 ). Segregation—the clustering of groups along ethnic or socioeconomic lines—produces homogeneity and amplifies opportunity gaps (Karsten et al., 2006 ; Oberti, 2007 ). Indicators such as migrant concentration, household density, and age composition in the present dataset capture these demographic cleavages. Gentrification compounds these patterns. Affluent newcomers displace long-standing residents, inflate housing prices, and reconfigure school enrolments (Forster, 2006 ; Uitermark, 2003 ). These spatial restructurings resonate with Galster’s ( 2012 ) theorisation of neighbourhood disadvantage and Wilson’s ( 2012 ) concept of concentrated poverty. Consequently, variables related to housing value, tenure, and ownership composition serve as proxies for gentrification and spatial transformation. Comparative research underscores the universality of these dynamics. In the United States, redlining institutionalised segregation and curtailed educational mobility (Sampson et al., 2002 ). European cities such as Amsterdam and Paris exhibit enduring socio-spatial stratification rooted in zoning and immigration (Pinkster and Boterman, 2017 ). In South Asia, informal settlement growth and weak regulatory regimes obstruct educational access (United Nations, 2016 ). Despite these contextual variations, a recurrent mechanism persists: affluent neighbourhoods foster achievement through concentrated resources and role models (Kauppinen, 2007 ), whereas disadvantaged areas constrain aspirations and channel youth into precarious work (Bauder, 2001 ). Housing markets are central to the reproduction of urban inequality. Property values, tenure regimes, and school catchment boundaries shape residential sorting and educational access (Francis and Hutchings, 2013 ; Hamnett and Butler, 2011 ). Homeownership signals stability and privilege, whereas public housing concentrates deprivation (Oberti, 2007 ). In this study, ownership patterns, property valuation, and household density operationalise these mechanisms, bridging housing theory with empirical indicators. The Dutch context exemplifies these urban dynamics. In cities such as Amsterdam, Rotterdam, and Utrecht, immigrant families cluster in affordable districts—often exceeding 70% of residents—thereby intensifying segregation (Zorlu and Latten, 2009 ). Dense social networks provide solidarity but limit exposure to broader social and economic opportunities (Pinkster, 2007 ). Socioeconomic advantage governs selective school access (Kuyvenhoven and Boterman, 2021 ), while institutional processes such as staff stability and student composition mediate disadvantage (Kaptan and Kocabaş, 2025 ). Incorporating the educational attainment of residents, this study tests whether neighbourhood-level education profiles spill over into student performance, extending metropolitan findings to a mid-sized context. Neighbourhood resources shape academic outcomes through multiple pathways. Concentrations of educated adults elevate school achievement (Andersson and Subramanian, 2006 ), while low-income districts experience resource scarcity and higher dropout rates (Lupton, 2005 ). School-level practices—such as vocational tracking and grade repetition—often mirror spatial segmentation, reinforcing existing inequalities. Integrating neighbourhood and school indicators allows this study to disentangle how spatial and institutional mechanisms jointly reproduce disadvantage. Inequality also manifests through crime, cohesion, and health. Affluent districts, marked by safety and trust, foster concentration and long-term planning, whereas disadvantaged ones suffer from crime, low trust, and chronic stress, undermining attendance and learning (Hastings, 2009 ; Sampson et al., 2002 ). The inclusion of crime rates, traffic accidents, and perceived health indicators thus enables a multidimensional assessment of environmental (dis)advantage. Despite a rich literature, neighbourhood-effects research remains dominated by metropolitan contexts. Mid-sized cities, however, present unique opportunities for spatial analysis due to their compact morphology and mixed demographic composition. By examining Zwolle—a city that is spatially coherent yet socially heterogeneous—this study extends neighbourhood-effects theory beyond the metropolis. It integrates the dynamics of segregation, gentrification, and urbanization into a multidomain empirical framework, reconceptualising educational inequality as a product of spatially embedded institutional mechanisms operating within the urban fabric of non-metropolitan Europe. Methods This study employed a quantitative cross-sectional design to explore the relationship between neighborhood-level urban characteristics, school-level institutional factors, and academic achievement. Context This study was conducted in Zwolle, a mid-sized city located in the eastern Netherlands with a population of approximately 130,000 (CBS, 2023 ). Zwolle is often considered a balanced city in terms of urban development, combining new residential areas with post-war housing districts (Gemeente Zwolle, 2022 ; 2023 ). Unlike the more fragmented urban landscapes of Amsterdam and Rotterdam, Zwolle’s spatial planning emphasizes integrated neighborhood development and social cohesion (RIVM, 2022 ). However, disparities persist between neighborhoods, particularly in terms of income levels, housing types, and access to educational facilities. Data Set The dataset was compiled from two sources. Academic achievement, the dependent variable, was measured using “fundamental” and “target” indicators from the URL scholenopdekaart.nl. The fundamental level represents the basic proficiency in language and arithmetic expected of all students at the end of primary education (Group 8, age 12), with a national benchmark of 85% set by the Inspectorate of Education. The target level is a more advanced standard, with thresholds adjusted for each school’s student population and used as signaling values for the overall performance. For each school, the percentage of students reaching fundamental and target levels served as dependent variables. Schools were identified by filtering for “primary school” in Zwolle on the website scholenopdekaart.nl. The independent variables were drawn from neighborhood-level data from the open-source data repository allecijfers.nl (2022), ensuring consistency. Guided by the literature on urbanization and the OECD Better Life Index, 25 indicators were selected and grouped into six domains: economy, demographics, security, health, education, and energy consumption. The final dataset includes 42 primary schools across 26 neighborhoods in Zwolle. Seven schools were excluded due to missing data, and those classified as Voortgezet speciaal onderwijs (special education) were also omitted. Each school represents one observation in the analysis. School-level variables include total enrollment, grade repetition, staff distribution by contract (full-time, part-time, less than half-time), share of temporary staff, and student enrollment across secondary education tracks: Praktijkonderwijs (practical education), Beroeps and Kader onderwijs (lower vocational education), VMBO (vocational middle education),, HAVO (higher general education),, and VWO (pre-university education). In the Dutch system, students are placed in tracks based on their performance and assessments. These variables capture institutional and socioeconomic disparities across schools. Data Analysis To capture academic achievement more comprehensively, we used two dependent variables: the fundamental level, representing the basic proficiency attained by approximately 85% of students, and the target level, reflecting higher performance expectations. This dual structure makes it possible to detect variation that would be obscured by relying only on minimum benchmarks or centralized exam data. The analysis was conducted in three steps. First, stepwise regression was used to test the relationship between achievement and neighborhood indicators. Second, the same method was used to assess school-level factors, including student composition and staff conditions. Finally, hierarchical regression was used to examine their combined effects, allowing us to identify whether neighborhood or school-level variables were more predictive. If neighborhood indicators lost significance once school variables were introduced, this suggested that their influence operated through school mechanisms such as composition, practices, or resources. Conversely, if school variables lost significance after adding neighborhood indicators, it implied that structural residential conditions were more decisive. Stepwise regression used forward selection to identify the strongest predictors among many urban and institutional variables (Draper and Smith, 1998 ; Field, 2005 ). Hierarchical regression entered variables in blocks to clarify how their explanatory roles shifted when they were considered together (Field, 2005 ). All statistical assumptions were tested: tolerance (> .20) and VIF (< 10) confirmed no multicollinearity, Durbin–Watson values (1–3) excluded autocorrelation, and Mahalanobis Distance identified outliers (Cook and Weisberg, 1982 ; Craney and Surles, 2002 ; Kim, 1996 ). Two non-normally distributed variables, total registered crime and the percentage of students in Praktijkonderwijs , were excluded. Descriptive statistics and open-access data from allecijfers.nl were used to address the fourth research question. As all data were anonymized and publicly available, no formal ethical approval was required. Results Assessing the Impact of Neighborhood Factors on Fundamental Academic Achievement: To understand the relationship between neighborhood effects and education, the level of achievement of basic educational goals was first examined in relation to the neighborhood's urban indicators using stepwise linear regression analysis. The model indicated that the share of migrants in the total population significantly predicted the fundamental achievement level, explaining about 17% of the variance without autocorrelation (Durbin–Watson ≈ 2). Table 1 presents the coefficients related to this model. Table 1 Fundamental Level and Neighborhood Characteristics Coefficients Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 98,425 ,780 126,215 ,000 96,853 99,998 The share of migrants in the total population -,186 ,058 -,438 -3,197 ,003 -,303 -,069 -,438 -,438 -,438 1,000 1,000 As shown in Table 1 , among the urban variables related to neighborhoods, the only variable associated with fundamental-level achievement was the proportion of migrants in the total neighborhood population. As the proportion of migrants in a neighborhood's total population increases, the rate of reaching fundamental-level goals tends to decrease ( B = -,438; p = ,003). School-Level Indicators and Fundamental Achievement Rates In the second round of analyses, the relationship between school-level characteristics and fundamental achievement rates was examined. Results showed that the percentage of students enrolled in lower vocational education significantly predicted fundamental achievement levels, accounting for about 10% of the variance, with no autocorrelation detected (Durbin–Watson ≈ 2). The coefficients of the model are presented in Table 2 . Table 2 Stepwise Regression Coefficients for Fundamental Level and School indicators Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 97,293 ,523 186,15 ,000 96,236 98,349 % lower VET -,060 ,025 -,353 -2,382 ,022 -,110 -,009 -,353 -,353 -,353 1,000 1,000 a. Dependent Variable: Fundamental Level In this study, the proportions of students graduating from primary schools in 2022 who continued to different types of secondary education were examined. The analysis indicates that in schools with a high number of students continuing to lower-level vocational education the following year, there tends to be a decrease in the rate of achieving fundamental educational goals ( B = -,353; p =,022). This result reveals a statistically significant difference in the rates at which schools send students to lower-level vocational and technical education, with some schools having a higher number of students continuing in these programs than others. Integrating Neighborhood and School Factors: Hierarchical Regression Analysis of Fundamental Educational Achievements In the final analysis of achieving fundamental educational goals, a hierarchical regression model was conducted using the previously significant predictors: the proportion of immigrants in the total neighborhood population and the percentage of students enrolled in lower-level vocational education. The first step of the model explained around 15% of the variance, and adding the school-level variable increased the explained variance to about 17%, though the change was not statistically significant; no autocorrelation was observed (Durbin–Watson ≈ 2.1). A significant change was observed in the level of statistical significance in the second model. The Durbin-Watson score indicates that there is no issue of autocorrelation in the model. Table 3 presents the model’s coefficients. Table 3 Coefficients of Hierarchical Regression Analysis of Fundamental Level Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 98,368 ,774 127,117 ,000 96,804 99,932 The share of migrants in the total population -,169 ,058 -,417 -2,899 ,006 -,287 -,051 -,417 -,417 -,417 1,000 1,000 2 (Constant) 98,549 ,774 127,373 ,000 96,984 100,114 The share of migrants in the total population -,133 ,063 -,329 -2,131 ,039 -,260 -,007 -,417 -,323 -,302 ,845 1,184 % beroeps kader -,038 ,026 -,223 -1,445 ,156 -,091 ,015 -,353 -,225 -,205 ,845 1,184 a. Dependent Variable: Fundamenteel niveau Table 3 shows that the variable representing the proportion of students continuing to low-level vocational education loses its statistical significance ( B = -,223; p = ,156) when examined alongside the proportion of immigrants in the total neighborhood population. This indicates a stronger relationship between the proportion of immigrants in neighborhoods and the rates of achieving fundamental educational goals ( B = -,326; p = 0,039). This suggests that as the proportion of immigrants in a neighborhood increases, the rate of achieving basic educational goals in schools tends to be lower. Urban Variables and Their Impact on the Percentage of Students Achieving Target Level In this section, the relationship between the percentage of students achieving the target level and the urban indicators of neighborhoods was examined using stepwise linear regression. The final model, including the number of households, business establishments, and residents’ education level, accounted for roughly 31% of the variance in target-level achievement, with no autocorrelation detected (Durbin–Watson ≈ 1.95). The coefficients related to the analysis results are listed in Table 4 . Table 4 Coefficients of Stepwise Analysis For Target Level with Nieghborhood Indicators Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 67,376 3,287 20,495 ,000 60,746 74,006 Number of Households -,005 ,002 -,355 -2,486 ,017 -,010 -,001 -,355 -,355 -,355 1,000 1,000 2 (Constant) 67,364 3,081 21,862 ,000 61,145 73,582 Number of Households -,016 ,004 -1,037 -3,558 ,001 -,025 -,007 -,355 -,481 -,476 ,210 4,759 Business Establishments ,048 ,018 ,768 2,635 ,012 ,011 ,085 -,154 ,377 ,352 ,210 4,759 3 (Constant) 48,501 7,736 6,269 ,000 32,878 64,124 Number of Households -,020 ,004 -1,275 -4,434 ,000 -,029 -,011 -,355 -,569 -,555 ,189 5,282 Business Establishments ,066 ,018 1,051 3,582 ,001 ,029 ,103 -,154 ,488 ,448 ,182 5,497 Education Level of Residents Aged 15 to 75: Middle Education Level ,486 ,185 ,354 2,628 ,012 ,113 ,860 ,190 ,380 ,329 ,864 1,157 a. Dependent Variable: Target Level As shown in Table 4 , as the number of households in a neighborhood increases, the proportion of students achieving the target level in schools tends to decrease. In contrast, as the number of businesses and individuals with intermediate education levels in a neighborhood increases, the proportion of students reaching the target level tends to rise. The variance inflation factor and tolerance values indicated that there was no multicollinearity among the variables. Analyzing School Indicators in Relation to the Proportion of Students Achieving Target Levels In this section, the relationship between the proportion of students achieving target levels and school characteristics was analyzed using stepwise linear regression. The final model, which included the percentage of students continuing in lower vocational education and those repeating a year, explained about 41% of the variance in target-level achievement, with no indication of autocorrelation (Durbin–Watson ≈ 2.29). The coefficients of the model are presented in Table 5 . Table 5 Coefficients of Stepwise regression of Target Level with School Indicators Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 67,739 2,041 33,187 ,000 63,614 71,864 Lower level VET -,466 ,098 -,601 -4,760 ,000 -,664 -,268 -,601 -,601 -,601 1,000 1,000 2 (Constant) 70,548 2,270 31,080 ,000 65,957 75,139 Lower Level VET -,422 ,095 -,544 -4,456 ,000 -,613 -,230 -,601 -,581 -,533 ,961 1,041 Students repeating a year (%) -,396 ,168 -,288 -2,362 ,023 -,735 -,057 -,396 -,354 -,283 ,961 1,041 a. Dependent Variable: Streefniveau Table 5 reveals that a higher proportion of students repeating grades and a higher proportion of students continuing to lower-level vocational education in a school are associated with a lower tendency to achieve the target level of students. Decoding Success: Hierarchical Insights into Student Achievement and Neighborhood-School Dynamics In this section, hierarchical regression analysis was conducted on the statistically significant neighborhood and school variables identified in the previous analyses. The combined model—including the number of households, business establishments, residents’ education level, and school-level indicators such as vocational tracking and grade repetition—explained about 47% of the variance in target-level achievement, with no sign of autocorrelation (Durbin–Watson ≈ 2.31). The coefficients of the model are presented in Table 6 . Table 6 Coefficients of Hierarchical Regression Analysis of Target Level with Neighborhood and School Indicators Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero-order Partial Part Tolerance VIF 1 (Constant) 43,991 11,140 3,949 ,000 21,439 66,543 Number of households -,020 ,004 -1,280 -4,489 ,000 -,029 -,011 -,389 -,589 -,574 ,201 4,971 Education Level of Residents Aged 15 to 75: Middle Education Level ,583 ,252 ,339 2,308 ,027 ,072 1,094 ,200 ,351 ,295 ,758 1,319 Business establishments ,070 ,019 1,106 3,637 ,001 ,031 ,109 -,169 ,508 ,465 ,177 5,655 2 (Constant) 61,938 11,453 5,408 ,000 38,711 85,165 Number of households -,010 ,005 -,663 -2,058 ,047 -,021 ,000 -,389 -,324 -,235 ,126 7,952 Education Level of Residents Aged 15 to 75: Middle Education Level ,278 ,245 ,162 1,135 ,264 -,218 ,774 ,200 ,186 ,130 ,643 1,554 Business establishments ,030 ,021 ,479 1,412 ,167 -,013 ,074 -,169 ,229 ,161 ,113 8,843 percentage of students continuing lower-level vocational education -,322 ,101 -,416 -3,189 ,003 -,527 -,117 -,601 -,469 -,364 ,766 1,306 Percentage of students repeating a year -,248 ,180 -,181 -1,373 ,178 -,614 ,118 -,396 -,223 -,157 ,754 1,327 a. Dependent Variable: target level When urban indicators related to neighborhoods and school characteristics are examined, variables such as the proportion of individuals with intermediate education, the number of businesses, and the rate of students repeating grades lose statistical significance. The final model suggests that a higher number of households in a neighborhood is associated with a decrease in the number of students achieving the target level in schools within that neighborhood, and a higher proportion of students continuing low-level vocational education in a school is associated with a lower rate of students achieving the target level. Visualizing Analysis Results on the Map: Spatial Reflections of Educational and Urban Indicators To visualize the distribution of neighborhoods according to the number of households, the proportion of students continuing in low-level vocational education, and the share of migrants in the population, an interactive 3D graph was developed. 1 . The neighborhoods of Milligen and Holtenbroek IV stand out negatively compared to other neighborhoods. Milligen, which had a population of 7,110 in 2013, decreased to 6,900 by 2024. The average house prices in this neighborhood were 300,000 Euros in 2022, rising to 347,000 Euros by 2023. The average house sale price in Zwolle in 2022 was 314,285 Euros. In Milligen, 70% of the houses are owner-occupied, 12% are rented, and 18% are under woningcorporaties 2 status. Most of the houses in this area were built between 1990 and 2020, with 32.7% being apartments, 46.8% semi-detached houses, and 15.7% corner houses. Key features of Milligen include a high percentage of houses built after 2000, a high number of cars per surface area, a high population density per square kilometer, a relatively long average distance to cafes, and better access to public services such as government, education, and healthcare compared to other areas ( https://allecijfers.nl/ ). Holtenbroek IV, which had a population of 3,170 in 2013, experienced a 0.47% increase, reaching 3,185 people by 2024. In this neighborhood, 62% of the homes are under the social housing status, with 24% owner-occupied and only 14% rented homes. The average house sale price in 2022 was 190,000 Euros, rising to approximately 220,000 Euros in 2023. In Holtenbroek IV, 78.3% of the homes are apartments, and 12.8% are semi-detached houses, most of which were built between 1950 and 1970. This neighborhood's average income per person is 22,400 Euros, compared to 30,100 Euros in Milligen. The average annual income per person for all neighborhoods included in the study is 29,345 Euros ( https://allecijfers.nl/ ). The sharp increase in housing prices and the dominance of newer, post-2000 housing stock in Milligen suggest a population movement toward more affordable areas, such as Holtenbroek IV. This trend is also reflected in shifting school demographics: the population of Triomundo School in the Milligen neighborhood has been gradually decreasing, from 500 students in 2012–2013 to 105 students in 2023–2024. In the Holtenbroek IV neighborhood, the Toonladder School has been experiencing a gradual increase in its student population. The school, which had approximately 150 students in the 2011–2012 academic year, had 373 students in the 2023–2024 academic year. These enrollment changes reflect broader residential and socioeconomic transitions at the neighborhood level. The map in Fig. 1 demonstrates that schools with a comparatively higher academic success rate ( black oval ) are established in the southern part of the city. In contrast, the schools (in a red oval) on the northern side have a comparatively lower academic achievement level. The map also exhibits movement from the western side to the eastern part of the city. Discussion This study examined how neighborhood indicators and school characteristics shape fundamental and target academic achievement in the mid-sized Dutch city of Zwolle. While previous research has focused on large metropolitan contexts, our aim was to provide a granular view of how inequalities unfold in a spatially balanced mid-sized city. Grounded in neighborhood effects theory (Dietz, 2002 ; Galster, 2012 ; Sampson et al., 2002 ), the findings show how spatial and institutional dynamics reproduce inequality locally. Applying this lens to Zwolle reveals whether mechanisms identified in metropolitan areas—segregation, tracking, and institutional sorting—also operate in more coherent, less fragmented urban contexts. As Havighurst ( 1967 ) and Jenkins ( 2013 ) note, even smaller cities can generate unequal access to education when zoning and investment favor particular neighborhoods. These results show how spatial reproduction functions within a compact urban regime where institutional and spatial mechanisms converge more tightly than in larger cities. Although much research has examined major urban centers such as Amsterdam, Rotterdam, and Paris—where density, diversity, and migration flows are more complex—this study demonstrates that similar mechanisms operate in mid-sized cities like Zwolle. Yet, important contrasts emerge. In Amsterdam, parental choice intersects with sharp ethnic and socioeconomic divisions, creating stratified school landscapes (Boterman, 2019 ; Boterman et al., 2019 ). In contrast, Zwolle’s smaller scale and coherent morphology allow clearer links between neighborhood stratification and school outcomes. Unlike the fragmented geography of large cities, Zwolle illustrates how even moderate variations in household density or business presence shape school performance. Together, these findings highlight that urban scale—not density or diversity alone—structures the reproduction of educational inequality. Our results align with Boterman et al. ( 2019 ) in identifying spatial sorting but show that in smaller cities these processes are more directly tied to urban form and institutional access, such as employment hubs and school types. The presence of highly educated adults reinforces localized educational capital and aspiration, a dynamic also observed by Andersson and Subramanian ( 2006 ). Neighborhood mechanisms—particularly opportunity structures and exposure—remain central to educational outcomes. Migrant concentration and household density are negatively associated with achievement (Galster, 2012 ), echoing Sampson et al.’s ( 2002 ) concept of institutional strain in dense areas. The reinforcing effect of spatial homogeneity, whether ethnic or socioeconomic, is equally significant. As Karsten et al. ( 2006 ) and Oberti ( 2007 ) note, homogeneous neighborhoods restrict institutional diversity and limit exposure to wider networks and resources. A further contribution is the multidimensional operationalization of neighborhood effects. Whereas most studies rely on single measures like socioeconomic status or ethnic concentration (Pinkster, 2007 ; Phillimore and Goodson, 2006 ), this study used 25 indicators across six domains. The significance of household density and business establishments indicates that inequality cannot be reduced to poverty or ethnicity alone. Instead, economic vitality, housing, health, security, and demographic transitions jointly shape opportunity. This multidomain approach expands the analytical scope of neighborhood-effects research, showing that inequality in mid-sized cities follows its own spatial logic rather than simply replicating metropolitan disadvantage. A methodological contribution lies in disaggregating academic performance into fundamental and target levels, revealing how inequality operates across thresholds. At the fundamental level, migrant concentration predicts attainment, reflecting Galster’s ( 2012 ) exposure mechanisms and Sampson et al.’s ( 2002 ) institutional disorganization. At the target level, this relationship disappears; instead, household density, parental education, and business density become decisive. This partly contrasts with Sykes ( 2011 ), who minimized ethnic composition effects, suggesting that clustering influences minimum benchmarks, while higher achievement depends on socioeconomic capital and institutional proximity. These findings also reflect Wilson’s ( 2012 ) notion of concentrated poverty, where deprivation constrains responsiveness and mobility. By distinguishing between thresholds, the study addresses a gap in research that often treats performance as a single outcome. The finding that migrant concentration constrains basic proficiency while other indicators drive higher attainment demonstrates that mechanisms vary by performance level. This contrasts with Kuyvenhoven and Boterman ( 2021 ), who emphasized school-level effects, and complements Boterman et al. ( 2019 ), who stressed spatial and institutional contexts. The results also echo Andersson and Subramanian ( 2006 ), refining how socio-cultural capital and demographic stability function across achievement levels. Consistent with Bauder ( 2001 ), Lupton ( 2005 ), and Kuyvenhoven and Boterman ( 2021 ), our data show that schools serving disadvantaged neighborhoods tend to have higher shares of students entering lower vocational tracks and repeating grades. These sorting mechanisms are key pathways through which spatial disadvantage becomes educational disadvantage. Lower vocational tracking negatively correlates with both fundamental and target levels, and its influence strengthens at higher achievement thresholds, confirming that early pathways constrain long-term opportunity. The spatial clustering of underperformance in Zwolle parallels findings by Boterman ( 2019 , 2022 ) and Boterman et al. ( 2019 ) on segregation in Dutch cities despite parental choice. Although smaller than Amsterdam, Zwolle’s north–south divide and stratification suggest similar dynamics. These patterns also align with Pinkster and Boterman ( 2017 ) and Zorlu and Latten ( 2009 ), who linked zoning and housing to inequality. Zwolle’s compact geography and centralized planning clarify these links: high-density neighborhoods—often social housing blocks—limit access to employment and commercial centers, while areas with greater business density are more affluent and connected. This explains the role of business establishments in predicting target-level achievement. Prior research has shown how housing and school systems reinforce exclusion (Francis and Hutchings, 2013 ; Hamnett and Butler, 2011 ). Although residential mobility or enrollment zones were not directly assessed, the spatial distribution of school performance suggests implicit selection and self-sorting. The absence of correlation in newer, affluent areas such as Milligen challenges Zorlu and Latten’s ( 2009 ) assumptions and supports Logan’s ( 2016 ) argument that inequality is not confined to visibly deprived areas. Simultaneously, Milligen may signal early gentrification, as demographic change reshapes educational demand and composition (Forster, 2006 ; Uitermark, 2003 ). While school-level effects like vocational tracking strongly predicted outcomes, their reduced significance in hierarchical models suggests partial endogeneity to neighborhood dynamics. This interpretation aligns with Galster et al. ( 2007 ) and Duncan ( 1994 ), who argued that neighborhood context shapes outcomes through institutional settings. The results also support aspiration-based mechanisms (Kauppinen, 2007 ) and the social capital constraints noted by Pinkster ( 2007 ). Environmental conditions such as crime and chronic stress further undermine achievement in disadvantaged areas (Hastings, 2009 ). Beyond analytical contributions, these findings raise issues of spatial justice and governance. Addressing educational inequality requires more than school-level reform; it demands integrated urban–educational planning that targets neighborhood disadvantage. Spatial justice depends on aligning housing, mobility, and education policies to redistribute opportunity structures within mid-sized cities. Overall, this study extends neighborhood-effects research to a mid-sized city (Boterman, 2019 ; Boterman et al., 2019 ), operationalizes disadvantage through multidomain indicators (Pinkster, 2007 ; Phillimore and Goodson, 2006 ; Uitermark, 2003 ), and differentiates between fundamental and target thresholds (Kuyvenhoven and Boterman, 2021 ). Comparing these findings with metropolitan research shows that while large Dutch cities like Amsterdam and Rotterdam exhibit complex, multi-ethnic segregation, Zwolle reveals how similar inequalities are reproduced through more direct spatial mechanisms. Mid-sized cities thus not only mirror but recalibrate metropolitan processes, offering a sharper view of how local urban forms and institutional structures jointly reproduce educational inequality. Conclusion This study examined how neighbourhood-level indicators and school-level factors shape academic performance in the mid-sized Dutch city of Zwolle, using fundamental and target achievement as outcomes. The analysis shows how spatial and institutional dynamics intersect to reproduce educational inequality within a compact yet stratified urban setting. At the neighbourhood scale, migrant concentration and household density were linked to lower scores, while the number and educational level of adult residents predicted higher achievement. These results confirm that exposure to spatial disadvantage and local educational capital remain key mechanisms shaping student outcomes. At the institutional level, school composition—particularly the share of students in lower vocational tracks and grade repetition—was associated with lower performance, especially at the target level. Yet when neighbourhood and school factors were analysed together, school effects weakened, indicating that institutional disparities are embedded in broader spatial contexts. By disaggregating outcomes into fundamental and target thresholds, this study clarifies how mechanisms differ across achievement levels: migrant concentration constrains minimum proficiency, whereas socioeconomic and infrastructural factors drive higher attainment. Beyond analysis, the findings call for integrated urban–educational strategies linking housing, mobility, and community investment to advance spatial justice. Mid-sized cities, as compact yet stratified regimes, thus offer critical insights into how local forms and institutions jointly reproduce educational inequality. Policy Implications The findings reveal that migrant concentration in schools and neighborhoods exerts a constitutive effect on educational quality, necessitating governance measures that more equitably redistribute populations and resources. Without such intervention, disadvantage risks becoming structurally entrenched and spatially reproduced across generations. Divergences in student trajectories underscore the need for targeted measures—such as interschool exchanges, enrichment programmes for lower-achieving pupils, and stronger alignment between educational guidance and labour-market demands. In Zwolle, this requires embedding educational and vocational pathways in the city’s evolving economy while resisting the reproduction of low-expectation trajectories. Gentrification is reconfiguring neighborhood demographics, threatening both displacement and the consolidation of concentrated poverty. Mitigation demands systematic socio-spatial monitoring and proactive housing regulation, including rent controls, the preservation of affordable stock, and the integration of social housing within mixed-tenure developments. Considered holistically, these implications affirm that reducing educational inequality requires interventions beyond the school domain, extending into the governance of urban space. Mid-sized cities such as Zwolle—with their compact geographies and clear institutional interfaces—offer earlier and more direct policy levers than metropolitan contexts, making them crucial laboratories for integrative urban–educational strategies. These recommendations align with the principles of spatial justice and inclusive urban governance, recognising that educational equity must be pursued as a central dimension of urban policy. This study conceptualises mid-sized cities as spatial regimes of inequality, where educational opportunity is structured and reproduced through the interdependence of housing, institutional composition, and local governance. Embedding equity-oriented planning within these compact urban regimes thus represents a key frontier for achieving spatial justice in education. Limitations and Future Delimiters The first limitation of this study is its cross-sectional design. Although more recent urban data were available, the most up-to-date school-level data pertained to 2022. For this reason, the analyses were confined to the year 2022 to ensure consistency across datasets and to maintain feasibility during the model-building phase, given the preliminary nature of the study. The results suggest that a longitudinal study could provide deeper insights into the process of urbanization. The second limitation is that it is a quantitative study that generalizes based on the findings related to schools. Schools may experience changes in their academic levels due to factors other than those identified in this study. Finally, the study was limited to Zwolle. Another limitation of this study is that it only included primary schools. Due to factors such as teacher employment conditions, school preferences of students and their families, psychological factors linked to the age characteristics of students, and differences in academic achievement expectations, conducting a similar study in secondary schools can provide deeper insights into neighborhood effects and school characteristics. The results demonstrate that urban factors and neighborhood effects are related to academic success at the micro level. Therefore, applying the same research methodology to examine neighborhoods in all cities in the Netherlands and other countries could provide more in-depth information about cities with different urbanization narratives. Declarations Author Contribution O.K. conceived the study, designed the research, collected and analysed the data, and wrote the main manuscript text. S.L.G. contributed to the conceptual development of the study, supported the interpretation of the findings, and critically revised the manuscript. E.J.V. contributed to the research design, supported the interpretation of the results, and critically revised the manuscript. M.K. contributed to the methodological framing of the study, supported the interpretation of the findings, and critically revised the manuscript. All authors reviewed and approved the final manuscript. Data Availability The data used in this study were obtained from the AlleCijfers.nl and Scholen op de Kaart (scholenopdekaart.nl) databases, and the resulting dataset is provided as supplementary material. References Ainsworth, J.W.: Why does it take a village? The mediation of neighborhood effects on educational achievement. Soc. Forces. 81 (1), 117–152 (2002). https://doi.org/10.1353/sof.2002.0038 AlleCijfers.nl: Wijk- en buurtcijfers Zwolle. (2024). Available at: https://allecijfers.nl (accessed 09 September 2024) Andersson, E., Subramanian, S.V.: Explorations of neighbourhood and educational outcomes for young Swedes. Urban Stud. 43 (11), 2013–2025 (2006). https://doi.org/10.1080/00420980600897834 Bauder, H.: You’re good with your hands, why don’t you become an auto mechanic’: Neighborhood context, institutions and career development. Int. J. Urban Reg. Res. 25 (3), 593–608 (2001). https://doi.org/10.1111/1468-2427.00331 Berliner, D.C.: Effects of inequality and poverty vs. teachers and schooling on America’s youth. Teachers Coll. Record. 115 (12), 1–26 (2013) Boterman, W., Musterd, S., Pacchi, C., Ranci, C.: School segregation in contemporary cities: Socio-spatial dynamics, institutional context and urban outcomes. Urban Stud. 56 (15), 3055–3073 (2019). https://doi.org/10.1177/0042098019868377 Boterman, W.R.: The role of geography in school segregation in the free parental choice context of Dutch cities. Urban Stud. 56 (15), 3074–3094 (2019). https://doi.org/10.1177/0042098019832201 Boterman, W.R.: School choice and school segregation in the context of gentrifying Amsterdam. Hous. Stud. 37 (5), 720–741 (2022). https://doi.org/10.1080/02673037.2020.1829563 CBS: Kerncijfers wijken en buurten 2023 – Zwolle. Centraal Bureau voor de Statistiek. (2023). Available at: https://www.cbs.nl (accessed 12 September 2024) Cook, R.D., Weisberg, S.: Residuals and influence in regression. Chapman and Hall, New York (1982) Craney, T.A., Surles, J.G.: Model-dependent variance inflation factor cutoff values. Qual. Eng. 14 (3), 391–403 (2002) Crowder, K., South, S.J.: Neighborhood distress and school dropout: The variable significance of community context. Soc. Sci. Res. 32 (4), 659–698 (2003). https://doi.org/10.1016/S0049-089X(03)00035-8 Dietz, R.D.: The estimation of neighborhood effects in the social sciences: An interdisciplinary approach. Soc. Sci. Res. 31 (4), 539–575 (2002). https://doi.org/10.1016/S0049-089X(02)00005-4 Draper, N.R., Smith, H.: Applied regression analysis, 1st edn. Wiley, New York (1998). https://doi.org/10.1002/9781118625590 Duncan, G.J.: Families and neighbors as sources of disadvantage in the schooling decisions of white and black adolescents. Am. J. Educ. 103 (1), 20–53 (1994). https://doi.org/10.1086/444088 Field, A.: Discovering statistics using SPSS. Sage, London (2005) Forster, C.: The challenge of change: Australian cities and urban planning in the new millennium. Geographical Res. 44 (2), 173–182 (2006). https://doi.org/10.1111/j.1745-5871.2006.00374.x Francis, B., Hutchings, M.: Parent power? Using money and information to boost children’s chances of educational success. London: Sutton Trust. (2013). Available at: https://eric.ed.gov/?id=ED594808 (accessed 22 October 2024) Galster, G.C.: The mechanism(s) of neighbourhood effects: Theory, evidence, and policy implications. In: Van Ham, M., Manley, D., Bailey, N., Simpson, L., Maclennan, D. (eds.) Neighbourhood effects research: New perspectives, pp. 23–56. Springer Netherlands, Dordrecht (2012). https://doi.org/10.1007/978-94-007-2309-2_2 Galster, G., Marcotte, D.E., Mandell, M., Wolman, H., Augustine, N.: The influence of neighborhood poverty during childhood on fertility, education, and earnings outcomes. Hous. Stud. 22 (5), 723–751 (2007). https://doi.org/10.1080/02673030701474669 Gemeente Zwolle: Stadsmonitor Zwolle 2022. (2022). Available at: https://www.zwolle.nl (accessed 14 October 2024) Gemeente Zwolle: Onderzoek naar de toekomst van Regio Zwolle 2040. (2023). Available at: https://www.zwolle.nl/onderzoek-naar-de-toekomst-van-regio-zwolle-2040 (accessed 14 October 2024) Hamnett, C., Butler, T.: Geography matters’: The role distance plays in reproducing educational inequality in East London. Trans. Inst. Br. Geogr. 36 (4), 479–500 (2011). https://doi.org/10.1111/j.1475-5661.2011.00444.x Harvey, D.: Rebel cities: From the right to the city to the urban revolution. Verso, London (2012) Hastings, A.: Neighbourhood environmental services and neighbourhood ‘effects’: Exploring the role of urban services in intensifying neighbourhood problems. Hous. Stud. 24 (4), 503–524 (2009). https://doi.org/10.1080/02673030902938389 Havighurst, R.J.: Urbanization and education in the United States. Int. Rev. Educ.: 393–409. (1967) Jenkins, P.: Urbanization, urbanism, and urbanity in an African city: Home spaces and house cultures. Springer, Dordrecht (2013) Kaptan, O., Kocabaş, İ.: Neighborhoods and schools: The socio-spatial dynamics of educational achievement in Amsterdam. Educational Process. Int. J. 15 (1) (2025). https://doi.org/10.22521/edupij.2025.149 Karsten, S., Felix, C., Ledoux, G., Meijnen, W., Roeleveld, J., Van Schooten, E.: Choosing segregation or integration? The extent and effects of ethnic segregation in Dutch cities. Educ. Urban Soc. 38 (2), 228–247 (2006). https://doi.org/10.1177/0013124505282606 Kauppinen, T.M.: Neighborhood effects in a European city: Secondary education of young people in Helsinki. Soc. Sci. Res. 36 (1), 421–444 (2007). https://doi.org/10.1016/j.ssresearch.2006.04.003 Kim, C.: Cutoff values for Cook’s distance. Commun. Stat. Appl. Methods. 3 (2), 13–19 (1996) Kuyvenhoven, J., Boterman, W.R.: Neighbourhood and school effects on educational inequalities in the transition from primary to secondary education in Amsterdam. Urban Stud. 58 (13), 2660–2682 (2021) Logan, J.R.: As long as there are neighborhood. City Community. 15 (1), 23–28 (2016). https://doi.org/10.1111/cico.12149 Lupton, R.: Social justice and school improvement: Improving the quality of schooling in the poorest neighbourhoods. Br. Edu. Res. J. 31 (5), 589–604 (2005). https://doi.org/10.1080/01411920500240759 Massey, D.: Space, place and gender. University of Minnesota Press, Minneapolis (1994) Massey, D.S., Fischer, M.J.: The effect of childhood segregation on minority academic performance at selective colleges. Ethnic Racial Stud. 29 (1), 1–26 (2006). https://doi.org/10.1080/01419870500351159 Oberti, M.: Social and school differentiation in urban space: Inequalities and local configurations. Environ. Plann. A: Econ. Space. 39 (1), 208–227 (2007). https://doi.org/10.1068/a39159 Phillimore, J., Goodson, L.: Problem or opportunity? Asylum seekers, refugees, employment and social exclusion in deprived urban areas. Urban Stud. 43 (10), 1715–1736 (2006). https://doi.org/10.1080/00420980600838606 Pinkster, F.M.: Localised social networks, socialisation and social mobility in a low-income neighbourhood in the Netherlands. Urban Stud. 44 (13), 2587–2603 (2007). https://doi.org/10.1080/00420980701558384 Pinkster, F.M., Boterman, W.R.: When the spell is broken: Gentrification, urban tourism and privileged discontent in the Amsterdam canal district. Cult. Geographies. 24 (3), 457–472 (2017). https://doi.org/10.1177/1474474017706176 RIVM: Leefomgeving en gezondheid in Zwolle – GezondStad verkenning. Rijksinstituut voor Volksgezondheid en Milieu. (2022). Available at: https://www.rivm.nl (accessed 14 October 2024) Sampson, R.J., Morenoff, J.D., Gannon-Rowley, T.: Assessing ‘neighborhood effects’: Social processes and new directions in research. Ann. Rev. Sociol. 28 (1), 443–478 (2002). https://doi.org/10.1146/annurev.soc.28.110601.141114 Scholenopdekaart.nl: Onderwijsgegevens van scholen in Zwolle. (2024). Available at: https://scholenopdekaart.nl (accessed 09 September 2024) Sykes, B.: Spatial order and social position: Neighbourhoods, schools and educational inequality. Universiteit van Amsterdam, Amsterdam (2011) Uitermark, J.: Social mixing’ and the management of disadvantaged neighbourhoods: The Dutch policy of urban restructuring revisited. Urban Stud. 40 (3), 531–549 (2003). https://doi.org/10.1080/0042098032000053905 United Nations: The world’s cities in 2016. United Nations, New York (2016). https://doi.org/10.18356/8519891f-en Van de Werfhorst, H.G.: Early tracking and social inequality in educational attainment: Educational reforms in 21 European countries. Am. J. Educ. 126 (1), 65–99 (2019). https://doi.org/10.1086/705500 Warrington, M.: Mirage in the desert? Access to educational opportunities in an area of social exclusion. Antipode. 37 (4), 796–816 (2005). https://doi.org/10.1111/j.0066-4812.2005.00526.x Wilson, W.J.: The truly disadvantaged: The inner city, the underclass, and public policy. University of Chicago Press, Chicago (2012) Zorlu, A., Latten, J.: Ethnic sorting in the Netherlands. Urban Stud. 46 (9), 1899–1923 (2009). https://doi.org/10.1177/0042098009106023 Footnotes "To access the interactive 3D graph, please visit: https://rzgrorhun.github.io/graphics/interactive2_plot.html woningcorporaties is a Dutch term referring to social housing corporations. These organizations own and manage rental properties, primarily for lower-income residents. The aim of huur-corporaties is to provide affordable housing, often at regulated rental prices, ensuring access to housing for those who may not be able to afford market-rate homes. They play a key role in the Netherlands' social housing system, supporting communities with a mix of income levels. Additional Declarations No competing interests reported. Supplementary Files Zwolledataset.xlsx Cite Share Download PDF Status: Posted 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-9270679","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633478758,"identity":"8c3df016-fd8c-40b7-b865-1265f6409f02","order_by":0,"name":"Orhun KAPTAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYFACHiA+AGYxPgBx+YjWAqSYDUBcNlK0sEmA+AS18EufPSZdcMYuz5797LHKrzl2MmwMzA8f3cCjRbIvL016xo3kYh6evLTbstuSgQ5jMzbOwaPF4AyPmTTPB+bEHoYcs9uS25iBWnjYpInQUp/Yw//GrFhyWz2xWm4cTuyRyDFj/LjtMGEtkj08xtY8Z44X89x4YyzNuO04DxszAb/w8/AY3uY5Vp3H3p9j+PHntmp7fvbmh4/xaYGBBBDBzAMmiVAO18L4g0jVo2AUjIJRMLIAAHsoPzjG5xrFAAAAAElFTkSuQmCC","orcid":"","institution":"Yıldız Technical University","correspondingAuthor":true,"prefix":"","firstName":"Orhun","middleName":"","lastName":"KAPTAN","suffix":""},{"id":633478759,"identity":"573ea946-1508-4eec-a670-d5f39c50518e","order_by":1,"name":"Sui Lin Goei","email":"","orcid":"","institution":"Windesheim University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sui","middleName":"Lin","lastName":"Goei","suffix":""},{"id":633478760,"identity":"66838757-a005-446a-8725-bbc789800c83","order_by":2,"name":"Evert-Jan Velzing","email":"","orcid":"","institution":"Windesheim University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Evert-Jan","middleName":"","lastName":"Velzing","suffix":""},{"id":633478761,"identity":"482e08d3-3c79-412e-82ea-89ddfaaae964","order_by":3,"name":"Mithat Korumaz","email":"","orcid":"","institution":"Yıldız Technical University","correspondingAuthor":false,"prefix":"","firstName":"Mithat","middleName":"","lastName":"Korumaz","suffix":""}],"badges":[],"createdAt":"2026-03-30 17:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9270679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9270679/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108976792,"identity":"724f3ce7-2de0-495f-86a7-c94496ab0e20","added_by":"auto","created_at":"2026-05-11 11:28:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":651125,"visible":true,"origin":"","legend":"\u003cp\u003eThe schools’ distribution in Zwolle\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9270679/v1/e66daaac8b99cdfc8cd37662.png"},{"id":108979531,"identity":"affdc2ac-5dc5-4b54-bb4c-f044d0c3e029","added_by":"auto","created_at":"2026-05-11 11:59:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9270679/v1/d2f4873d-d76d-4fac-bd8f-bd0a6cfe85b7.pdf"},{"id":108443757,"identity":"5eaf6fa2-6ae7-463d-b214-6ebe900bfd1a","added_by":"auto","created_at":"2026-05-04 17:22:35","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20911,"visible":true,"origin":"","legend":"","description":"","filename":"Zwolledataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9270679/v1/7f80154492c964f4f2d3ebcc.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond the Metropolis: Neighborhood Effects and Educational Inequality in a Mid-Sized Dutch City","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEfforts to enhance educational quality and reduce inequality consistently return to a central question: To what extent are educational outcomes structured by school-level dynamics or by the broader urban environments in which schools are situated? While both domains exert influence, existing research has disproportionately focused on large metropolitan areas or national aggregates, leaving the spatial dynamics of mid-sized cities comparatively under-theorised.\u003c/p\u003e \u003cp\u003eBuilding on neighbourhood effects theory, this study examines how place-based configurations mediate access to opportunity structures and shape educational trajectories (Dietz, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Galster et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Prior research demonstrates that income distribution, housing quality, and residential mobility condition school choice and academic achievement (Ainsworth, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Andersson and Subramanian, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bauder, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Crowder and South, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Massey and Fischer, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Warrington, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Yet such processes are not limited to metropolitan settings. In gentrifying contexts, middle-class families often bypass local schools in favour of socially homogeneous institutions, thereby sustaining segregation despite residential diversity (Boterman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, the settlement of refugees and low-income households in deprived neighbourhoods reinforces cumulative disadvantages across educational and social domains (Phillimore and Goodson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the Netherlands, school segregation correlates more strongly with socioeconomic composition than with ethnicity (Sykes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and residential stratification continues to influence parental choice despite policies of formal freedom (Boterman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boterman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the institutional level, processes such as early tracking, school composition, and staff stability can either amplify or mitigate neighbourhood disadvantage (Berliner, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kaptan and Kocabaş, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; van de Werfhorst, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree interrelated gaps motivate this study. First, the specific dynamics of mid-sized cities, characterised by relative spatial coherence yet pronounced internal heterogeneity, remain empirically neglected in urban research. Second, neighbourhood effects are often operationalised through narrow socioeconomic or ethnic proxies, overlooking multidimensional configurations that span economy, security, health, demography, education, and energy. Third, educational achievement is typically treated as a single aggregate measure, concealing how contextual mechanisms operate differently across performance thresholds. In the Dutch education system, these thresholds are distinguished as fundamental achievement (the minimum proficiency expected of all students) and target achievement (a higher-level benchmark indicating advanced performance).\u003c/p\u003e \u003cp\u003eTo address these gaps, this study examines Zwolle, a mid-sized Dutch city, to disentangle the relative and combined influence of neighbourhood- and school-level factors on educational outcomes. It asks:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhich neighbourhood indicators predict fundamental and target achievement?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhich school-level characteristics predict student performance?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhen both domains are analysed jointly, which remain significant?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do housing conditions and demographic transitions shape enrolment patterns and spatial disparities?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy disaggregating achievement outcomes and adopting a multidomain urban framework, this study extends neighbourhood effects theory to a non-metropolitan setting. It contributes to Urban Studies debates on spatial inequality, segregation, and the governance of educational space, illustrating how socio-spatial and institutional mechanisms intersect to reproduce inequality in mid-sized European cities. These considerations provide the entry point into the theoretical framework discussed in the following section.\u003c/p\u003e\n\u003ch3\u003eUrban Inequality and Education: Neighborhood Effects through the Lens of Segregation, Gentrification, and Urbanization\u003c/h3\u003e\n\u003cp\u003eUrbanization, segregation, and gentrification redistribute opportunities, risks, and resources in profoundly uneven ways, creating spatial mosaics of privilege and deprivation (Logan, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These interlocking processes constitute what Harvey (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) terms the urbanization of inequality\u0026mdash;a socio-spatial regime in which access to education, housing, and labour markets becomes unevenly distributed across urban territories. Following Massey\u0026rsquo;s (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) understanding of spatial divisions of labor, these processes are not merely economic but also social mechanisms that reproduce inequality through spatial organization.\u003c/p\u003e \u003cp\u003eNeighbourhood-effects theory provides a conceptual anchor for this study, positing that spatial configurations mediate educational trajectories and social mobility (Dietz, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Galster et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Translating these abstract mechanisms into measurable indicators, this research operationalises a multidomain dataset encompassing economy, demography, health, security, education, and energy, thereby connecting urban theory to empirical analysis.\u003c/p\u003e \u003cp\u003eUrbanization, driven by migration and economic restructuring, simultaneously fuels growth and entrenches inequality. It generates segregated districts with stratified access to public services, particularly education (Havighurst, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Jenkins, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Segregation\u0026mdash;the clustering of groups along ethnic or socioeconomic lines\u0026mdash;produces homogeneity and amplifies opportunity gaps (Karsten et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oberti, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Indicators such as migrant concentration, household density, and age composition in the present dataset capture these demographic cleavages.\u003c/p\u003e \u003cp\u003eGentrification compounds these patterns. Affluent newcomers displace long-standing residents, inflate housing prices, and reconfigure school enrolments (Forster, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uitermark, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These spatial restructurings resonate with Galster\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) theorisation of neighbourhood disadvantage and Wilson\u0026rsquo;s (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) concept of concentrated poverty. Consequently, variables related to housing value, tenure, and ownership composition serve as proxies for gentrification and spatial transformation.\u003c/p\u003e \u003cp\u003eComparative research underscores the universality of these dynamics. In the United States, redlining institutionalised segregation and curtailed educational mobility (Sampson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). European cities such as Amsterdam and Paris exhibit enduring socio-spatial stratification rooted in zoning and immigration (Pinkster and Boterman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In South Asia, informal settlement growth and weak regulatory regimes obstruct educational access (United Nations, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite these contextual variations, a recurrent mechanism persists: affluent neighbourhoods foster achievement through concentrated resources and role models (Kauppinen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), whereas disadvantaged areas constrain aspirations and channel youth into precarious work (Bauder, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHousing markets are central to the reproduction of urban inequality. Property values, tenure regimes, and school catchment boundaries shape residential sorting and educational access (Francis and Hutchings, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hamnett and Butler, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Homeownership signals stability and privilege, whereas public housing concentrates deprivation (Oberti, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this study, ownership patterns, property valuation, and household density operationalise these mechanisms, bridging housing theory with empirical indicators.\u003c/p\u003e \u003cp\u003eThe Dutch context exemplifies these urban dynamics. In cities such as Amsterdam, Rotterdam, and Utrecht, immigrant families cluster in affordable districts\u0026mdash;often exceeding 70% of residents\u0026mdash;thereby intensifying segregation (Zorlu and Latten, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Dense social networks provide solidarity but limit exposure to broader social and economic opportunities (Pinkster, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Socioeconomic advantage governs selective school access (Kuyvenhoven and Boterman, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while institutional processes such as staff stability and student composition mediate disadvantage (Kaptan and Kocabaş, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Incorporating the educational attainment of residents, this study tests whether neighbourhood-level education profiles spill over into student performance, extending metropolitan findings to a mid-sized context.\u003c/p\u003e \u003cp\u003eNeighbourhood resources shape academic outcomes through multiple pathways. Concentrations of educated adults elevate school achievement (Andersson and Subramanian, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), while low-income districts experience resource scarcity and higher dropout rates (Lupton, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). School-level practices\u0026mdash;such as vocational tracking and grade repetition\u0026mdash;often mirror spatial segmentation, reinforcing existing inequalities. Integrating neighbourhood and school indicators allows this study to disentangle how spatial and institutional mechanisms jointly reproduce disadvantage.\u003c/p\u003e \u003cp\u003eInequality also manifests through crime, cohesion, and health. Affluent districts, marked by safety and trust, foster concentration and long-term planning, whereas disadvantaged ones suffer from crime, low trust, and chronic stress, undermining attendance and learning (Hastings, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sampson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The inclusion of crime rates, traffic accidents, and perceived health indicators thus enables a multidimensional assessment of environmental (dis)advantage.\u003c/p\u003e \u003cp\u003eDespite a rich literature, neighbourhood-effects research remains dominated by metropolitan contexts. Mid-sized cities, however, present unique opportunities for spatial analysis due to their compact morphology and mixed demographic composition. By examining Zwolle\u0026mdash;a city that is spatially coherent yet socially heterogeneous\u0026mdash;this study extends neighbourhood-effects theory beyond the metropolis. It integrates the dynamics of segregation, gentrification, and urbanization into a multidomain empirical framework, reconceptualising educational inequality as a product of spatially embedded institutional mechanisms operating within the urban fabric of non-metropolitan Europe.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eThis study employed a quantitative cross-sectional design to explore the relationship between neighborhood-level urban characteristics, school-level institutional factors, and academic achievement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContext\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in Zwolle, a mid-sized city located in the eastern Netherlands with a population of approximately 130,000 (CBS, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zwolle is often considered a balanced city in terms of urban development, combining new residential areas with post-war housing districts (Gemeente Zwolle, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Unlike the more fragmented urban landscapes of Amsterdam and Rotterdam, Zwolle\u0026rsquo;s spatial planning emphasizes integrated neighborhood development and social cohesion (RIVM, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, disparities persist between neighborhoods, particularly in terms of income levels, housing types, and access to educational facilities.\u003c/p\u003e\n\u003ch3\u003eData Set\u003c/h3\u003e\n\u003cp\u003eThe dataset was compiled from two sources. Academic achievement, the dependent variable, was measured using \u0026ldquo;fundamental\u0026rdquo; and \u0026ldquo;target\u0026rdquo; indicators from the URL scholenopdekaart.nl. The fundamental level represents the basic proficiency in language and arithmetic expected of all students at the end of primary education (Group 8, age 12), with a national benchmark of 85% set by the Inspectorate of Education. The target level is a more advanced standard, with thresholds adjusted for each school\u0026rsquo;s student population and used as signaling values for the overall performance.\u003c/p\u003e \u003cp\u003eFor each school, the percentage of students reaching fundamental and target levels served as dependent variables. Schools were identified by filtering for \u0026ldquo;primary school\u0026rdquo; in Zwolle on the website scholenopdekaart.nl. The independent variables were drawn from neighborhood-level data from the open-source data repository allecijfers.nl (2022), ensuring consistency. Guided by the literature on urbanization and the OECD Better Life Index, 25 indicators were selected and grouped into six domains: economy, demographics, security, health, education, and energy consumption.\u003c/p\u003e \u003cp\u003eThe final dataset includes 42 primary schools across 26 neighborhoods in Zwolle. Seven schools were excluded due to missing data, and those classified as \u003cem\u003eVoortgezet speciaal onderwijs\u003c/em\u003e (special education) were also omitted. Each school represents one observation in the analysis.\u003c/p\u003e \u003cp\u003eSchool-level variables include total enrollment, grade repetition, staff distribution by contract (full-time, part-time, less than half-time), share of temporary staff, and student enrollment across secondary education tracks: \u003cem\u003ePraktijkonderwijs\u003c/em\u003e (practical education), \u003cem\u003eBeroeps\u003c/em\u003e and \u003cem\u003eKader onderwijs\u003c/em\u003e (lower vocational education), VMBO (vocational middle education),, HAVO (higher general education),, and VWO (pre-university education). In the Dutch system, students are placed in tracks based on their performance and assessments. These variables capture institutional and socioeconomic disparities across schools.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eTo capture academic achievement more comprehensively, we used two dependent variables: the fundamental level, representing the basic proficiency attained by approximately 85% of students, and the target level, reflecting higher performance expectations. This dual structure makes it possible to detect variation that would be obscured by relying only on minimum benchmarks or centralized exam data.\u003c/p\u003e \u003cp\u003eThe analysis was conducted in three steps. First, stepwise regression was used to test the relationship between achievement and neighborhood indicators. Second, the same method was used to assess school-level factors, including student composition and staff conditions. Finally, hierarchical regression was used to examine their combined effects, allowing us to identify whether neighborhood or school-level variables were more predictive. If neighborhood indicators lost significance once school variables were introduced, this suggested that their influence operated through school mechanisms such as composition, practices, or resources. Conversely, if school variables lost significance after adding neighborhood indicators, it implied that structural residential conditions were more decisive.\u003c/p\u003e \u003cp\u003eStepwise regression used forward selection to identify the strongest predictors among many urban and institutional variables (Draper and Smith, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Field, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Hierarchical regression entered variables in blocks to clarify how their explanatory roles shifted when they were considered together (Field, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). All statistical assumptions were tested: tolerance (\u0026gt;\u0026thinsp;.20) and VIF (\u0026lt;\u0026thinsp;10) confirmed no multicollinearity, Durbin\u0026ndash;Watson values (1\u0026ndash;3) excluded autocorrelation, and Mahalanobis Distance identified outliers (Cook and Weisberg, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Craney and Surles, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Two non-normally distributed variables, total registered crime and the percentage of students in \u003cem\u003ePraktijkonderwijs\u003c/em\u003e, were excluded.\u003c/p\u003e \u003cp\u003eDescriptive statistics and open-access data from allecijfers.nl were used to address the fourth research question. As all data were anonymized and publicly available, no formal ethical approval was required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessing the Impact of Neighborhood Factors on Fundamental Academic Achievement:\u003c/h2\u003e \u003cp\u003eTo understand the relationship between neighborhood effects and education, the level of achievement of basic educational goals was first examined in relation to the neighborhood's urban indicators using stepwise linear regression analysis. The model indicated that the share of migrants in the total population significantly predicted the fundamental achievement level, explaining about 17% of the variance without autocorrelation (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;2). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the coefficients related to this model.\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\u003eFundamental Level and Neighborhood Characteristics Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99,998\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe share of migrants in the total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3,197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, among the urban variables related to neighborhoods, the only variable associated with fundamental-level achievement was the proportion of migrants in the total neighborhood population. As the proportion of migrants in a neighborhood's total population increases, the rate of reaching fundamental-level goals tends to decrease (\u003cem\u003eB\u003c/em\u003e = -,438; \u003cem\u003ep\u003c/em\u003e = ,003).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSchool-Level Indicators and Fundamental Achievement Rates\u003c/h3\u003e\n\u003cp\u003eIn the second round of analyses, the relationship between school-level characteristics and fundamental achievement rates was examined. Results showed that the percentage of students enrolled in lower vocational education significantly predicted fundamental achievement levels, accounting for about 10% of the variance, with no autocorrelation detected (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;2). The coefficients of the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eStepwise Regression Coefficients for Fundamental Level and School indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98,349\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% lower VET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003ea. Dependent Variable: Fundamental Level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, the proportions of students graduating from primary schools in 2022 who continued to different types of secondary education were examined. The analysis indicates that in schools with a high number of students continuing to lower-level vocational education the following year, there tends to be a decrease in the rate of achieving fundamental educational goals (\u003cem\u003eB\u003c/em\u003e = -,353; \u003cem\u003ep\u003c/em\u003e =,022). This result reveals a statistically significant difference in the rates at which schools send students to lower-level vocational and technical education, with some schools having a higher number of students continuing in these programs than others.\u003c/p\u003e\n\u003ch3\u003eIntegrating Neighborhood and School Factors: Hierarchical Regression Analysis of Fundamental Educational Achievements\u003c/h3\u003e\n\u003cp\u003eIn the final analysis of achieving fundamental educational goals, a hierarchical regression model was conducted using the previously significant predictors: the proportion of immigrants in the total neighborhood population and the percentage of students enrolled in lower-level vocational education. The first step of the model explained around 15% of the variance, and adding the school-level variable increased the explained variance to about 17%, though the change was not statistically significant; no autocorrelation was observed (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;2.1). A significant change was observed in the level of statistical significance in the second model. The Durbin-Watson score indicates that there is no issue of autocorrelation in the model. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the model\u0026rsquo;s coefficients.\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\u003eCoefficients of Hierarchical Regression Analysis of Fundamental Level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98,368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99,932\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe share of migrants in the total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98,549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100,114\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe share of migrants in the total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% beroeps\u003c/p\u003e \u003cp\u003ekader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003ea. Dependent Variable: Fundamenteel niveau\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the variable representing the proportion of students continuing to low-level vocational education loses its statistical significance (\u003cem\u003eB\u003c/em\u003e= -,223; \u003cem\u003ep\u003c/em\u003e = ,156) when examined alongside the proportion of immigrants in the total neighborhood population. This indicates a stronger relationship between the proportion of immigrants in neighborhoods and the rates of achieving fundamental educational goals (\u003cem\u003eB\u003c/em\u003e = -,326; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,039). This suggests that as the proportion of immigrants in a neighborhood increases, the rate of achieving basic educational goals in schools tends to be lower.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUrban Variables and Their Impact on the Percentage of Students Achieving Target Level\u003c/h2\u003e \u003cp\u003eIn this section, the relationship between the percentage of students achieving the target level and the urban indicators of neighborhoods was examined using stepwise linear regression. The final model, including the number of households, business establishments, and residents\u0026rsquo; education level, accounted for roughly 31% of the variance in target-level achievement, with no autocorrelation detected (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;1.95). The coefficients related to the analysis results are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eCoefficients of Stepwise Analysis For Target Level with Nieghborhood Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60,746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74,006\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73,582\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness Establishments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64,124\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5,282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness Establishments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5,497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation Level of Residents Aged 15 to 75: Middle Education Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003ea. Dependent Variable: Target Level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, as the number of households in a neighborhood increases, the proportion of students achieving the target level in schools tends to decrease. In contrast, as the number of businesses and individuals with intermediate education levels in a neighborhood increases, the proportion of students reaching the target level tends to rise. The variance inflation factor and tolerance values indicated that there was no multicollinearity among the variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalyzing School Indicators in Relation to the Proportion of Students Achieving Target Levels\u003c/h2\u003e \u003cp\u003eIn this section, the relationship between the proportion of students achieving target levels and school characteristics was analyzed using stepwise linear regression. The final model, which included the percentage of students continuing in lower vocational education and those repeating a year, explained about 41% of the variance in target-level achievement, with no indication of autocorrelation (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;2.29). The coefficients of the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of Stepwise regression of Target Level with School Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71,864\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower level VET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70,548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65,957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75,139\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Level VET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudents repeating a year (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003ea. Dependent Variable: Streefniveau\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals that a higher proportion of students repeating grades and a higher proportion of students continuing to lower-level vocational education in a school are associated with a lower tendency to achieve the target level of students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDecoding Success: Hierarchical Insights into Student Achievement and Neighborhood-School Dynamics\u003c/h2\u003e \u003cp\u003eIn this section, hierarchical regression analysis was conducted on the statistically significant neighborhood and school variables identified in the previous analyses. The combined model\u0026mdash;including the number of households, business establishments, residents\u0026rsquo; education level, and school-level indicators such as vocational tracking and grade repetition\u0026mdash;explained about 47% of the variance in target-level achievement, with no sign of autocorrelation (Durbin\u0026ndash;Watson\u0026thinsp;\u0026asymp;\u0026thinsp;2.31). The coefficients of the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of Hierarchical Regression Analysis of Target Level with Neighborhood and School Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95,0% Confidence Interval for B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZero-order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66,543\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4,971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation Level of Residents Aged 15 to 75: Middle Education Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness establishments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38,711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85,165\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\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation Level of Residents Aged 15 to 75: Middle Education Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness establishments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8,843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epercentage of students continuing lower-level vocational education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of students repeating a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003ea. Dependent Variable: target level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen urban indicators related to neighborhoods and school characteristics are examined, variables such as the proportion of individuals with intermediate education, the number of businesses, and the rate of students repeating grades lose statistical significance. The final model suggests that a higher number of households in a neighborhood is associated with a decrease in the number of students achieving the target level in schools within that neighborhood, and a higher proportion of students continuing low-level vocational education in a school is associated with a lower rate of students achieving the target level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVisualizing Analysis Results on the Map: Spatial Reflections of Educational and Urban Indicators\u003c/h2\u003e \u003cp\u003eTo visualize the distribution of neighborhoods according to the number of households, the proportion of students continuing in low-level vocational education, and the share of migrants in the population, an interactive 3D graph was developed.\u003csup\u003e1\u003c/sup\u003e. The neighborhoods of Milligen and Holtenbroek IV stand out negatively compared to other neighborhoods. Milligen, which had a population of 7,110 in 2013, decreased to 6,900 by 2024. The average house prices in this neighborhood were 300,000 Euros in 2022, rising to 347,000 Euros by 2023. The average house sale price in Zwolle in 2022 was 314,285 Euros. In Milligen, 70% of the houses are owner-occupied, 12% are rented, and 18% are under woningcorporaties \u003csup\u003e2\u003c/sup\u003e status. Most of the houses in this area were built between 1990 and 2020, with 32.7% being apartments, 46.8% semi-detached houses, and 15.7% corner houses. Key features of Milligen include a high percentage of houses built after 2000, a high number of cars per surface area, a high population density per square kilometer, a relatively long average distance to cafes, and better access to public services such as government, education, and healthcare compared to other areas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://allecijfers.nl/\u003c/span\u003e\u003cspan address=\"https://allecijfers.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHoltenbroek IV, which had a population of 3,170 in 2013, experienced a 0.47% increase, reaching 3,185 people by 2024. In this neighborhood, 62% of the homes are under the social housing status, with 24% owner-occupied and only 14% rented homes. The average house sale price in 2022 was 190,000 Euros, rising to approximately 220,000 Euros in 2023. In Holtenbroek IV, 78.3% of the homes are apartments, and 12.8% are semi-detached houses, most of which were built between 1950 and 1970. This neighborhood's average income per person is 22,400 Euros, compared to 30,100 Euros in Milligen. The average annual income per person for all neighborhoods included in the study is 29,345 Euros (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://allecijfers.nl/\u003c/span\u003e\u003cspan address=\"https://allecijfers.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sharp increase in housing prices and the dominance of newer, post-2000 housing stock in Milligen suggest a population movement toward more affordable areas, such as Holtenbroek IV. This trend is also reflected in shifting school demographics: the population of Triomundo School in the Milligen neighborhood has been gradually decreasing, from 500 students in 2012\u0026ndash;2013 to 105 students in 2023\u0026ndash;2024. In the Holtenbroek IV neighborhood, the Toonladder School has been experiencing a gradual increase in its student population. The school, which had approximately 150 students in the 2011\u0026ndash;2012 academic year, had 373 students in the 2023\u0026ndash;2024 academic year. These enrollment changes reflect broader residential and socioeconomic transitions at the neighborhood level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe map in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates that schools with a comparatively higher academic success rate ( black oval ) are established in the southern part of the city. In contrast, the schools (in a red oval) on the northern side have a comparatively lower academic achievement level. The map also exhibits movement from the western side to the eastern part of the city.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how neighborhood indicators and school characteristics shape fundamental and target academic achievement in the mid-sized Dutch city of Zwolle. While previous research has focused on large metropolitan contexts, our aim was to provide a granular view of how inequalities unfold in a spatially balanced mid-sized city. Grounded in neighborhood effects theory (Dietz, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Galster, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sampson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the findings show how spatial and institutional dynamics reproduce inequality locally. Applying this lens to Zwolle reveals whether mechanisms identified in metropolitan areas\u0026mdash;segregation, tracking, and institutional sorting\u0026mdash;also operate in more coherent, less fragmented urban contexts. As Havighurst (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) and Jenkins (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) note, even smaller cities can generate unequal access to education when zoning and investment favor particular neighborhoods. These results show how spatial reproduction functions within a compact urban regime where institutional and spatial mechanisms converge more tightly than in larger cities.\u003c/p\u003e \u003cp\u003eAlthough much research has examined major urban centers such as Amsterdam, Rotterdam, and Paris\u0026mdash;where density, diversity, and migration flows are more complex\u0026mdash;this study demonstrates that similar mechanisms operate in mid-sized cities like Zwolle. Yet, important contrasts emerge. In Amsterdam, parental choice intersects with sharp ethnic and socioeconomic divisions, creating stratified school landscapes (Boterman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boterman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, Zwolle\u0026rsquo;s smaller scale and coherent morphology allow clearer links between neighborhood stratification and school outcomes. Unlike the fragmented geography of large cities, Zwolle illustrates how even moderate variations in household density or business presence shape school performance. Together, these findings highlight that urban scale\u0026mdash;not density or diversity alone\u0026mdash;structures the reproduction of educational inequality.\u003c/p\u003e \u003cp\u003eOur results align with Boterman et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in identifying spatial sorting but show that in smaller cities these processes are more directly tied to urban form and institutional access, such as employment hubs and school types. The presence of highly educated adults reinforces localized educational capital and aspiration, a dynamic also observed by Andersson and Subramanian (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeighborhood mechanisms\u0026mdash;particularly opportunity structures and exposure\u0026mdash;remain central to educational outcomes. Migrant concentration and household density are negatively associated with achievement (Galster, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), echoing Sampson et al.\u0026rsquo;s (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) concept of institutional strain in dense areas. The reinforcing effect of spatial homogeneity, whether ethnic or socioeconomic, is equally significant. As Karsten et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Oberti (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) note, homogeneous neighborhoods restrict institutional diversity and limit exposure to wider networks and resources.\u003c/p\u003e \u003cp\u003eA further contribution is the multidimensional operationalization of neighborhood effects. Whereas most studies rely on single measures like socioeconomic status or ethnic concentration (Pinkster, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillimore and Goodson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), this study used 25 indicators across six domains. The significance of household density and business establishments indicates that inequality cannot be reduced to poverty or ethnicity alone. Instead, economic vitality, housing, health, security, and demographic transitions jointly shape opportunity. This multidomain approach expands the analytical scope of neighborhood-effects research, showing that inequality in mid-sized cities follows its own spatial logic rather than simply replicating metropolitan disadvantage.\u003c/p\u003e \u003cp\u003eA methodological contribution lies in disaggregating academic performance into fundamental and target levels, revealing how inequality operates across thresholds. At the fundamental level, migrant concentration predicts attainment, reflecting Galster\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) exposure mechanisms and Sampson et al.\u0026rsquo;s (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) institutional disorganization. At the target level, this relationship disappears; instead, household density, parental education, and business density become decisive. This partly contrasts with Sykes (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who minimized ethnic composition effects, suggesting that clustering influences minimum benchmarks, while higher achievement depends on socioeconomic capital and institutional proximity. These findings also reflect Wilson\u0026rsquo;s (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) notion of concentrated poverty, where deprivation constrains responsiveness and mobility.\u003c/p\u003e \u003cp\u003eBy distinguishing between thresholds, the study addresses a gap in research that often treats performance as a single outcome. The finding that migrant concentration constrains basic proficiency while other indicators drive higher attainment demonstrates that mechanisms vary by performance level. This contrasts with Kuyvenhoven and Boterman (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who emphasized school-level effects, and complements Boterman et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who stressed spatial and institutional contexts. The results also echo Andersson and Subramanian (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), refining how socio-cultural capital and demographic stability function across achievement levels.\u003c/p\u003e \u003cp\u003eConsistent with Bauder (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Lupton (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and Kuyvenhoven and Boterman (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our data show that schools serving disadvantaged neighborhoods tend to have higher shares of students entering lower vocational tracks and repeating grades. These sorting mechanisms are key pathways through which spatial disadvantage becomes educational disadvantage. Lower vocational tracking negatively correlates with both fundamental and target levels, and its influence strengthens at higher achievement thresholds, confirming that early pathways constrain long-term opportunity.\u003c/p\u003e \u003cp\u003eThe spatial clustering of underperformance in Zwolle parallels findings by Boterman (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Boterman et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on segregation in Dutch cities despite parental choice. Although smaller than Amsterdam, Zwolle\u0026rsquo;s north\u0026ndash;south divide and stratification suggest similar dynamics. These patterns also align with Pinkster and Boterman (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Zorlu and Latten (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), who linked zoning and housing to inequality. Zwolle\u0026rsquo;s compact geography and centralized planning clarify these links: high-density neighborhoods\u0026mdash;often social housing blocks\u0026mdash;limit access to employment and commercial centers, while areas with greater business density are more affluent and connected. This explains the role of business establishments in predicting target-level achievement.\u003c/p\u003e \u003cp\u003ePrior research has shown how housing and school systems reinforce exclusion (Francis and Hutchings, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hamnett and Butler, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Although residential mobility or enrollment zones were not directly assessed, the spatial distribution of school performance suggests implicit selection and self-sorting. The absence of correlation in newer, affluent areas such as Milligen challenges Zorlu and Latten\u0026rsquo;s (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) assumptions and supports Logan\u0026rsquo;s (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) argument that inequality is not confined to visibly deprived areas. Simultaneously, Milligen may signal early gentrification, as demographic change reshapes educational demand and composition (Forster, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uitermark, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile school-level effects like vocational tracking strongly predicted outcomes, their reduced significance in hierarchical models suggests partial endogeneity to neighborhood dynamics. This interpretation aligns with Galster et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), who argued that neighborhood context shapes outcomes through institutional settings. The results also support aspiration-based mechanisms (Kauppinen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and the social capital constraints noted by Pinkster (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Environmental conditions such as crime and chronic stress further undermine achievement in disadvantaged areas (Hastings, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond analytical contributions, these findings raise issues of spatial justice and governance. Addressing educational inequality requires more than school-level reform; it demands integrated urban\u0026ndash;educational planning that targets neighborhood disadvantage. Spatial justice depends on aligning housing, mobility, and education policies to redistribute opportunity structures within mid-sized cities.\u003c/p\u003e \u003cp\u003eOverall, this study extends neighborhood-effects research to a mid-sized city (Boterman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boterman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), operationalizes disadvantage through multidomain indicators (Pinkster, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillimore and Goodson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uitermark, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and differentiates between fundamental and target thresholds (Kuyvenhoven and Boterman, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Comparing these findings with metropolitan research shows that while large Dutch cities like Amsterdam and Rotterdam exhibit complex, multi-ethnic segregation, Zwolle reveals how similar inequalities are reproduced through more direct spatial mechanisms. Mid-sized cities thus not only mirror but recalibrate metropolitan processes, offering a sharper view of how local urban forms and institutional structures jointly reproduce educational inequality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined how neighbourhood-level indicators and school-level factors shape academic performance in the mid-sized Dutch city of Zwolle, using fundamental and target achievement as outcomes. The analysis shows how spatial and institutional dynamics intersect to reproduce educational inequality within a compact yet stratified urban setting.\u003c/p\u003e \u003cp\u003eAt the neighbourhood scale, migrant concentration and household density were linked to lower scores, while the number and educational level of adult residents predicted higher achievement. These results confirm that exposure to spatial disadvantage and local educational capital remain key mechanisms shaping student outcomes.\u003c/p\u003e \u003cp\u003eAt the institutional level, school composition\u0026mdash;particularly the share of students in lower vocational tracks and grade repetition\u0026mdash;was associated with lower performance, especially at the target level. Yet when neighbourhood and school factors were analysed together, school effects weakened, indicating that institutional disparities are embedded in broader spatial contexts.\u003c/p\u003e \u003cp\u003eBy disaggregating outcomes into fundamental and target thresholds, this study clarifies how mechanisms differ across achievement levels: migrant concentration constrains minimum proficiency, whereas socioeconomic and infrastructural factors drive higher attainment. Beyond analysis, the findings call for integrated urban\u0026ndash;educational strategies linking housing, mobility, and community investment to advance spatial justice. Mid-sized cities, as compact yet stratified regimes, thus offer critical insights into how local forms and institutions jointly reproduce educational inequality.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Implications\u003c/h2\u003e \u003cp\u003eThe findings reveal that migrant concentration in schools and neighborhoods exerts a constitutive effect on educational quality, necessitating governance measures that more equitably redistribute populations and resources. Without such intervention, disadvantage risks becoming structurally entrenched and spatially reproduced across generations.\u003c/p\u003e \u003cp\u003eDivergences in student trajectories underscore the need for targeted measures\u0026mdash;such as interschool exchanges, enrichment programmes for lower-achieving pupils, and stronger alignment between educational guidance and labour-market demands. In Zwolle, this requires embedding educational and vocational pathways in the city\u0026rsquo;s evolving economy while resisting the reproduction of low-expectation trajectories.\u003c/p\u003e \u003cp\u003eGentrification is reconfiguring neighborhood demographics, threatening both displacement and the consolidation of concentrated poverty. Mitigation demands systematic socio-spatial monitoring and proactive housing regulation, including rent controls, the preservation of affordable stock, and the integration of social housing within mixed-tenure developments.\u003c/p\u003e \u003cp\u003eConsidered holistically, these implications affirm that reducing educational inequality requires interventions beyond the school domain, extending into the governance of urban space. Mid-sized cities such as Zwolle\u0026mdash;with their compact geographies and clear institutional interfaces\u0026mdash;offer earlier and more direct policy levers than metropolitan contexts, making them crucial laboratories for integrative urban\u0026ndash;educational strategies. These recommendations align with the principles of spatial justice and inclusive urban governance, recognising that educational equity must be pursued as a central dimension of urban policy.\u003c/p\u003e \u003cp\u003eThis study conceptualises mid-sized cities as spatial regimes of inequality, where educational opportunity is structured and reproduced through the interdependence of housing, institutional composition, and local governance. Embedding equity-oriented planning within these compact urban regimes thus represents a key frontier for achieving spatial justice in education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Delimiters\u003c/h2\u003e \u003cp\u003eThe first limitation of this study is its cross-sectional design. Although more recent urban data were available, the most up-to-date school-level data pertained to 2022. For this reason, the analyses were confined to the year 2022 to ensure consistency across datasets and to maintain feasibility during the model-building phase, given the preliminary nature of the study. The results suggest that a longitudinal study could provide deeper insights into the process of urbanization. The second limitation is that it is a quantitative study that generalizes based on the findings related to schools. Schools may experience changes in their academic levels due to factors other than those identified in this study. Finally, the study was limited to Zwolle. Another limitation of this study is that it only included primary schools. Due to factors such as teacher employment conditions, school preferences of students and their families, psychological factors linked to the age characteristics of students, and differences in academic achievement expectations, conducting a similar study in secondary schools can provide deeper insights into neighborhood effects and school characteristics. The results demonstrate that urban factors and neighborhood effects are related to academic success at the micro level. Therefore, applying the same research methodology to examine neighborhoods in all cities in the Netherlands and other countries could provide more in-depth information about cities with different urbanization narratives.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eO.K. conceived the study, designed the research, collected and analysed the data, and wrote the main manuscript text. S.L.G. contributed to the conceptual development of the study, supported the interpretation of the findings, and critically revised the manuscript. E.J.V. contributed to the research design, supported the interpretation of the results, and critically revised the manuscript. M.K. contributed to the methodological framing of the study, supported the interpretation of the findings, and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study were obtained from the AlleCijfers.nl and Scholen op de Kaart (scholenopdekaart.nl) databases, and the resulting dataset is provided as supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAinsworth, J.W.: Why does it take a village? The mediation of neighborhood effects on educational achievement. Soc. Forces. \u003cb\u003e81\u003c/b\u003e(1), 117\u0026ndash;152 (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1353/sof.2002.0038\u003c/span\u003e\u003cspan address=\"10.1353/sof.2002.0038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlleCijfers.nl: \u003cem\u003eWijk- en buurtcijfers Zwolle.\u003c/em\u003e (2024). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://allecijfers.nl\u003c/span\u003e\u003cspan address=\"https://allecijfers.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 09 September 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson, E., Subramanian, S.V.: Explorations of neighbourhood and educational outcomes for young Swedes. Urban Stud. \u003cb\u003e43\u003c/b\u003e(11), 2013\u0026ndash;2025 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00420980600897834\u003c/span\u003e\u003cspan address=\"10.1080/00420980600897834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauder, H.: You\u0026rsquo;re good with your hands, why don\u0026rsquo;t you become an auto mechanic\u0026rsquo;: Neighborhood context, institutions and career development. Int. J. Urban Reg. Res. \u003cb\u003e25\u003c/b\u003e(3), 593\u0026ndash;608 (2001). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1468-2427.00331\u003c/span\u003e\u003cspan address=\"10.1111/1468-2427.00331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerliner, D.C.: Effects of inequality and poverty vs. teachers and schooling on America\u0026rsquo;s youth. Teachers Coll. Record. \u003cb\u003e115\u003c/b\u003e(12), 1\u0026ndash;26 (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoterman, W., Musterd, S., Pacchi, C., Ranci, C.: School segregation in contemporary cities: Socio-spatial dynamics, institutional context and urban outcomes. Urban Stud. \u003cb\u003e56\u003c/b\u003e(15), 3055\u0026ndash;3073 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0042098019868377\u003c/span\u003e\u003cspan address=\"10.1177/0042098019868377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoterman, W.R.: The role of geography in school segregation in the free parental choice context of Dutch cities. Urban Stud. \u003cb\u003e56\u003c/b\u003e(15), 3074\u0026ndash;3094 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0042098019832201\u003c/span\u003e\u003cspan address=\"10.1177/0042098019832201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoterman, W.R.: School choice and school segregation in the context of gentrifying Amsterdam. Hous. Stud. \u003cb\u003e37\u003c/b\u003e(5), 720\u0026ndash;741 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02673037.2020.1829563\u003c/span\u003e\u003cspan address=\"10.1080/02673037.2020.1829563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCBS: \u003cem\u003eKerncijfers wijken en buurten 2023 \u0026ndash; Zwolle.\u003c/em\u003e Centraal Bureau voor de Statistiek. (2023). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbs.nl\u003c/span\u003e\u003cspan address=\"https://www.cbs.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 12 September 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook, R.D., Weisberg, S.: Residuals and influence in regression. Chapman and Hall, New York (1982)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraney, T.A., Surles, J.G.: Model-dependent variance inflation factor cutoff values. Qual. Eng. \u003cb\u003e14\u003c/b\u003e(3), 391\u0026ndash;403 (2002)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrowder, K., South, S.J.: Neighborhood distress and school dropout: The variable significance of community context. Soc. Sci. Res. \u003cb\u003e32\u003c/b\u003e(4), 659\u0026ndash;698 (2003). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0049-089X(03)00035-8\u003c/span\u003e\u003cspan address=\"10.1016/S0049-089X(03)00035-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDietz, R.D.: The estimation of neighborhood effects in the social sciences: An interdisciplinary approach. Soc. Sci. Res. \u003cb\u003e31\u003c/b\u003e(4), 539\u0026ndash;575 (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0049-089X(02)00005-4\u003c/span\u003e\u003cspan address=\"10.1016/S0049-089X(02)00005-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDraper, N.R., Smith, H.: Applied regression analysis, 1st edn. Wiley, New York (1998). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9781118625590\u003c/span\u003e\u003cspan address=\"10.1002/9781118625590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncan, G.J.: Families and neighbors as sources of disadvantage in the schooling decisions of white and black adolescents. Am. J. Educ. \u003cb\u003e103\u003c/b\u003e(1), 20\u0026ndash;53 (1994). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/444088\u003c/span\u003e\u003cspan address=\"10.1086/444088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eField, A.: Discovering statistics using SPSS. Sage, London (2005)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForster, C.: The challenge of change: Australian cities and urban planning in the new millennium. Geographical Res. \u003cb\u003e44\u003c/b\u003e(2), 173\u0026ndash;182 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1745-5871.2006.00374.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1745-5871.2006.00374.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancis, B., Hutchings, M.: \u003cem\u003eParent power? Using money and information to boost children\u0026rsquo;s chances of educational success.\u003c/em\u003e London: Sutton Trust. (2013). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eric.ed.gov/?id=ED594808\u003c/span\u003e\u003cspan address=\"https://eric.ed.gov/?id=ED594808\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 22 October 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalster, G.C.: The mechanism(s) of neighbourhood effects: Theory, evidence, and policy implications. In: Van Ham, M., Manley, D., Bailey, N., Simpson, L., Maclennan, D. (eds.) Neighbourhood effects research: New perspectives, pp. 23\u0026ndash;56. Springer Netherlands, Dordrecht (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-007-2309-2_2\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-2309-2_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalster, G., Marcotte, D.E., Mandell, M., Wolman, H., Augustine, N.: The influence of neighborhood poverty during childhood on fertility, education, and earnings outcomes. Hous. Stud. \u003cb\u003e22\u003c/b\u003e(5), 723\u0026ndash;751 (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02673030701474669\u003c/span\u003e\u003cspan address=\"10.1080/02673030701474669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGemeente Zwolle: \u003cem\u003eStadsmonitor Zwolle 2022.\u003c/em\u003e (2022). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.zwolle.nl\u003c/span\u003e\u003cspan address=\"https://www.zwolle.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 14 October 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGemeente Zwolle: \u003cem\u003eOnderzoek naar de toekomst van Regio Zwolle 2040.\u003c/em\u003e (2023). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.zwolle.nl/onderzoek-naar-de-toekomst-van-regio-zwolle-2040\u003c/span\u003e\u003cspan address=\"https://www.zwolle.nl/onderzoek-naar-de-toekomst-van-regio-zwolle-2040\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 14 October 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamnett, C., Butler, T.: Geography matters\u0026rsquo;: The role distance plays in reproducing educational inequality in East London. Trans. Inst. Br. Geogr. \u003cb\u003e36\u003c/b\u003e(4), 479\u0026ndash;500 (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1475-5661.2011.00444.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1475-5661.2011.00444.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey, D.: Rebel cities: From the right to the city to the urban revolution. Verso, London (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHastings, A.: Neighbourhood environmental services and neighbourhood \u0026lsquo;effects\u0026rsquo;: Exploring the role of urban services in intensifying neighbourhood problems. Hous. Stud. \u003cb\u003e24\u003c/b\u003e(4), 503\u0026ndash;524 (2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02673030902938389\u003c/span\u003e\u003cspan address=\"10.1080/02673030902938389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavighurst, R.J.: Urbanization and education in the United States. Int. Rev. Educ.: 393\u0026ndash;409. (1967)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkins, P.: Urbanization, urbanism, and urbanity in an African city: Home spaces and house cultures. Springer, Dordrecht (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaptan, O., Kocabaş, İ.: Neighborhoods and schools: The socio-spatial dynamics of educational achievement in Amsterdam. Educational Process. Int. J. \u003cb\u003e15\u003c/b\u003e(1) (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22521/edupij.2025.149\u003c/span\u003e\u003cspan address=\"10.22521/edupij.2025.149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarsten, S., Felix, C., Ledoux, G., Meijnen, W., Roeleveld, J., Van Schooten, E.: Choosing segregation or integration? The extent and effects of ethnic segregation in Dutch cities. Educ. Urban Soc. \u003cb\u003e38\u003c/b\u003e(2), 228\u0026ndash;247 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0013124505282606\u003c/span\u003e\u003cspan address=\"10.1177/0013124505282606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKauppinen, T.M.: Neighborhood effects in a European city: Secondary education of young people in Helsinki. Soc. Sci. Res. \u003cb\u003e36\u003c/b\u003e(1), 421\u0026ndash;444 (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ssresearch.2006.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ssresearch.2006.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, C.: Cutoff values for Cook\u0026rsquo;s distance. Commun. Stat. Appl. Methods. \u003cb\u003e3\u003c/b\u003e(2), 13\u0026ndash;19 (1996)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuyvenhoven, J., Boterman, W.R.: Neighbourhood and school effects on educational inequalities in the transition from primary to secondary education in Amsterdam. Urban Stud. \u003cb\u003e58\u003c/b\u003e(13), 2660\u0026ndash;2682 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogan, J.R.: As long as there are neighborhood. City Community. \u003cb\u003e15\u003c/b\u003e(1), 23\u0026ndash;28 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cico.12149\u003c/span\u003e\u003cspan address=\"10.1111/cico.12149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLupton, R.: Social justice and school improvement: Improving the quality of schooling in the poorest neighbourhoods. Br. Edu. Res. J. \u003cb\u003e31\u003c/b\u003e(5), 589\u0026ndash;604 (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01411920500240759\u003c/span\u003e\u003cspan address=\"10.1080/01411920500240759\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassey, D.: Space, place and gender. University of Minnesota Press, Minneapolis (1994)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassey, D.S., Fischer, M.J.: The effect of childhood segregation on minority academic performance at selective colleges. Ethnic Racial Stud. \u003cb\u003e29\u003c/b\u003e(1), 1\u0026ndash;26 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01419870500351159\u003c/span\u003e\u003cspan address=\"10.1080/01419870500351159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOberti, M.: Social and school differentiation in urban space: Inequalities and local configurations. Environ. Plann. A: Econ. Space. \u003cb\u003e39\u003c/b\u003e(1), 208\u0026ndash;227 (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1068/a39159\u003c/span\u003e\u003cspan address=\"10.1068/a39159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillimore, J., Goodson, L.: Problem or opportunity? Asylum seekers, refugees, employment and social exclusion in deprived urban areas. Urban Stud. \u003cb\u003e43\u003c/b\u003e(10), 1715\u0026ndash;1736 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00420980600838606\u003c/span\u003e\u003cspan address=\"10.1080/00420980600838606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinkster, F.M.: Localised social networks, socialisation and social mobility in a low-income neighbourhood in the Netherlands. Urban Stud. \u003cb\u003e44\u003c/b\u003e(13), 2587\u0026ndash;2603 (2007). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00420980701558384\u003c/span\u003e\u003cspan address=\"10.1080/00420980701558384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinkster, F.M., Boterman, W.R.: When the spell is broken: Gentrification, urban tourism and privileged discontent in the Amsterdam canal district. Cult. Geographies. \u003cb\u003e24\u003c/b\u003e(3), 457\u0026ndash;472 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1474474017706176\u003c/span\u003e\u003cspan address=\"10.1177/1474474017706176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRIVM: \u003cem\u003eLeefomgeving en gezondheid in Zwolle \u0026ndash; GezondStad verkenning.\u003c/em\u003e Rijksinstituut voor Volksgezondheid en Milieu. (2022). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rivm.nl\u003c/span\u003e\u003cspan address=\"https://www.rivm.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 14 October 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson, R.J., Morenoff, J.D., Gannon-Rowley, T.: Assessing \u0026lsquo;neighborhood effects\u0026rsquo;: Social processes and new directions in research. Ann. Rev. Sociol. \u003cb\u003e28\u003c/b\u003e(1), 443\u0026ndash;478 (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.soc.28.110601.141114\u003c/span\u003e\u003cspan address=\"10.1146/annurev.soc.28.110601.141114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScholenopdekaart.nl: \u003cem\u003eOnderwijsgegevens van scholen in Zwolle.\u003c/em\u003e (2024). Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scholenopdekaart.nl\u003c/span\u003e\u003cspan address=\"https://scholenopdekaart.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 09 September 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSykes, B.: Spatial order and social position: Neighbourhoods, schools and educational inequality. Universiteit van Amsterdam, Amsterdam (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUitermark, J.: Social mixing\u0026rsquo; and the management of disadvantaged neighbourhoods: The Dutch policy of urban restructuring revisited. Urban Stud. \u003cb\u003e40\u003c/b\u003e(3), 531\u0026ndash;549 (2003). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0042098032000053905\u003c/span\u003e\u003cspan address=\"10.1080/0042098032000053905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations: The world\u0026rsquo;s cities in 2016. United Nations, New York (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18356/8519891f-en\u003c/span\u003e\u003cspan address=\"10.18356/8519891f-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan de Werfhorst, H.G.: Early tracking and social inequality in educational attainment: Educational reforms in 21 European countries. Am. J. Educ. \u003cb\u003e126\u003c/b\u003e(1), 65\u0026ndash;99 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/705500\u003c/span\u003e\u003cspan address=\"10.1086/705500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarrington, M.: Mirage in the desert? Access to educational opportunities in an area of social exclusion. Antipode. \u003cb\u003e37\u003c/b\u003e(4), 796\u0026ndash;816 (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.0066-4812.2005.00526.x\u003c/span\u003e\u003cspan address=\"10.1111/j.0066-4812.2005.00526.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson, W.J.: The truly disadvantaged: The inner city, the underclass, and public policy. University of Chicago Press, Chicago (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZorlu, A., Latten, J.: Ethnic sorting in the Netherlands. Urban Stud. \u003cb\u003e46\u003c/b\u003e(9), 1899\u0026ndash;1923 (2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0042098009106023\u003c/span\u003e\u003cspan address=\"10.1177/0042098009106023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \"To access the interactive 3D graph, please visit: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rzgrorhun.github.io/graphics/interactive2_plot.html\u003c/span\u003e\u003cspan address=\"https://rzgrorhun.github.io/graphics/interactive2_plot.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e woningcorporaties is a Dutch term referring to social housing corporations. These organizations own and manage rental properties, primarily for lower-income residents. The aim of huur-corporaties is to provide affordable housing, often at regulated rental prices, ensuring access to housing for those who may not be able to afford market-rate homes. They play a key role in the Netherlands' social housing system, supporting communities with a mix of income levels.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neighborhood effect, spatial inequality, urban segregation, gentrification, academic achievement","lastPublishedDoi":"10.21203/rs.3.rs-9270679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9270679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of urban indicators at the neighborhood level and school-level characteristics on academic performance in Zwolle, a mid-sized city in the Netherlands. Addressing a gap in existing research, which often focuses on large metropolitan areas or national datasets, this study applies stepwise and hierarchical regression analyses to data from 42 primary schools located in 26 neighborhoods. The data used in the study were obtained from the official platforms of Dutch educational and statistical institutions, namely Scholen op de Kaart and AlleCijfers. Two outcome measures were evaluated: the percentage of students who met the fundamental and the target academic benchmarks. Neighborhood indicators, categorized into six groups (economy, security, health, demography, education, and energy consumption), were represented by 25 variables. Simultaneously, school-level characteristics were captured by 13 variables concerning student demographics and staffing. The findings show that migrant concentration and household density are significant negative predictors of academic achievement. School-level factors, such as vocational tracking and grade repetition, also played a role but lost significance when analyzed alongside neighborhood variables, indicating that institutional disparities may be embedded within broader spatial dynamics. Spatial mapping revealed a clear north\u0026ndash;south divide in school performance, even in a city generally perceived as spatially balanced. This study contributes to neighborhood effects theory by demonstrating how structural and institutional mechanisms reproduce inequality in a mid-sized urban setting. This underscores the need for policies that monitor the spatial distribution of disadvantage, promote more balanced school compositions, and provide targeted support for students from marginalized neighborhoods.\u003c/p\u003e","manuscriptTitle":"Beyond the Metropolis: Neighborhood Effects and Educational Inequality in a Mid-Sized Dutch City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 17:22:31","doi":"10.21203/rs.3.rs-9270679/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"369b03fb-2771-424d-b16a-f6fcfb7fcddf","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T19:13:15+00:00","index":39,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T16:57:02+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T17:22:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 17:22:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9270679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9270679","identity":"rs-9270679","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}