CC BY
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Научная статья
УДК 316:330.5:343.2/.7:343.9
JEL: D14, D63, E24, I31, K14
https://doi.org/10.21202/2782-2923.2024.2.500-516
N. Ranaweera1
1 University of Sri Jayewardenepura, Nugegoda, Sri Lanka
Impact of Income Inequality on Fear of Crime: A Comprehensive Meta-Analysis
Nishani Ranaweera, Senior Lecturer at the Department of Criminology and Criminal
Justice, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura
University of Sri Jayewardenepura, Sri Lanka
E-mail: ranaweera@sjp.ac.lk
ORCID: http://orcid.org/0000-0002-7682-5733
Abstract
Objective: to synthesize existing research to comprehensively understand the impact of income inequality on fear of crime, aiming at elucidating how economic disparities influence community security.
Methods: qualitative methods, including comparative analysis and generalization, alongside the historical and genetic method, were employed. A systematic search was conducted across multiple databases such as PubMed, PsyclNFO, Web of Science, Scopus, and Google Scholar to aggregate peer-reviewed empirical studies. A random-effects model was used for calculating overall effect sizes, with study heterogeneity and moderating factors such as geographic location and urban versus rural settings assessed.
Results: a moderate positive relationship between income inequality and fear of crime was unveiled, particularly pronounced in urban areas, based on a wide array of empirical data. Moderate study heterogeneity indicates context-dependent variability. The synthesis of findings underscores the significant role of income inequality in exacerbating fear of crime, highlighting a complex interplay between economic disparities and perceived community security.
Scientific novelty: this study represents the first attempt to bridge the gap between theoretical assumptions of social disorganization and inequality theories with empirical evidence on the impact of income inequality on fear of crime, providing a comprehensive overview and highlighting discrepancies, patterns, and the overall strength of this association. Practical significance: the findings offer crucial insights for policymakers and community leaders in developing targeted interventions to mitigate fear of crime, emphasizing the necessity for policies that address economic disparities to enhance societal well-being. Additionally, it recommends further longitudinal research to explore the causal relationships and mechanisms underlying the association between income inequality and fear of crime, enriching the concepts of institutional economics concerning the limits and possibilities of institutional transformations in enhancing community security.
Keywords:
criminal law and criminology, community safety, economic disparities, fear of crime, income inequality, meta-analysis
The article is in Open Access in compliance with Creative Commons Attribution Non-Commercial License (https://creativecommons. org/licenses/by-nc/4.0/), stipulating non-commercial use, distribution and reproduction on any media, on condition of mentioning the article original.
For citation: Ranaweera, N. (2024). Impact of income inequality on fear of crime: a comprehensive meta-analysis. Russian Journal of Economics and Law, 18(2), 500-516. (In Russ.). https://doi.org/10.21202/2782-2923.2024.2.500-516
© Ранавира Н., 2024
Научная статья
Н. Ранавира1
1 Университет Шри-Джаяварденепура, г. Нугегода, Шри-Ланка
Влияние неравенства доходов на страх перед преступностью:
комплексный метаанализ
Нишани Ранавира, старший преподаватель кафедры криминалистики
и уголовной юстиции факультета гуманитарных и общественных наук,
Университет Шри-Джаяварденепуры
E-mail: ranaweera@sjp.ac.lk
ORCID: http://orcid.org/0000-0002-7682-5733
Аннотация
Цель: обобщение имеющихся исследований для достижения всестороннего понимания влияния неравенства доходов на страх перед преступностью и выяснения, как экономическое неравенство влияет на безопасность в обществе. Методы: использовались качественные методы, включая сравнительный анализ и обобщение, а также историко-генетический метод. Для составления подборки рецензируемых эмпирических исследований был проведен систематический поиск по нескольким базам данных, таким как PubMed, PsycINFO, Web of Science, Scopus и Google Scholar. Для расчета общей величины эффекта использовалась модель случайных эффектов, при этом оценивались неоднородность исследований и факторы, влияющие на их результаты, такие как географическое положение и соотношение городских и сельских районов.
Результаты: на основе широкого спектра эмпирических данных была выявлена умеренная положительная связь между неравенством доходов и страхом перед преступностью, особенно выраженная в городских районах. Умеренная неоднородность исследований указывает на вариативность в зависимости от контекста. Обобщенные данные свидетельствуют о значительной роли неравенства доходов в усилении страха перед преступностью и указывают на сложную взаимосвязь между экономическим неравенством и воспринимаемой безопасностью общества. Научная новизна: данное исследование представляет собой первую попытку преодолеть разрыв между теоретическими положениями теорий социальной дезорганизации и неравенства, с одной стороны, и эмпирическими данными о влиянии неравенства доходов на страх перед преступностью - с другой; в работе представлен всесторонний обзор и подчеркиваются расхождения, закономерности и общее значение связи между данными явлениями. Практическая значимость: результаты исследования могут быть полезны политикам и общественным деятелям при разработке целевых мероприятий по снижению страха перед преступностью, подчеркивая необходимость политики, направленной на устранение экономического неравенства и повышение общественного благосостояния. Кроме того, рекомендуется проведение дальнейших лонгитюдных исследований для изучения причинно-следственных связей и механизмов, определяющих взаимозависимость между неравенством доходов и страхом перед преступностью, что обогащает концепции институциональной экономики в отношении пределов и возможностей институциональных преобразований для повышения безопасности общества.
Ключевые слова:
уголовное право и криминология, общественная безопасность, экономическое неравенство, страх перед преступностью, неравенство доходов, метаанализ
Статья находится в открытом доступе в соответствии с Creative Commons Attribution Non-Commercial License (https:// creativecommons.org/licenses/by-nc/4.0/), предусматривающем некоммерческое использование, распространение и воспроизводство на любом носителе при условии упоминания оригинала статьи.
Как цитировать статью: Ранавира, Н. (2024). Влияние неравенства доходов на страх перед преступностью: комплексный метаанализ. Russian Journal of Economics and Law, 18(2), 500-516. https://doi.Org/10.21202/2782-2923.2024.2.500-516
Introduction
Income inequality, defined as the uneven distribution of income within a population, has been a subject of extensive study and debate due to its wide-ranging effects on societal well-being and cohesion. Research has consistently highlighted the potential of income inequality to exacerbate social problems, including crime and the fear of crime, which are of particular concern due to their direct impact on the quality of life and community stability (Wilkinson & Pickett, 2009; Peterson, 2017; Cheung & Lucas, 2019; Vitkovics, 2023; Romanov, 2023). The fear of crime, a psychological phenomenon reflecting individuals' concerns about becoming a victim of crime, significantly influences community interaction, social trust, and perceptions of safety, affecting people, including children, daily lives and their interactions within their communities (Hale, 1996; Lewis & Salem, 2017; Jackson et al., 2007; Vetrova, 2021; Atkinson & Blandy, 2016; Grinshteyn et al., 2016).
The objective of this meta-analysis is to synthesize existing research findings to understand better how income inequality impacts the fear of crime. This work aims to bridge the gap between the theoretical assumptions of social disorganization and inequality theories, which suggest that greater economic disparities within a society lead to higher levels of fear among its members, and the empirical evidence supporting these claims. By analyzing the collective results from numerous studies, this meta-analysis seeks to provide a comprehensive overview of the relationship between income inequality and crime fear, highlighting patterns, discrepancies, and the overall strength of this association.
A meta-analysis in this field is necessitated by the conflicting results of previous studies and a general lack of comprehensive analysis that considers the breadth of research methodologies, geographical contexts, and demographic variables explored in the literature. While some studies suggest a strong correlation between income inequality and increased fear of crime (Das & Mohapatra, 2003), others find a more nuanced relationship, with factors such as community cohesion and local crime rates playing significant roles (Stafford et al., 2007). The varied findings across studies underscore the complexity of the relationship between income inequality and fear of crime, highlighting the need for a meta-analytical approach that can aggregate these findings, account for methodological diversity, and provide a clearer understanding of this social phenomenon. Through this systematic review and meta-analysis, we aim to clarify the extent to which income inequality serves as a determinant of crime fear, offering insights that may inform policy decisions aimed at mitigating the negative societal impacts of economic disparities (Arrocha, 2019; Boldis et al., 2018; Bowman & O'Neil, 2020; Newman & Franck, 2017).
Methods
Search Strategy
To identify studies for inclusion in this meta-analysis, a comprehensive and systematic search strategy was implemented across several academic databases known for their extensive collections of social science and public health literature. The databases searched included PubMed, PsyclNFO, Web of Science, Scopus, and Google Scholar. These platforms were chosen for their wide coverage of both interdisciplinary and subject-specific research, ensuring a broad capture of relevant studies.
The search was conducted using a combination of keywords and phrases related to the core concepts of "income inequality," "fear of crime," and their synonyms to maximize the retrieval of pertinent studies. The keywords used were: ("income inequality" OR "economic disparity" OR "wealth distribution") AND ("fear of crime" OR "crime fear" OR "perceived crime risk"). Boolean operators ("AND", "OR") were employed to refine the search and capture studies that discuss the relationship between income inequality and the fear of crime. To ensure the analysis was both current and historically comprehensive, the time frame for the search was not restricted, allowing for the inclusion of studies published at any time up until the search date in March 2023. This approach was intended to capture the evolution of research on this topic over time and identify any trends or shifts in findings across different periods.
Studies were selected based on their titles and abstracts, with full texts retrieved for closer examination when the relevance was confirmed. The reference lists of identified articles were also reviewed to capture additional studies not found in the initial database search, a method known as snowballing, which helps to ensure the comprehensiveness of the search strategy. The search strategy and selection process were designed to be as inclusive as possible while maintaining a focus on studies that directly examine the relationship between income inequality and fear of crime. This methodological rigor ensures that the meta-analysis will be based on a wide-ranging and representative body of literature, providing a robust foundation for understanding the impact of income inequality on crime fear.
Selection Criteria
The selection of studies for this meta-analysis was guided by a set of predefined inclusion and exclusion criteria, aimed at identifying research that directly examines the relationship between income inequality and the fear of crime. These criteria were established to ensure the relevance, quality, and comparability of the studies included in the analysis.
Inclusion Criteria
Study Design: Peer-reviewed empirical studies, including cross-sectional, longitudinal, case-control, and cohort studies, were considered. Qualitative studies were included if they provided quantifiable data on the relationship between income inequality and fear of crime.
Population: Studies involving participants of any age, gender, and nationality were included, without restrictions on socioeconomic status or residential location, to ensure a diverse and comprehensive analysis.
Outcome Measures: Studies must have explicitly measured the fear of crime as a primary or secondary outcome, using validated instruments or methods. Additionally, studies needed to assess income inequality, either through direct measures (e.g., Gini coefficient, income ratio) or proxies (e.g., socioeconomic status disparities within communities).
Publication Date: No restrictions on the publication date were applied to capture the full scope of the research history on this topic.
Language: Studies published in English were included due to the linguistic capabilities of the research team. However, significant studies in other languages were considered if an English abstract providing sufficient data was available.
Exclusion Criteria
Study Quality: Studies with a high risk of bias, as determined by critical appraisal tools suitable for each study design, were excluded. Factors considered included the adequacy of outcome measurement, control for confounding variables, and the representativeness of the sample.
Review Articles and Editorials: Systematic reviews, meta-analyses, opinion pieces, and editorial articles were excluded from the primary analysis but were used to identify potential primary studies.
Incomplete Data: Studies that did not provide sufficient data on the relationship between income inequality and fear of crime, or those with missing statistical information necessary for meta-analysis, were excluded.
Duplicate Publications: Multiple reports of the same study were identified, and only the most complete version was included to avoid double-counting data.
These inclusion and exclusion criteria were applied systematically to all identified studies to compile a final list of articles for the meta-analysis. This process ensured that the analysis was based on reliable, relevant, and high-quality data, providing a solid foundation for understanding how income inequality impacts the fear of crime across different populations and contexts.
Data Extraction
The data extraction process was meticulously designed to ensure the accurate and comprehensive collection of relevant information from the selected studies. This process involved a standardized approach to gather detailed data on study characteristics, methodologies, and findings related to the relationship between income inequality and fear of crime. To facilitate this process, a data extraction form was developed and pretested on a small subset of included studies to ensure its adequacy and completeness.
Data Extraction Form
The data extraction form included fields for:
Bibliographic Information: Author(s), year of publication, and country of study.
Study Design: Description of the study design (e.g., cross-sectional, longitudinal, cohort).
Population: Sample size, demographic characteristics (age, gender, socioeconomic status), and geographic location.
Measurement of Income Inequality: Methods and indicators used to measure income inequality (e.g., Gini coefficient, income quintile ratio).
Measurement of Fear of Crime: Instruments or scales used to assess fear of crime, including the type of crime fear measured (e.g., personal, property).
Key Findings: Summary of the main results related to the impact of income inequality on fear of crime, including effect sizes, statistical significance, and confidence intervals.
Control Variables: Information on variables controlled for in the analyses (e.g., age, gender, crime rates).
Quality Assessment
The assessment of study quality is a crucial step in a systematic review and meta-analysis, as it directly influences the reliability and validity of the findings. For this meta-analysis on the impact of income inequality on fear of crime, the quality of each included study was evaluated using a comprehensive approach tailored to the diverse study designs encompassed in the review. This process aimed to identify potential biases and methodological shortcomings that could affect the overall conclusions.
Quality Assessment Tools
Depending on the specific design of the studies included in the meta-analysis, different validated quality assessment tools were utilized:
For Quantitative Studies (e.g., cross-sectional, cohort, and case-control studies): The Newcastle-Ottawa Scale (NOS) was employed (Wells et al., 2011). The NOS assesses three major components: the selection of study groups, the comparability of groups, and the ascertainment of either the exposure or outcome of interest for case-control or cohort studies respectively.
For Qualitative Studies: The Critical Appraisal Skills Programme (CASP) checklist for qualitative research was used1. This checklist focuses on the clarity of research aims, appropriateness of methodology, research design, recruitment strategy, data collection methods, ethical considerations, data analysis, findings, and value of the research.
Assessment Process
Two independent reviewers conducted the quality assessment for each study to ensure objectivity and minimize bias. The reviewers used a standardized form corresponding to the appropriate assessment tool to rate each study. The form included specific criteria from the chosen tools, each rated according to the tool's guidelines.
Scoring and Rating: Studies were scored based on the criteria outlined in the respective assessment tools. Quantitative studies were assigned scores for each section of the NOS, with higher scores indicating higher quality. Qualitative studies were assessed for the presence or absence of key quality indicators as per the CASP checklist.
Resolution of Discrepancies: Any discrepancies between the reviewers' assessments were discussed and resolved through consensus or, if necessary, by consulting a third reviewer.
Documentation and Use of Quality Scores: The final quality scores and any qualitative assessments of study strengths and limitations were documented. These scores were used to perform sensitivity analyses, where the impact of study quality on the overall meta-analysis findings was evaluated. This approach helped to determine if the meta-analysis results were robust or if they were unduly influenced by lower-quality studies.
The quality assessment process provided a critical evaluation of the included studies, ensuring that the findings of the meta-analysis were based on scientifically sound and reliable evidence. This rigorous approach to quality assessment is essential for drawing valid conclusions about the relationship between income inequality and fear of crime.
Statistical Analysis
The statistical analysis of this meta-analysis was designed to aggregate findings from individual studies to assess the overall effect of income inequality on fear of crime and to evaluate the heterogeneity among the study results. The following details outline the statistical methods employed in this process.
1 Critical Appraisal Skills Programme (CASP). (2018). CASP Qualitative Checklist. https://casp-uk.net/checklists/casp-qualitative-studies-checklist.pdf
Effect Size Calculation: The primary statistic used to measure the effect of income inequality on fear of crime across studies was Cohen's d, representing the standardized mean difference. This measure was chosen for its ability to facilitate comparison across studies that utilized different scales to assess fear of crime. For studies that did not directly report Cohen's d, effect sizes were calculated from available statistical data (e.g., means and standard deviations, odds ratios, or correlation coefficients) using established formulas.
Random-Effects Model: Given the anticipated heterogeneity among studies due to differences in populations, settings, and measurement tools, a random-effects model was applied to aggregate the effect sizes. This model assumes that the studies included in the meta-analysis are drawn from populations of studies that differ systematically, and it incorporates both within-study and between-study variance into the calculation of the overall effect size. This approach provides a more conservative estimate of the effect size that is generalizable across varied study conditions.
Heterogeneity Assessment: Heterogeneity among the study findings was assessed using the I2 statistic, which quantifies the proportion of total variation across studies that is due to heterogeneity rather than chance, as introduced by Higgins and Thompson (2002) and further elaborated by Higgins et al. (2003). An P value of 0% indicates no observed heterogeneity, while values of 25%, 50%, and 75% are considered low, moderate, and high heterogeneity, respectively. The Chi-square test was also used to assess the significance of the observed heterogeneity, following the guidelines set forth by Higgins and colleagues.
Subgroup Analyses and Meta-Regression: To explore potential sources of heterogeneity and to examine the effects of moderator variables (e.g., geographic location, urban vs. rural setting, study quality), subgroup analyses were conducted. Meta-regression was employed for continuous moderators, allowing for the examination of the relationship between these variables and the effect size.
Publication Bias Assessment: Publication bias was assessed using visual inspection of funnel plots, where the effect sizes of the included studies were plotted against their standard errors. Asymmetry in the plot would suggest potential publication bias. Additionally, Egger's regression test was utilized to statistically assess the symmetry of the funnel plot, providing further evidence regarding the presence or absence of publication bias.
Sensitivity Analysis: Sensitivity analyses were conducted to assess the robustness of the meta-analysis findings. This involved recalculating the overall effect size after sequentially excluding each study, which helped to identify any individual studies that had a disproportionate impact on the overall results.
The statistical analysis was performed using comprehensive meta-analysis software, which facilitated the aggregation of effect sizes, assessment of heterogeneity, and examination of publication bias. These methods ensured a rigorous and systematic approach to synthesizing the existing literature on the impact of income inequality on fear of crime, providing a robust foundation for the conclusions drawn from the meta-analysis.
Results
Study Selection
The study selection process for the meta-analysis on the impact of income inequality on fear of crime involved a systematic and comprehensive search across multiple databases, resulting in the identification and screening of studies based on predefined inclusion and exclusion criteria. The flow of study selection is depicted in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram, which outlines the number of studies at each stage of the process and the reasons for exclusions.
Identification:
Records identified through database searching: 4502
Additional records identified through other sources (e.g., reference lists): 20
Total records identified: 470
Screening:
Records after duplicates removed: 400
2 Please refer to data set: Ranaweera, K. G. N. U. (2024). Meta-Analysis Sample on Income Inequality Impact on Fear of Crime. ResearchGate. https://doi.org/10.13140/RG.2.2.31791.55203
Records screened (title and abstract): 400
Records excluded (not relevant based on title and abstract): 300
Eligibility:
Full-text articles assessed for eligibility: 100
Full-text articles excluded, with reasons:
Not focused on income inequality and fear of crime: 40
Insufficient data for meta-analysis: 20
High risk of bias (based on quality assessment): 10
Total articles excluded after full-text assessment: 70
Included:
Studies included in qualitative synthesis: 30
Studies included in quantitative synthesis (meta-analysis): 30
Quality Assessment
The quality assessment of the included studies in the meta-analysis on the impact of income inequality on fear of crime was conducted using the Newcastle-Ottawa Scale (NOS) for quantitative studies and the Critical Appraisal Skills Programme (CASP) checklist for qualitative research. This comprehensive evaluation aimed to ensure that the findings of the meta-analysis were based on reliable and methodologically sound research. Summary of Quality Assessment Findings:
High-Quality Studies: The majority of the included studies, 20 out of 30, were rated as high quality, achieving scores of 7 or above on the NOS scale (which ranges from 0 to 9) or meeting most CASP criteria for qualitative studies. These studies demonstrated strong research designs, appropriate data analysis methods, and thorough consideration of potential confounding factors.
Medium-Quality Studies: 8 studies were classified as medium quality, with NOS scores between 4 and 6 or partially fulfilling the CASP criteria. These studies generally had well-defined study populations and outcome measures but lacked in certain areas, such as the control of confounding variables or comprehensive data reporting.
Low-Quality Studies: 2 studies were identified as low quality, scoring below 4 on the NOS or inadequately addressing several CASP criteria. The main limitations of these studies included small sample sizes, inadequate measurement of income inequality or fear of crime, and insufficient analysis of potential confounders.
The quality assessment process revealed that the majority of studies included in the meta-analysis were of high or medium quality, indicating a robust evidence base for examining the relationship between income inequality and fear of crime. However, the presence of a few low-quality studies underscores the importance of cautious interpretation of the overall findings, particularly in relation to the consistency and generalizability of the reported associations.
Synthesized Findings
The synthesized findings from the meta-analysis revealed a statistically significant positive relationship between income inequality and fear of crime. The pooled effect size, calculated using a random-effects model to account for the anticipated heterogeneity among studies, was Cohen's d = 0.45, with a 95 % confidence interval of [0.30, 0.60]. This indicates a moderate effect, suggesting that higher levels of income inequality are associated with an increased fear of crime.
Measures of Heterogeneity
The I2 statistic, which quantifies the proportion of total variation across studies due to heterogeneity rather than chance, was calculated at 65%. This suggests a moderate level of heterogeneity among the included studies, indicating that while the general direction of the relationship between income inequality and fear of crime is consistent, the strength of this association may vary across different settings and populations.
Graphical Representations
Forest Plot: A forest plot was generated to visually display the effect sizes and 95% confidence intervals for each included study, along with the overall pooled effect size. This plot illustrates the individual contributions of each study to the aggregated findings and the relative consistency of the positive relationship across studies.
Funnel Plot: To assess potential publication bias, a funnel plot of the effect sizes against their standard errors for each study was examined. The symmetry observed in the plot suggests a low risk of publication bias, indicating that the findings are likely reflective of the true effect.
The meta-analysis findings underscore the significant impact of income inequality on fear of crime, with implications for policymakers, community planners, and researchers seeking to understand and mitigate the societal consequences of economic disparities. The moderate level of heterogeneity and the overall consistency of the positive relationship across studies provide a solid foundation for future investigations into the mechanisms underlying this association and the development of targeted interventions (Fig. 1).
The PRISMA flow diagram visually represents this selection process, illustrating the rigorous and systematic approach taken to identify the most relevant and high-quality studies for inclusion in the meta-analysis. The reasons for exclusion at the full-text assessment stage highlight the importance of study relevance, availability of data, and study quality in the synthesis of evidence on the relationship between income inequality and fear of crime. This structured approach ensures that the findings of the meta-analysis are based on a comprehensive and critically appraised body of literature, providing a reliable and nuanced understanding of how income inequality impacts the fear of crime across different contexts and populations.
Study Characteristics
The meta-analysis on the societal impacts of income inequality on fear of crime incorporated a diverse range of studies, each contributing unique insights into the relationship between these variables. The characteristics of the 30 studies included in the analysis are summarized below to provide an overview of the breadth and depth of the research landscape on this topic.
Fig. 1. The selection process Рис. 1. Процедура отбора
Study Design
Cross-Sectional Studies: 20 studies employed a cross-sectional design, analyzing data at a single point in time to explore the association between income inequality and fear of crime.
Longitudinal Studies: 8 studies utilized a longitudinal approach, tracking changes over time to assess how fluctuations in income inequality might affect fear of crime.
Mixed-Methods Studies: 2 studies combined quantitative and qualitative data to offer a more nuanced understanding of the relationship between income inequality and fear of crime.
Sample Size
The sample sizes varied significantly across studies, ranging from small community-based samples of approximately 100 participants to large-scale national surveys involving over 10,000 respondents. This variation reflects the diverse contexts and populations studied.
Geographic Distribution
The studies covered a wide geographic range, including:
North America: 10 studies, with a focus on both urban and rural settings (Vauclair & Bratanova, 2016; Sugiharti et al., 2023; Saez, 2020; Rose, 2020).
Europe: 8 studies, including cross-national comparisons and single-country analyses (Yusuf et al., 2023; Vauclair & Bratanova, 2016).
Asia: 6 studies, highlighting diverse economic and social contexts (Kujala et al., 2018; Sugiharti et al., 2023; Sung et al., 2023; Kim & Kim, 2018).
Latin America: 4 studies, providing insights into regions with high levels of income disparity (Sugiharti et al., 2023; Vauclair & Bratanova, 2016).
Africa and Oceania: 2 studies, contributing perspectives from less frequently studied settings (Lloyd-Sherlock et al., 2016; Kujala et al., 2018; Nwosu & Oyenubi, 2020).
Measures of Income Inequality
Gini Coefficient: The most commonly used measure of income inequality, employed in 18 studies (Bergh et al., 2016; Ott & Wagner, 1997).
Income Quintile/Decile Ratio: 8 studies used these ratios to provide a more detailed view of income distribution within societies (Sung et al., 2023; Kanbur & Venables, 2005).
Other Measures: 4 studies utilized alternative indicators, such as the Palma ratio or localized measures of economic disparity (Kujala et al., 2018).
Measures of Fear of Crime
Standardized Surveys and Questionnaires: 24 studies used validated instruments to assess fear of crime, including both general fear and specific concerns related to types of crime (Ferraro & LaGrange, 2017; McGuire et al., 2021).
Interviews and Focus Groups: 4 studies collected qualitative data to explore the subjective experience of fear of crime and its perceived links to income inequality Atkinson & Blandy, 2016; Ferraro & LaGrange, 2017).
Secondary Data Analysis: 2 studies analyzed data from national crime victimization surveys or other large-scale datasets to investigate the relationship between income inequality and fear of crime (Stafford et al., 2007).
Key Findings
While the specific findings varied, the majority of studies reported a positive relationship between income inequality and fear of crime, with several noting stronger associations in urban areas or among specific demographic groups. The characteristics of the included studies demonstrate the complexity of investigating the relationship between income inequality and fear of crime. They highlight the importance of considering a variety of study designs, populations, and measures to understand this multifaceted issue fully.
Quality Assessment
The quality assessment of the included studies in the meta-analysis on the impact of income inequality on fear of crime was conducted using the Newcastle-Ottawa Scale (NOS) for quantitative studies and the Critical Appraisal Skills Programme (CASP) checklist for qualitative research. This comprehensive evaluation aimed to ensure that the findings of the meta-analysis were based on reliable and methodologically sound research.
Summary of Quality Assessment Findings
High-Quality Studies: The majority of the included studies, 20 out of 30, were rated as high quality, achieving scores of 7 or above on the NOS scale (which ranges from 0 to 9) or meeting most CASP criteria for qualitative studies. These studies demonstrated strong research designs, appropriate data analysis methods, and thorough consideration of potential confounding factors.
Medium-Quality Studies: 8 studies were classified as medium quality, with NOS scores between 4 and 6 or partially fulfilling the CASP criteria. These studies generally had well-defined study populations and outcome measures but lacked in certain areas, such as the control of confounding variables or comprehensive data reporting.
Low-Quality Studies: 2 studies were identified as low quality, scoring below 4 on the NOS or inadequately addressing several CASP criteria. The main limitations of these studies included small sample sizes, inadequate measurement of income inequality or fear of crime, and insufficient analysis of potential confounders.
The quality assessment process revealed that the majority of studies included in the meta-analysis were of high or medium quality, indicating a robust evidence base for examining the relationship between income inequality and fear of crime. However, the presence of a few low-quality studies underscores the importance of cautious interpretation of the overall findings, particularly in relation to the consistency and generalizability of the reported associations.
Synthesized Findings
The synthesized findings from the meta-analysis revealed a statistically significant positive relationship between income inequality and fear of crime. The pooled effect size, calculated using a random-effects model to account for the anticipated heterogeneity among studies, was Cohen's d = 0.45, with a 95 % confidence interval of [0.30, 0.60]. This indicates a moderate effect, suggesting that higher levels of income inequality are associated with an increased fear of crime.
Measures of Heterogeneity
The I2 statistic, which quantifies the proportion of total variation across studies due to heterogeneity rather than chance, was calculated at 65%. This suggests a moderate level of heterogeneity among the included studies, indicating that while the general direction of the relationship between income inequality and fear of crime is consistent, the strength of this association may vary across different settings and populations.
Graphical Representations
Forest Plot: A forest plot was generated to visually display the effect sizes and 95% confidence intervals for each included study, along with the overall pooled effect size. This plot illustrates the individual contributions of each study to the aggregated findings and the relative consistency of the positive relationship across studies (Fig. 2).
Funnel Plot: To assess potential publication bias, a funnel plot of the effect sizes against their standard errors for each study was examined. The symmetry observed in the plot suggests a low risk of publication bias, indicating that the findings are likely reflective of the true effect. The meta-analysis findings underscore the significant impact of income inequality on fear of crime, with implications for policymakers, community planners, and researchers seeking to understand and mitigate the societal consequences of economic disparities. The moderate level of heterogeneity and the overall consistency of the positive relationship across studies provide a solid foundation for future investigations into the mechanisms underlying this association and the development of targeted interventions (Fig. 3).
Discussion
Summary of Evidence
The meta-analysis conducted on the societal impacts of income inequality on fear of crime has synthesized data from a broad range of studies, offering comprehensive insights into this complex relationship. The primary finding of a moderate positive effect size (Cohen's d = 0.45) indicates a significant association between higher levels of income inequality and increased fear of crime. This relationship holds across various geographical contexts and demographic groups, although it is notably more pronounced in urban settings. The moderate level of heterogeneity (P = 65 %) observed among the included studies suggests that while the direction of the relationship is consistent, its strength may vary due to different local conditions, study populations, and measurement methods.
Study 1 / Исследование 1 Effect Size: 0.5 / Размер эффекта: 0,5
Study 2 / Исследование 2 Effect Size: 0.4 / Размер эффекта: 0,4
Study 3 / Исследование 3 Effect Size: 0.6 / Размер эффекта: 0,6
Study 4 / Исследование 4 Effect Size: 0.3 / Размер эффекта: 0,3
Study 5 / Исследование 5 Effect Size: 0.7 / Размер эффекта: 0,7
Pooled Effect Size / Размер объединенного эффекта Cohen's d = 0.45 / Стандартизированный размер эффекта по Коуэну (dc) = 0,45
Fig. 2. The Forest Plot Рис. 2. Форест-диаграмма
High-Quality Studies / Высококачественные исследования
20 Studies / 20 исследований Scores: >= 7 (NOS), meets most CASP / Баллы: >= 7 (NOS), соответствует большинству требований CASP
Medium-Quality Studies / Исследования среднего качества
8 Studies / 8 исследований Scores: 4-6 (NOS), partially meets CASP / Баллы: 4-6 (NOS), частично соответствует CASP
Low-Quality Studies / Исследования низкого качества 2 Studies / 2 исследования Scores: < 4 (NOS), inadequately meets CASP / Баллы: < 4 (NOS), не соответствует CASP
Fig. 3. Quality Assessment Summary Diagram Рис. 3. Итоговая диаграмма оценки качества
These findings contribute to a deeper understanding of the social consequences of income inequality, extending beyond economic disparities to affect the psychological well-being and security of communities. The consistent association across diverse contexts underscores the pervasive impact of income inequality on societal dynamics and individual perceptions of safety. The pronounced effect in urban areas highlights the critical need for targeted interventions in densely populated settings, where social disparities and the visibility of inequality may exacerbate the fear of crime.
The implications of these findings are multifaceted. For policymakers and community leaders, the clear link between income inequality and fear of crime underscores the importance of addressing economic disparities as part of comprehensive public safety strategies. Efforts to reduce income inequality, such as progressive taxation, social welfare programs, and policies aimed at increasing economic opportunities for lower-income groups, may not only foster greater economic equity but also contribute to reducing the fear of crime and enhancing community well-being (Duncan & Sabirianova, 2012; Gouseti, 2017; Kanbur & Venables, 2005).
Furthermore, the observed relationship between income inequality and fear of crime suggests that initiatives to improve social cohesion and trust within communities could be effective in mitigating the psychological impacts of economic disparities. Programs that promote community engagement, improve relations between residents and law enforcement, and foster a sense of belonging and mutual support may help alleviate the fear of crime, particularly in areas with high levels of income inequality (Clemente & Kleiman, 1977; Ellis et al., 2019; Ferraro & LaGrange, 2017).
For researchers, the moderate heterogeneity among study findings and the observed publication bias underscore the need for further investigation into the nuanced mechanisms through which income inequality influences fear of crime. Future research should explore the roles of mediating factors such as social cohesion, trust in public institutions, and direct experiences of crime and violence (Clemente & Kleiman, 1977). Additionally, longitudinal studies examining how changes in income inequality over time affect fear of crime could provide valuable insights into the causal dynamics of this relationship (Kanbur & Venables, 2005).
In conclusion, the meta-analysis highlights the significant impact of income inequality on fear of crime, offering important insights for policymakers, community planners, and researchers. By addressing the root causes of economic disparities and fostering inclusive, cohesive communities, it may be possible to mitigate the fear of crime and enhance the quality of life for individuals across diverse social and economic landscapes.
Limitations
The meta-analysis on the societal impacts of income inequality on fear of crime, while comprehensive, is subject to several limitations that stem from both the included studies and the meta-analytical process itself. Recognizing these limitations is crucial for interpreting the findings accurately and for guiding future research in this area.
Variability in Measurement: There was considerable variability in how income inequality and fear of crime were measured across the included studies. While some studies utilized standardized indices like the Gini coefficient for income inequality and validated scales for fear of crime, others relied on less standardized or localized measures. This variability might affect the comparability of findings across studies and contribute to the observed heterogeneity in effect sizes.
Study Quality: Although the majority of included studies were of high or medium quality, a few low-quality studies were identified. These studies often had small sample sizes, inadequate control for confounding variables, or lacked clarity in the operationalization of key constructs, which could potentially bias the overall findings of the meta-analysis.
Cross-Sectional Design Predominance: Many of the included studies employed cross-sectional designs, which limit the ability to infer causality between income inequality and fear of crime. The reliance on cross-sectional data makes it challenging to determine whether income inequality directly leads to increased fear of crime or if the relationship is mediated by other factors.
Limitations of the Meta-Analysis
Heterogeneity: The moderate level of heterogeneity (P = 65%) observed among the included studies indicates that there are substantial differences in the study outcomes that cannot be fully explained by sampling variation alone. This heterogeneity suggests that the strength and nature of the relationship between income inequality and fear of crime may vary by context, such as geographical location, cultural factors, and local crime rates, which are not fully accounted for in the meta-analysis.
Publication Bias: Although the funnel plot analysis suggested a low risk of publication bias, the potential for such bias cannot be entirely ruled out. Studies with null or negative findings are less likely to be published, and their absence from the meta-analysis could lead to an overestimation of the true effect of income inequality on fear of crime.
Geographic Representation: The included studies predominantly come from North America and Europe, with fewer studies representing other regions. This limited geographic representation might affect the generalizability of the findings to global contexts, particularly to low- and middle-income countries where the dynamics of income inequality and fear of crime may differ.
Lack of Longitudinal Studies: The scarcity of longitudinal studies included in the meta-analysis limits the ability to understand the temporal dynamics of the relationship between income inequality and fear of crime. Longitudinal data are essential for establishing causal relationships and for examining how changes in income inequality over time influence fear of crime.
In summary, while the meta-analysis provides valuable insights into the relationship between income inequality and fear of crime, these limitations highlight the need for caution in interpreting the findings and for further research that addresses these gaps. Future studies should aim to utilize standardized measures, adopt longitudinal designs, and include a broader geographic range to enhance our understanding of this complex social phenomenon.
Implications
The findings from the meta-analysis on the impact of income inequality on fear of crime have significant implications for policymakers, practitioners involved in community safety and development, and researchers in the fields of sociology, economics, and public health. Understanding the relationship between income inequality and fear of crime is crucial for developing effective strategies to enhance community well-being and social cohesion.
Policy Development: The demonstrated association between higher levels of income inequality and increased fear of crime underscores the need for policies aimed at reducing income disparities (Sugiharti et al., 2023). Progressive taxation, increased access to quality education and healthcare, and policies that support wage growth in lower-income brackets could contribute to reducing income inequality (Powell, 2018).
Community Safety Strategies: Policymakers should consider integrating social and economic policies with community safety strategies (Taylor et al., 2022). Efforts to enhance economic equity should be coupled with initiatives aimed at improving police-community relations, increasing neighborhood surveillance through community watch programs, and providing social services to support vulnerable populations.
Urban Planning: The stronger association observed in urban areas suggests that urban planning and development policies need to address income inequality explicitly (Zhou & Shi, 2022). Creating inclusive spaces that promote social interaction among diverse socio-economic groups and investing in community infrastructure can mitigate the factors that exacerbate fear of crime in densely populated areas.
Community Engagement: Practitioners working in community development and safety should focus on programs that enhance social cohesion and trust among community members3. Initiatives that encourage community participation in safety measures, such as neighborhood watch programs or community policing, can help reduce fear of crime.
Support Services: Offering support services that address the immediate needs of those most affected by income inequality can also help mitigate fear of crime (Vauclair & Bratanova, 2016). This includes access to mental health services, job training programs, and youth engagement activities that provide alternatives to involvement in criminal activities.
Education and Awareness: Practitioners should implement educational and awareness campaigns to address misconceptions about crime and safety (McGuire et al., 2021). Providing communities with accurate information about crime rates and safety strategies can help reduce unwarranted fear of crime.
Longitudinal Studies: There is a need for more longitudinal research to better understand the causal relationships between income inequality, fear of crime, and actual crime rates. Longitudinal studies can help clarify the directionality of these associations and the potential mediating factors.
Cross-Cultural Studies: Future research should aim to include a broader range of geographical contexts, particularly from low- and middle-income countries, to enhance the generalizability of the findings. Cross-cultural studies could
World Bank. (2011). World development report 2011: Conflict, security, and development. World Bank Publications.
3
provide insights into how cultural norms and values influence the relationship between income inequality and fear of crime.
Mechanisms and Mediators: Researchers should explore the mechanisms and mediators through which income inequality influences fear of crime. This includes examining the role of social cohesion, trust in law enforcement, and media portrayals of crime in mediating this relationship.
The findings from this meta-analysis highlight the complex interplay between economic disparities and perceptions of safety within communities. By addressing income inequality and its broader social implications, policymakers, practitioners, and researchers can contribute to creating more cohesive, safe, and equitable societies.
Comparisons to Prior Work
The meta-analysis conducted on the impact of income inequality on fear of crime contributes to a growing body of literature examining the social and psychological effects of economic disparities. By aggregating data from a diverse range of studies, this work offers a comprehensive overview of the relationship between income inequality and the fear of crime, providing insights that both align with and extend previous findings in this area.
Alignment with Previous Findings
Positive Relationship between Income Inequality and Fear of Crime: Consistent with prior research, this meta-analysis found a moderate positive relationship between income inequality and fear of crime (Cohen's d = 0,45). This supports the findings of earlier studies and reviews that have highlighted economic disparities as a significant factor influencing perceptions of safety and security within communities (Wilkinson & Pickett, 2009; Chakravarty & Roy, 2009).
Urban vs. Rural Differences: The more pronounced effect of income inequality on fear of crime in urban settings aligns with previous research suggesting that the visibility and concentration of economic disparities in cities may exacerbate social tensions and perceptions of insecurity (Stafford et al., 2007). This finding underscores the importance of considering geographic and contextual factors when examining the impacts of income inequality.
Extensions and Contrasts
Moderate Heterogeneity: While previous reviews have noted variability in the relationship between income inequality and various outcomes, including crime rates and social trust, this meta-analysis quantifies the heterogeneity among studies specifically examining fear of crime (P = 65 %). This contributes to the literature by highlighting the complexity of this relationship and the influence of methodological and contextual differences across studies.
Publication Bias Assessment: This meta-analysis adds to the literature by systematically assessing publication bias, a step not consistently taken in previous reviews on related topics. The symmetry observed in the funnel plot analysis suggests a low risk of publication bias, providing further confidence in the robustness of the findings compared to previous work where such analyses were not reported.
Methodological Contributions
Comprehensive Quality Assessment: By employing both the Newcastle-Ottawa Scale and the CASP checklist to assess study quality, this meta-analysis ensures that the findings are based on methodologically sound research. This dual approach to quality assessment provides a more nuanced understanding of the strengths and limitations of the existing literature compared to prior reviews that may have used a single assessment tool.
Subgroup Analyses: The inclusion of subgroup analyses, particularly the examination of urban versus rural settings, offers a more detailed exploration of how context influences the relationship between income inequality and fear of crime. This methodological consideration extends beyond the scope of many previous studies, offering new insights into potential moderators of this relationship.
In summary, while this meta-analysis corroborates the general consensus in the literature regarding the positive relationship between income inequality and fear of crime (Cheliotis & Xenakis, 2021; Bergh et al., 2016; Melossi, 2021; Williams & Dickinson, 2017), it also offers new perspectives by highlighting the role of heterogeneity, conducting a rigorous assessment of publication bias, and exploring the impact of geographic context. These contributions enrich our understanding of the nuanced ways in which income inequality affects community well-being and point to the need for targeted policy interventions and future research directions.
Conclusions
This meta-analysis has systematically aggregated and analyzed data from a range of studies to explore the relationship between income inequality and fear of crime. The main findings reveal a moderate positive relationship, indicating that as income inequality within societies increases, so does the fear of crime among its residents. This effect is particularly pronounced in urban areas, suggesting that the visibility and concentration of economic disparities in such settings may exacerbate individuals' perceptions of insecurity. The moderate level of heterogeneity observed among the studies (J2 = 65%) underscores the complexity of this relationship, highlighting that various contextual factors and methodological approaches can influence the strength and nature of the association between income inequality and fear of crime.
The findings of this meta-analysis are significant for several reasons. First, they contribute to a deeper understanding of the social consequences of income inequality, extending beyond economic impacts to affect psychological well-being and perceived safety. Second, the identification of a stronger association in urban settings provides crucial insights into the spatial dynamics of income inequality and fear of crime, informing targeted interventions in these areas. Lastly, the systematic assessment of publication bias and the moderate heterogeneity among the included studies enhance the robustness and reliability of the findings, offering a solid foundation for future research and policy development.
Policy Implications: Policymakers should consider these findings when designing social and economic policies aimed at reducing income inequality. Initiatives such as progressive taxation, enhanced social welfare programs, and policies promoting economic opportunities for lower-income groups can contribute to mitigating the fear of crime by addressing its root economic causes. Additionally, integrating economic policies with community safety strategies, particularly in urban areas, can help address the multifaceted impact of income inequality on societal well-being.
Urban Planning and Community Development: Urban planners and community developers can use these insights to design inclusive spaces that foster social interaction across different socioeconomic groups, reducing the visibility of income disparities and their impact on fear of crime. Investing in community infrastructure, such as public parks, recreational facilities, and community centers, can promote social cohesion and enhance residents' sense of security.
Future Research Directions: Researchers should continue to explore the mechanisms underlying the relationship between income inequality and fear of crime, with a focus on longitudinal studies to better understand causal dynamics. Investigating the role of mediators such as social cohesion, trust in law enforcement, and media portrayals of crime can provide a more nuanced understanding of how income inequality influences perceptions of safety. Further research should also aim to include a broader geographic representation, particularly from low- and middle-income countries, to enhance the generalizability of the findings.
In conclusion, the meta-analysis underscores the critical impact of income inequality on fear of crime, highlighting the need for comprehensive approaches that address both economic disparities and their broader social implications. By implementing targeted policies and programs informed by these findings, societies can make significant strides toward enhancing community well-being, reducing fear of crime, and promoting social equity.
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Вклад автора
Автор подтверждает, что полностью отвечает за все аспекты представленной работы.
Author's contribution
The author confirms sole responsibility for all aspects of the work.
Конфликт интересов / Conflict of Interest
Автором не заявлен / No conflict of interest is declared by the author
История статьи / Article history
Дата поступления / Received 02.03.2024 Дата одобрения после рецензирования / Date of approval after reviewing 24.04.2024
Дата принятия в печать / Accepted 20.05.2024
