The purpose of this study is to identify the effects of multidimensional (fuzzy) inequalities and marginal changes on the Gini coefficients of various factors. This allows a range of social policies to be specifically targeted to reduce broader inequalities, but these policies are focused primarily on health, education, housing, sanitation, energy and drinking water. It is necessary to target policy areas that are unequally distributed, such as those with access to unevenly distributed drinking water policies. The data are from the Household and Consumption Survey of 6695 households in 2003 and 9259 households in 2011. This paper uses Lerman and Yitzhaki’s method. The results revealed that the main contributors to inequalities over the two periods were health and education. These sources have a potentially significant effect on total inequality. Health increases overall inequalities, but sources such as housing, sanitation and energy reduce them. This article provides resources to disadvantaged and vulnerable target groups. Multiple inequalities are analyzed for different subgroups of households, such as place of residence and the gender of the head of household. Analyzing fuzzy poverty inequalities makes it possible to develop targeted measures to combat poverty and inequality. This study is the first to investigate the sources of Gini’s fuzzy inequality in Chad via data analysis techniques, and in general, it is one of the few studies in Saharan Africa to be interested in this subject. Some development policies in sub-Saharan Africa should therefore focus on different sources (negative effect), sources (positive effect) and the equalization effect.

Given the multifaceted nature of crime trends shaped by a range of social, economic, and demographic variables, grasping the fundamental drivers behind crime patterns is pivotal for crafting effective crime deterrence methodologies. This investigation adopted a systematic literature review technique to distill thirty key factors from a corpus of one hundred scholarly articles. Utilizing the Principal Component Analysis (PCA) for diminishing dimensionality facilitated a nuanced understanding of the determinants deemed essential in influencing crime trends. The findings highlight the necessity of tackling issues such as inequality, educational deficits, poverty, unemployment, insufficient parental guidance, and peer influence in the realm of crime prevention efforts. Such knowledge empowers policymakers and law enforcement bodies to optimize resource allocation and roll out interventions grounded in empirical evidence, thereby fostering a safer and more secure societal environment.

By reviewing US state-level panel data on infrastructure spending and on per capita income inequality from 1950 to 2010, this paper sets out to test whether an empirical link exists between infrastructure and inequality. Panel regressions with fixed effects show that an increase in the growth rate of spending on highways and higher education in a given decade correlates negatively with Gini indices at the end of the decade, thus suggesting a causal effect from growth in infrastructure spending to a reduction in inequality through better access to education and opportunities for employment. More significantly, this relationship is more pronounced with inequality at the bottom 40 percent of the income distribution. In addition, infrastructure expenditures on highways are shown to be more effective at reducing inequality. By carrying out a counterfactual experiment, the results show that those US states with a significantly higher bottom Gini coefficient in 2010 had underinvested in infrastructure during the previous decade. From a policy-making perspective, new innovations in finance for infrastructure investments are developed, for the US, other industrially advanced countries and also for developing economies.

The objective of this research is to examine the effects of income inequality, governance quality, and their interaction on environmental quality in Asian countries. Time series data are obtained from 45 Asian countries for the period 1996–2020 for this empirical analysis. The research has performed various econometric tests to ensure the robustness and reliability of the results. We have addressed different econometric issues, such as autocorrelation, heteroskedasticity, and cross-sectional dependence, using the Driscoll-Kraay (DK) standard error estimation and endogeneity issues by the system generalized method of moments (S-GMM). The results of the study revealed that income inequality and governance quality have a positive impact on environmental degradation, while the interaction of governance quality with income inequality has a negative effect on it. In addition, economic growth, population growth, urbanization, and natural resource dependency are found to deteriorate the quality of the environment. The findings of the study offer insightful policies to reduce environmental degradation in Asian countries.