Intra-Urban Inequalities in a Southern European City: The Case of Palermo

As nations grapple with the challenges posed by increasing debt burdens, finding out the intricacies between macroeconomic indicators would give insights into how household consumption and foreign investments are affected by the high national debt level, and how they move along with the identified variables such as tax revenues and economic health of the country. The goal of this paper is to focus on the implications of high external debt for macroeconomic variables and find out the variables’ effects in the short term and the long term by using a quantitative approach or method. The researchers intend to find out if the household consumption expenditure is influenced by high national debt, tax revenues, total economic health, and foreign investments, or otherwise, finding out as well if the foreign investments are influenced by high national debt, tax revenues, total economic health, and household consumption expenditure or not. The paper also has its constraints given that related literature is limited in terms of the immediate relationship of the variables that the researchers want to study. With the knowledge that high national debt would have a toll on the country’s economic performance or the gross domestic product (GDP), there is the perception of high debt having an impact on household consumption or consumer spending that can alter the living conditions or reduce incomes of the people, given that the government would need to look for ways in order to pay off high debt by resorting to collecting more taxes. An impending increase in tax rates would eat up a portion of personal income that can in turn affect their ability to consume. High tax rates can also discourage foreign direct investments (FDIs) into the country, as this factor can also contribute to decisions to investments since other countries might offer a more competitive tax package. Aside from this, with the perception that a country can be on the brink of debt overheating due to high national debt, foreign investors would hesitate to come in due to the idea that the economy is struggling, a bad precedent for doing business. High amounts of public debt can restrict the government's options for fiscal policy during recessions, which lessens the government's ability to help the economy recover and push forward. As debts increase, the growing perception that this would be contra beneficial to the living conditions of people despite the everyday grind could affect consumption behavior and future expectations on price and policy directions, further affecting the country’s overall economic health. While fiscal policies are strong indicators of government revenue raising and spending directions and actions, tax collection or revenues are necessarily integral as a key variable influencing a country’s capacity to pay that can also limit potentials of incurring a high national debt level. With good tax collection practices and tax policies, as a key source of revenues to help pay off the country’s debt, other factors such as monetary policy are also important. Monetary policy control is needed because rising interest rates can make it more expensive to borrow to cover basic household needs and other forms of financial needs, such as mortgages and other financial obligations. This could limit disposable income and reduce consumer spending. Moreover, related hazards that impact general consumption behavior include the depreciation of the currency and price rises that lead to inflation, which could exacerbate the living conditions of the people. These might lower families' purchasing power. Therefore, the effects of large public debt on investments and budgets emphasize the necessity of sound fiscal management and policy use to reduce any potential negative effects. The research examines the dynamics of macroeconomic aggregates in the context of the nation's high levels of national debt, tax revenue, household spending, and foreign investments. Among the objectives are trend analyses of significant variables such as GDP, tax revenues, foreign direct investment (FDI), household consumption, and national government debt. Another study objective is to comprehend the relationship between tax income, state debt, household expenditure, and foreign direct investments. Its goal is to determine whether the country's growing debt affects household consumption and investments. The study utilizes the Autoregressive Distributed Lag (ARDL) cointegration technique to examine the links between GDP, tax collections, foreign investments, household consumption, and national debt. In conclusion, the analysis' result regarding the influence of total national debt, overall economic health, and foreign investment on the household's final consumption expenditure (HCFE) is largely evident in both the short run and long run. The ARDL is a flexible model that allows analysis at level and first differences. Different lag lengths may also be used in the model having different variables.

Household expenditure on food consumption has become an issue of great concern in Nigeria today. This is so because an increasing percentage of the household in Nigeria was feeding less and therefore become more food insecure which made households suffer serious ailments arising from an acute deficiency in their diets. This study examined the effect of socioeconomic factors on household expenditure on food consumption in Nigeria. Data were gotten through the administration of questionnaires to 2500 household heads in three senatorial districts in Ondo State. The study adopted multistage sample techniques; we analyzed information extracted from the questionnaire using both descriptive and regression techniques. The result indicated that major source of income exerts a significant negative (t=-3.76, p<0.05) effect on household consumption expenditure in Ondo State, Nigeria, a positive relationship between household size and consumption expenditure (t=5,38, p<0.05), and a positive impact between education qualification and household expenditure for consumption (t-3.26,p<0.05). However, gender, age, marital status, and respondents' senatorial districts did not get a significant effect on households' consumption expenditure of food. The study recommended that government should try to pay workers' salaries as at when due to discourage reduction in food consumption expenditure of the people

This study entitled the effect of household consumption expenditure, private investment and direct regional government expenditure on economic growth in East Lombok Regency in 2001-2017 . This study aims to partially and jointly analyze household consumption expenditure, private investment and direct local government expenditure on economic growth in East Lombok Regency and analyze which variables have a dominant influence on economic growth in East Lombok Regency. This research is a type of causal research with quantitative methods using multiple linear regression analysis methods. Source of data is secondary data in the form of time series data, during the period 2001- 2017 . The dependent variable in this study is economic growth, household consumption expenditure, private investment and direct regional government expenditure as the independent variable. Regression results show that household consumption expenditure and private investment have no significant effect on economic growth at α 5 percent while the local government direct expenditure variable has a significant effect on economic growth at α 5 percent. By simultaneous observations of the three variables are statistically significant at alpha 5 percent. Local government direct expenditure variable has a dominant influence on economic growth with a coefficient value of 4.881637 while the smallest effect on economic growth is the variable of household consumption with a coefficient value of 0.106645.This study entitled the effect of household consumption expenditure, private investment and direct regional government expenditure on economic growth in East Lombok Regency in 2001-2017 . This study aims to partially and jointly analyze household consumption expenditure, private investment and direct local government expenditure on economic growth in East Lombok Regency and analyze which variables have a dominant influence on economic growth in East Lombok Regency. This research is a type of causal research with quantitative methods using multiple linear regression analysis methods. Source of data is secondary data in the form of time series data, during the period 2001- 2017 . The dependent variable in this study is economic growth, household consumption expenditure, private investment and direct regional government expenditure as the independent variable. Regression results show that household consumption expenditure and private investment have no significant effect on economic growth at α 5 percent while the local government direct expenditure variable has a significant effect on economic growth at α 5 percent. By simultaneous observations of the three variables are statistically significant at alpha 5 percent. Local government direct expenditure variable has a dominant influence on economic growth with a coefficient value of 4.881637 while the smallest effect on economic growth is the variable of household consumption with a coefficient value of 0.106645.

This study empirically investigates the applicability of the Permanent Income Hypothesis (PIH) to household consumption expenditure in Akwa Ibom State, Nigeria, using primary data from 403 households. Data were collected on household income, consumption expenditure, savings, and socio-demographic characteristics through structured questionnaires. Descriptive and inferential analyses, including correlation and multiple regression with log-transformed income, were employed to examine the relationship between household income and consumption patterns. Findings reveal a strong and significant positive association between household income and consumption expenditure, supporting the PIH prediction that household smooth consumption based on expected permanent income. Non-linear analysis further indicates that higher-income households tend to spend proportionally more, while increased savings reduce current consumption, reflecting forward-looking behavior. Demographic factors such as age, household size, and education exhibit minimal influence on consumption. The study provides robust micro-level evidence of the PIH in a sub-national context and highlights the importance of income stability and savings for consumption planning. Policy interventions aimed at stabilizing household income and promoting savings are recommended to enhance economic well-being.

Research, mainly conducted in Europe and North America, has shown an inequitable burden of internalising mental health problems among adolescents from poorer households. We investigated whether these mental health inequalities differ across a diverse range of countries and multiple measures of economic circumstances. In this longitudinal observational cohort study, we analysed data from studies conducted in eight countries (Australia, Ethiopia, India, Mexico, Peru, South Africa, the UK, and Viet Nam) across five global regions. All studies had self-reported measures of internalising symptoms using a validated scale at two timepoints in adolescence; a measure of household income, household consumption expenditure, or subjective wealth; and data collected between 2000 and 2019. Household income (measured in four countries), consumption expenditure (six countries), and adolescents' subjective assessment of household wealth (five countries) were measured in mid-adolescence (14-17 years). The primary outcome (internalising symptoms, characterised by negative mood, affect, and anxiety) was measured later in adolescence between age 17 and 19 years. Analyses were linear regression models with adjustment. Effect estimates were added to random-effects meta-analyses to aid understanding of cross-country differences. The overall pooled sample of eight studies featured 18 910 adolescents (9568 [50·6%] female and 9342 [49·4%] male). Household income had a small or null association with adolescents' internalising symptoms. Heterogeneity (I2 statistic) was 71·04%, falling to 39·71% after adjusting for baseline symptoms. Household consumption expenditure had a stronger association with internalising symptoms (decreases of 0·075 SD in Peru [95% CI -0·136 to -0·013], 0·034 SD in South Africa [-0·061 to -0·006], and 0·141 SD in Viet Nam [-0·202 to -0·081] as household consumption expenditure doubled). The I2 statistic was 74·24%, remaining similar at 74·83% after adjusting for baseline symptoms. Adolescents' subjective wealth was associated with internalising symptoms in four of the five countries where it was measured. The I2 statistic was 57·09% and remained similar after adjusting for baseline symptoms (53·25%). We found evidence for cross-country differences in economic inequalities in adolescents' internalising symptoms, most prominently for inequalities according to household consumption expenditure. Subjective wealth explained greater variance in symptoms compared with the objective measures. Our study suggests that economic inequalities in adolescents' mental health are prevalent in many but not all countries and vary by the economic measure considered. Variation in the magnitude of inequalities suggests that the wider context within countries plays an important role in the development of these inequalities. Wellcome Trust.

Several integrative indices at the neighborhood level have been developed in the US, but direct comparisons across these indices for the prevalence of cardiovascular-kidney-metabolic (CKM) conditions are limited. Moreover, it is not known whether certain indices better capture place-based variability in CKM conditions or provide additional information when compared with a single measure of income. To determine how much variability is explained by neighborhood indices in the prevalence of CKM conditions at the census tract level and the incremental value of each index when added to median household income. This cross-sectional study of US national surveillance data (Behavioral Risk Factor Surveillance System and American Community Survey) included all census tracts with complete data for exposures, covariates, and outcomes from 2010 to 2022. Analyses were completed between October 2024 and July 2025. Seven neighborhood indices available at the census tract level (Area Deprivation Index, Child Opportunity Index, Environmental Justice Index, Neighborhood Deprivation Index, Social Deprivation Index, Social Vulnerability Index, Structural Racism Effect Index) and median household income at the census tract level from the American Community Survey. The primary outcome was prevalence of CKM conditions (coronary heart disease [CHD], stroke, and chronic kidney disease [CKD]) at the census tract level. Differences in the exposures and outcomes were visualized by mapping index scores, median household income, and prevalences of CKM conditions for all census tracts. To assess index agreement, pairwise correlations were computed with Spearman rank correlation coefficients, and to assess the incremental variability explained by each index when added to median household income, change in r2 was calculated. Of the 65 476 US census tracts included, median (IQR) prevalence was 5.9% (4.7%-7.4%) for CHD, 3.3% (2.6%-4.1%) for stroke, and 2.9% (2.5%-3.4%) for CKD. The indices and income were modestly to highly correlated (range, 0.46-0.94), with some discordance in how each measure classified census tracts by quartiles of index scores. All indices and income were significantly associated with CKM condition prevalence at the census tract level in multivariable linear regression models, adjusted for median population size and age. The r2 values of the indices with CHD, stroke, and CKD ranged from 0.379 (SE = 0.033) for the correlation between the Environmental Justice Index and stroke to 0.688 (SE = 0.002) for the correlation between the Structural Racism Effect Index and stroke. Additionally, the improvement in variability for CHD, stroke, and CKD (change in r2) explained by the addition of each index to income ranged from 0.014 (SE = 0.001) for the correlation between the Environmental Justice Index and CHD to 0.195 (SE = 0.002) for the correlation between Structural Racism Effect Index and stroke. In this cross-sectional study, there were similar associations across neighborhood indices and income with the prevalence of CKM conditions. These findings inform how different place-based measures can be applied in public health research and policy.

Introduction: Alcohol is the major chemical risk factor for hepatocellular carcinoma (HCC) around the world, however other toxins including Arsenic have been shown to promote hepatocarcinogenesis in animals, though the exact mechanism is poorly understood. This ecological study assesses neighborhood-level HCC burden in Texas relative to arsenic exposure. Methods: Using data from the Texas Cancer Registry, we identified a cohort of individuals diagnosed with HCC between 2011 and 2015. The primary exposure of interest is Arsenic pollution as reported in the 2011 National Air Toxics Assessment (NATA) inventory, this national screening assessment by the Environmental Protection Agency (EPA) uses emissions data to estimate health risks from toxic air pollutants. NATA calculates the concentrations of toxic air pollutants at the census tract (neighborhood) level; the inhalation exposure concentrations of Arsenic are in units of micrograms per cubic meter, however, for analysis, exposure concentrations were divided into deciles. Arsenic concentrations, demographic data, and the Area Deprivation Index (composite measure of neighborhood socioeconomic disadvantage that relies on 17 census variables drawn from these categories: poverty, housing, employment, and education) were included in multivariable Poisson-based modeling using negative binomial regression to evaluate the association between Arsenic exposure and HCC incidence in Texas. Results: In a univariable model, the association between Arsenic inhalation exposure concentrations and HCC was not significant (IRR = 1.06 [95% CI, 0.98-1.16]). Whereas, in a multivariable model that included selected demographic and socioeconomic factors, results show that variation in census tract HCC incidence across Texas is significantly associated with the inhalation exposure concentrations of Arsenic. Based on our findings, in a typical Texas census tract, a 10-unit increase in decile classification of Arsenic inhalation exposure concentration increases the risk of HCC incidence by a factor of 1.30, while holding other explanatory variables constant (IRR = 1.30 [95% CI, 1.19-1.42]) (Table). Conclusion: Variation in HCC incidence across Texas’s census tracts is significantly associated with the inhalation exposure concentrations of Arsenic at the census tract level, higher Arsenic concentrations are associated with an increased incidence. This ecological finding needs to be further examined in direct association studies. Table 1. - Relationships between HCC incidence in Texas (2011 to 2015) and inhalation exposure concentrations of Arsenic (2011 estimate). Texas census tracts; N = 5,205 Univariable Models a Multivariable Model b IRR 95% CI p value IRR 95% CI p value Arsenic concentrations c 1.06 0.98-1.16 0.159 1.302 1.194-1.419 0.001 Area Deprivation Index 1.17 1.16-1.20 <0.001 1.618 1.444-1.813 <0.001 % Hispanic or Latino (NH) 1.08 1.07-1.09 <0.001 1.092 1.080-1.105 <0.001 % Non-Hispanic Asians 0.68 0.65-0.71 <0.001 0.892 0.853-0.934 <0.001 % Non-Hispanic African American 1.06 1.05-1.07 <0.001 1.098 1.080-1.116 <0.001 % Others; 2+ races d 0.58 0.54-0.63 <0.001 - % Population ≥ 60 y.o. 1.19 1.15-1.22 <0.001 1.406 1.359-1.455 <0.001 % Population male 1.05 1.00-1.11 0.064 1.171 1.117-1.226 <0.001 % Non-Hispanic White e 0.91 0.90-0.92 <0.001 aUnivariable models where Arsenic exposure concentrations was regressed on the HCC incidence separately. Also, each covariate was regressed on the HCC incidence separately.bMultivariable model where Arsenic concentrations and the covariates (P value ≤ 0.10) were regressed on the HCC incidence simultaneously.cThe 2011 National Air Toxics Assessment (NATA) inhalation exposure concentrations for Arsenic are in units of micrograms per cubic meter; for analysis, concentrations were divided into deciles.dVariable dropped from the multivariable model run because p value > 0.10.eTo avoid model overfitting from multicollinearity among race/ethnicity, Non-Hispanic White excluded from multivariable model.

The present study analyzed to investigate the change in the pattern of consumption expenditure across both in rural and urban areas Further the study estimated the linear regression to identify the causality between per capita income and consumption expenditure. The analysis of the study used the NSS data from 1993/94-2011-12. The results of the study revealed that the monthly per capita consumption expenditure has increased tremendously over the period of time. The variation in consumption expenditure among household types has narrowed down, agriculture labour in the rural areas and casual labour in the urban areas registered a remarkable growth particularly during the period 2004/05–2009/10. The analysis of data showed a further shift away from food to non-food items in all expenditure categories in both rural and urban areas. But in the case of agriculture labour In rural areas and casual labour in urban areas, the preference of food items in total consumption remains the same. This study also evidenced that the non-farm employment has played a greater role on increasing income levels in both rural and urban areas. The correlation coefficient results showed there was positive relation between household income and consumption expenditure in both rural and urban areas. It also observed from the results the raise in income could lead to change the dietary patterns.

In March 2020, the COVID-19 pandemic caused a significant economic shock in countries worldwide, negatively affecting every aspect of the world economy. Due to the situation with the COVID-19 pandemic, governments imposed a lockdown on households to slow the spread of the pandemic. It was unknown how long the lockdown could last and how much impact it would have on households and the general government. Household consumption is a specific component of final GDP consumption and generally represents about 60% of GDP. Crises most often affect the individual and manifest in unplanned and unnecessary costs that affect household consumption and savings, and consequently growth and development. Eurostat states that the household savings rate of the European Union (EU) decreased in the third quarter of 2020 but was 4.5% higher than in 2019. This paper aims to analyze the differences in household consumption expenditure and net savings across the EU from 2018 until 2021 and general government consumption expenditure and net savings. In addition, it will compare the differences in household and general government consumption and savings in 2019 and 2020. This paper uses univariate statistical methods to define the differences between the EU member states and their private and public consumption expenditure and net savings. The authors will suggest further research on the topic mentioned above and provide evidence on how households should react to future pandemic situations.

This study estimates the neighborhood socioeconomic status (SES) effect on the risk of preterm birth (PTB) using multilevel regression (MLR) models. Birth data retrieved from year 2000 and 2010 Georgia Vital Records were linked to their respective census tracts. Principle component analysis (PCA) was performed on nine selected census variables and the first two principal components (Fac1 and Fac2) were used to represent the neighborhood-level SES in the MLR models. Two-level random intercept MLR models were specified using 122,744 and 112,578 live and singleton births at the individual level and 1613 and 1952 census tracts at the neighborhood level, for 2000 and 2010, respectively. After adjustment for individual level factors, Fac1, which represents disadvantaged SES, respectively generated an Odds Ratio of 1.056 (95% CI: 1.031–1.081) and 1.080 (95% CI: 1.056–1.105) for these two years, showing a modest but statistically significant effect on PTB. After adjusting for individual level factors and the census tract level factors, Intra-class correlation (ICC) was 1.2% and 1.4%, for year 2000 and 2010, respectively. The two IOR-80% intervals, 0.73–1.52 (year 2000) and 0.73–1.59 (year 2010) suggest large unexplained between census tract variation. The Median Odds Ratio (MOR) value of 1.21(year 2000) and 1.23 (year 2010) revealed that the un-modeled neighborhood effect was smaller than two individual-level predictor variables, race, and tobacco use but larger than the fixed effect of census tract-level predicting variable, Fac1 and all the other individual level factors. Overall, better census tract level SES was found to have a modest protective effect for PTB risk and the effects of the two examined years were similar. Large unexplained between census tract heterogeneity warrants more sophisticated MLR models to further investigate the PTB risk factors and their interactions at both individual and neighborhood levels.

Predictors of Neighborhood Risk for Late-Stage Melanoma: Addressing Disparities through Spatial Analysis and Area-Based Measures

Tackling Health Disparities in Radiology: A Practical Conceptual Framework.

Introduction Promoting sleep health at the neighborhood level may be an efficient way to promote overall health and well-being. This study examined the relative contribution of sleep health, versus other regional health metrics. Methods Neighborhood sleep health values were obtained from the “500 Cities” data collected by the CDC, which includes census tract and proportion that report values associated with health. Data include the population of each census tract as well as census-estimated proportion of the population in each census tract that report obtaining at least 7 hours of sleep. Other health indicators evaluated included access to health insurance, past-year routine medical or dental checkup, older adult preventive care, leisure-time activity, mammography, pap testing, and prevalence of arthritis, binge drinking, hypertension, antihypertensive use, cancer, asthma, coronary disease, cholesterol screening, colon screening, COPD, smoking, diabetes, hypercholesterolemia, kidney disease, poor mental and physical health, obesity, stroke, and teeth lost. The Child Opportunity Index (COI) is a publicly-available index (DiversityDataKids.org) reported at the census tract level. It provides indices for “Education,” “Health and Environment,” and “Social and Economic” domains, as well as a global score. The present analysis merged the 500 Cities data with the COI data, using census tract as the matching variable. When data were merged, 27,130 census tracts were included. Results In stepwise analyses adjusted for population size, with global COI as the dependent variable, sleep health emerged as the strongest predictor, accounting for 57.2% of the variance of global COI (p&amp;lt;0.0001). When all other health predictors were included in the model, the next largest contributors were teeth lost (additional 15.5%), health insurance (additional 3.0%), and asthma (additional 1.4%). Similarly, when stepwise analyses examined each component of COI as dependent variable, sleep health consistently emerged as the most substantial predictor, accounting for 41.2%, 24.3%, and 56.4% of the variance of “Education,” “Health and Environment,” and “Social and Economic” scores, respectively (all p&amp;lt;0.0001). Conclusion Sleep health is more strongly associated with overall COI (and all its components) than any other regional health metric. Public health efforts targeting sleep health may have disproportionately beneficial impact on factors that support family health and well-being. Support (If Any)

Creative industries have gained increasing attention in light of the cultural economy as viable magnets for local and regional economic development. Policy makers thus would benefit from attracting creative industries as potential economic boosters. However, it is hard to target such catalyst industries without better knowledge of the urban form conditions that may influence the location preference of these industries; do creative industries favor compact, pedestrian-friendly neighborhoods with transit accessibility to employment? This paper, as one of the first national studies, answers this question using a multilevel modeling approach to control for the socioeconomic and built environment characteristics at both local and regional levels. Factor analysis is used to define a Creative Score, which captures the geography of creative industries using the number of creative firms, employment, the percentage of creative firms, and a creative employment location quotient. The compactness/sprawl index is used at both census tract and metropolitan levels as a proxy for urban form. Accounting for the socioeconomic factors, the findings suggest that, at the neighborhood level, the compactness index is significantly and positively associated with the Creative Score. Every 10% increase in compactness score results in a 0.3% increase in Creative Score at the census tract level. This is partly because compact neighborhoods provide creative industries with a stronger consumer base as a reliable source of development. Compact urban form also serves agglomeration economies by facilitating knowledge exchange, reducing travel time and costs, and giving greater accessibility to destinations by transit.

This study investigates the contextual effect of neighbourhood socio-economic status (SES) on the risk of preterm birth (PTB) using multilevel models. Birth data retrieved from 2000 Georgia Vital Records were geocoded and joined to their respective census tracts. The census tract level Index of Deprivation (IoD) was calculated using nine 2000 Census variables based on a previously proposed ‘standard’ index. Two-level random intercept regression models were developed using 117,329 live and singleton births at the individual level and 1618 census tracts at the neighbourhood level. After adjustment for individual-level factors, IoD generated an odds ratio of 1.006 (95% CI 1.00–1.01), showing a modest but significant effect on PTB. Intra-class correlation (ICC) was 0.83% after adjusting for individual-level factors and the census tract level IoD. A wide IOR-80% interval (0.74–1.36) suggests large unexplained residual in between census tract variation remained. The median odds ratio (MOR) value of 1.17 revealed that the unmodelled neighbourhood effect was stronger than the fixed effect of census tract-level predicting variable, IoD, but weaker than the effects of several individual-level predictor variables, including race, tobacco use, prenatal care, foetal death history and marital status. Overall, better census tract-level SES would have a modest protective effect for PTB risk. The full strength of multilevel models should be exploited further to help our understanding of PTB aetiology.