Study on the impact of air pollution on residents' health expenditures in Guangdong, Hong Kong, and Macao Greater Bay Area.

The Guangdong, Hong Kong, and Macao Greater Bay Area (GHMA) has experienced economic development and rapid growth of transportation infrastructure in recent years. However, the economic advancement is also accompanied by serious atmospheric pollution, which threatens the health of the residents, thus, it is of great significance to explore the impact of atmospheric pollution on the health expenditures of residents in the GHMA. The article establishes a spatial econometric model to study the impact of atmospheric pollution on residents' health expenditure in the GHMA based on panel data from 2014 to 2021, using nine prefectures in the GHMA as research objects. The results show that: (1) Atmospheric pollution in the GHMA has an obvious spatial agglomeration phenomenon and spatial spillover effect, and the impact of atmospheric pollution on the health of the residents is still very serious; (2) PM2.5 emissions are positively and significantly related to the actual health care cost per person, and the rise in air pollution is the main reason for the rise in public health spending; (3) Other factors also have different impacts on residents' health expenditures. Based on the above research, the article puts forward corresponding policy recommendations.

Importance of temporary and permanent snow for new second homes.

This study investigates empirically how natural snow depth and permanent snow affect the number of new second homes in Norway. One out of four Norwegian municipalities is partly covered by glaciers and permanent snow. In the winter seasons of 1983-2020, there is a decline in snow depth from 50 to 35 cm on average (based on 41 popular second-home areas in the mountains). Results of the fixed effects Poisson estimator with spatial elements show that there is a significant and positive relationship between natural snow depth in the municipality and the number of second homes started. There is also a significant and negative relationship between the number of new second homes in the municipality and a scarcity of snow in the surrounding municipalities. However, the magnitude of both effects is small. Estimates also show a strong positive relationship between the proportion of surface covered by permanent snow or glaciers in the municipality and new second homes. This implies that a decline in permanent snow and glaciers may make these areas less attractive for the location of second homes.

Regional socioeconomic characteristics and density of general practitioners in Germany: A nationwide cross-sectional and longitudinal spatial analysis.

OBJECTIVES: Limited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics. STUDY DESIGN: This longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants. METHODS: Univariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective. RESULTS: In contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. ß = -2.31, p = 0.021; 2019: std. ß = -4.14, p < 0.001) and higher unemployment rate (2015: std. ß = -2.84, p = 0.005; 2019: std. ß = -5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. ß = -2.17, p = 0.030). CONCLUSION: A higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care.

Distribution characteristics and influencing factors of household consumption carbon emissions in China from a spatial perspective.

Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

The impact of digital inclusive finance on the collaborative reduction of pollutant and carbon emissions: spatial spillover and mechanism analysis.

In light of the escalating global climate risks threatening human survival, there is a global consensus on the necessity for collaborative reduction of pollutant and carbon emissions (CRPC). Within this context, digital inclusive finance (DIF) is recognized for its unique inclusiveness and digital characteristics as a critical factor in promoting environmentally friendly and sustainable development. DIF provides advantageous channels for environmental governance, thereby making the achievement of CRPC objectives feasible. However, the impact of DIF on CRPC has not been fully explored. This study employs a spatial econometric model to investigate the impact of DIF on CRPC in 278 prefecture-level cities in China from 2011 to 2020. The findings indicate that DIF has a positive impact on CRPC, with significant spatial spillover effects. The analysis highlights the pivotal mediating roles played by technology effect and electrified effect of the energy mix, while environmental regulation effect plays a moderating role. Notably, disparities in the impact of DIF on CRPC are evident, particularly in non-resource-based cities, cities with low carbon intensity, and eastern regions where spatial spillover effects are more pronounced. These experiences enrich the relevant thesis in terms of DIF on CRPC, providing a theoretical basis for formulating CRPC schemes.

Application of the sustainable system-of-systems approach and econometric analysis to address China's decarbonisation problem.

The Sustainable system-of-systems (SSoS) approach, complemented with econometric analysis was used to address China's decarbonisation problem, i.e. selecting fossil fuel consumption sources to be reduced in various regions to meet CO2 reduction targets with minimal effect on population and economic growth. In the SSoS, the micro-level system is represented by residents' health expenditure, the meso-level system by industry's CO2 emissions intensity, and the macro-level system by the government's achievement of economic growth. Regional panel data from 2009 to 2019 were used in an econometric analysis conducted using structural equation modelling. The results show that health expenditure was affected by CO2 emissions from the consumption of raw coal and natural gas. To support economic growth, the government should reduce raw coal consumption. For CO2 emissions reduction, industry in the eastern region should reduce raw coal consumption. The key advantage is SSoS with econometrics offers a way to reach a common goal among stakeholders.Practitioner summary: This research shows that the use of the SSoS approach, complemented with an econometric analysis of key social, economic, and natural capital data, can address a complex decarbonisation problem facing a nation (China, in the present case) while considering the goals of all stakeholders (the government, industrial communities, and residential communities).Abbreviations: CEADs: Carbon Emissions Accounts and Datasets for Emerging Countries; CEIC: CEIC Global Database; GRPS: World Economic Forum's Global Risks Perception Survey; HFE: human factors/ergonomics; ML-SEM: maximum likelihood estimation method; NDRC: National Development and Reform Commission of China; SEM: structural equation modelling; SSoS: Sustainable system-of-systems; TBL: triple bottom line.

A joint analysis of accessibility and household trip frequencies by travel mode.

This paper examines the endogenous relationship between residential level of accessibility and household trip frequencies to tease out the direct and indirect effects of observed behavioural differences. We estimate a multivariate ordered probit model system, which allows dependence in both observed and unobserved factors, using data from the 2016 Transportation Tomorrow Survey (TTS), a household travel survey in the Greater Golden Horseshoe Area (GGH) in Toronto. The modelling framework is used to analyse the influence of exogenous variables on eight outcome variables of accessibility levels and trip frequencies by four modes (auto, transit, bicycle and walk), and to explore the nature of the relationships between them. The results confirm our hypothesis that not only does a strong correlation exist between the residential level of accessibility and household trip frequency, but there are also direct effects to be observed. The complementarity effect between auto accessibility and transit trips, and the substitution effect observed between transit accessibility and auto trips highlight the residential neighbourhood dissonance of transit riders. It shows that locations with better transit service are not necessarily locations where people who make more transit trips reside. Essentially, both jointness (due to error correlations) as well as directional effects observed between accessibility and trip frequencies of multiple modes offer strong support for the notion that accessibility and trip frequency by mode constitute a bundled choice and need to be considered as such.

Impact of prevention in primary care on costs in primary and secondary care for people with serious mental illness.

A largely unexplored part of the financial incentive for physicians to participate in preventive care is the degree to which they are the residual claimant from any resulting cost savings. We examine the impact of two preventive activities for people with serious mental illness (care plans and annual reviews of physical health) by English primary care practices on costs in these practices and in secondary care. Using panel two-part models to analyze patient-level data linked across primary and secondary care, we find that these preventive activities in the previous year are associated with cost reductions in the current quarter both in primary and secondary care. We estimate that there are large beneficial externalities for which the primary care physician is not the residual claimant: the cost savings in secondary care are 4.7 times larger than the cost savings in primary care. These activities are incentivized in the English National Health Service but the total financial incentives for primary care physicians to participate were considerably smaller than the total cost savings produced. This suggests that changes to the design of incentives to increase the marginal reward for conducting these preventive activities among patients with serious mental illness could have further increased welfare.

Environmental quality and its impact on total fertility rate: an econometric analysis from a new perspective.

BACKGROUND: Environmental quality significantly affects various aspects of human existence. This study employs ecological footprint as a proxy to assess the impact of environmental quality on the TFR, measured as births per woman. This study investigates the extent to which ecological footprint indicators impact on the TFR in across 31 countries between from 1990 to 2017. METHODS: We gathered data on ecological footprints, specifically carbon, agricultural land, grazing land, forest products, and fisheries, from the Global Footprint Network. Information on the TFR, Human Development Index (HDI), and per capita Gross National Income (GNI) were sourced from the World Bank and the United Nations. We applied static panel and quantile regression models to scrutinize the connection between the ecological footprint and TFR, showing how the former influences the latter. RESULTS: The outcomes reveal that, in both fixed and random effects models, factors including HDI, carbon, and fishing grounds exert a negative influence on TFR, all at a significance level of p < 0.01. Conversely, cropland and forest product footprints exhibited a favorable impact on the TFR (p < 0.01). Furthermore, GNI per capita positively affected the TFR in both models, with a p-value of 0.01. Quantiles regression analysis demonstrated that HDI and carbon footprint had a negative impact on TFR across all quantiles. This statistical significance is maintained for all quantiles, although it is only significant for the carbon footprint up to the 60th quantile, at p < 0.01. CONCLUSIONS: This study establishes a negative correlation between specific ecological footprint indicators, such as carbon and fishing grounds, and TFR. Conversely, there was a positive correlation between the footprint of forest products and the TFR. The primary conclusion drawn is that there is heterogeneity in the results regarding the relationship between ecological footprint and TFR. Moreover, the ecological footprint indicators considered in this study did not uniformly influence TFR. Each ecological footprint indicator exhibited distinct effects on the TFR, displaying either positive or negative correlation coefficients. Future research endeavors may delve into how ecological footprints impact other population dynamics, such as mortality and migration.

The effect of informal social support on the health of Chinese older adults: a cross-sectional study.

BACKGROUND: This study aimed to assess the effect of informal social support (ISS) on the health of Chinese older adults, identify channels of the association between the two, and assess the magnitude of this effect in different groups of older adults. METHODS: Based on the data from the 2018 China Longitudinal Aging Social Survey (CLASS), we first used both the Quality of Well-Being (QWB) scale and the analytic hierarchy process (AHP) method to construct the QWB score that can objectively measure the health status of Chinese older adults. Next, we conducted an econometric equation controlling for various high-dimensional fixed effects, estimated the effects using the Tobit model, and used various robustness check strategies and the propensity score matching (PSM) method to ensure reliability and deal with the potential endogeneity, respectively. Finally, we performed staging and grouping regression for mechanism and heterogeneity analysis. RESULTS: The mean QWB score of Chinese older adults was 0.778. ISS has a significant positive effect on the health of older adults (P < 0.001), and there were similar patterns of findings for the effects of SE (P < 0.001), PSS (P < 0.001), and ES (P < 0.001). Additionally, the health promotion effect is higher in older adults who are male (P < 0.001), under the age of 80 (P < 0.001), with agricultural household registration (P < 0.001), or with high income (P < 0.001) than in the control group. CONCLUSION: ISS, including SE, PSS, and ES, had significant promotion effects on the health of older adults, especially on those who are male, under the age of 80, with agricultural household registration, or with high income. Meanwhile, these effects could be reflected through two channels: alleviating loneliness and improving the positive emotional status of older adults.

Spatial and temporal evolution characteristics and spillover effects of China's regional carbon emissions.

China's carbon emissions account for approximately a quarter of the world's total greenhouse gas emissions. In 2020, China's fossil fuels accounted for approximately 85% of the primary energy demand, with coal alone accounting for 60%. Considering the severe global warming situation, it is necessary to reveal the spatial and temporal differences and analyze the spillover effects of carbon emissions between regions. In this study, a positive and significant spatial correlation between regional carbon emissions in China was found using an exploratory spatial data analysis. The spatial Durbin model was then utilized to explore the direct and spillover effects of factors that included economic growth, the energy intensity, and the level of technological innovation on regional carbon emissions. Whether a direct effect or a spillover effect, economic growth and improvements in the regional levels of technological innovation had significant inhibitory effects on carbon emissions both in the long term and in the short term. Specifically, an increase of 1% in the level of technological innovation led to a reduction of approximately 0.17% in the region's carbon emissions. However, a growth in the energy intensity will increase carbon emissions. In addition, an increase in the technological input intensity will lead to an increase in carbon emissions in local regions. However, an increase in neighboring regions will restrain carbon emissions in a local region. Based on these findings, it is recommended that the government accelerate regional innovation synergies and increase investment in clean energy technologies.

Do tourism clusters contribute to low-carbon destinations? The spillover effect of tourism agglomerations on urban residential CO2 emissions.

A burgeoning literature has investigated the relationship between tourism and regional CO2 emissions. However, the results are equivocal. Some scholars find that tourism induces more emissions, while others find that tourism benefits emission mitigation. Previous research suffers three deficiencies: (1) the CO2 emissions induced by tourism beyond the tourism sectors are neglected; (2) the role of tourism agglomerations in sustainable destination development is underrated; (3) the tripartite mechanism of tourism agglomerations, household income, and urban direct residential CO2 emissions (DRCEs) remains to be determined. Based on the theories of complex adaptive system and an empirical study of 30 provinces in China, we examine the relationship between the tripartite roles via multiple spatial econometric models. The results disclose that tourism agglomerations have dual effects on urban DRCEs and an inverted U-shaped effect on household income. The growth of tourism agglomerations has generally promoted urban DRCEs in many provinces of mainland China during the last fourteen years because its indirect effect (positive) outweighs the direct effect (negative). However, tourism agglomerations are feasible for low-carbon destinations once the emission-income decoupling happens. This study contributes to low-carbon destination management by presenting a more holistic image of the social-economic-environmental impacts of tourism agglomerations.

Spatial-temporal evaluation of urban resilience in the Yangtze River Delta from the perspective of the coupling coordination degree.

Scientific evaluation of urban resilience will help to improve the ability of self-prevention and self-recovery when facing internal and external pressure. However, existing studies are on basis of the overall perspective of the urban resilience evaluation index system to measure urban resilience, often ignoring the coupling and coordination degree among indicators. Therefore, an empirical analysis is developed, which is used to measure the urban resilience of eight cities in the Yangtze River Delta urban agglomeration from 2010 to 2019 from the perspective of coupling coordination degree based on the urban resilience evaluation index system. The empirical results show that (1) In time, the eight cities' resilience fluctuated dynamically and varied to different degrees. It presents the spatial distribution characteristics of "high in the center and low in the periphery" in space. (2) In time, the coupling coordination degree in the eight cities fluctuated slightly. The spatial distribution pattern of "high in the center and low in the periphery" was formed in terms of space. (3) There is a long-term stable relationship between urban resilience and the coupling coordination degree among all indicators. In a certain sense, the higher the coupling coordination degree is, the higher the urban resilience is. These results can improve urban resilience to some extent and make cities more resilient in the future collaborative development process, and provide a way to evaluate urban resilience at different spatial-temporal scales.

Econometric Issues in Prospective Economic Evaluations Alongside Clinical Trials: Combining the Nonparametric Bootstrap With Methods That Address Missing Data.

Prospective economic evaluations conducted alongside clinical trials have become an increasingly popular approach in evaluating the cost-effectiveness of a public health initiative or treatment intervention. These types of economic studies provide improved internal validity and accuracy of cost and effectiveness estimates of health interventions and, compared with simulation or decision-analytic models, have the advantage of jointly observing health and economics outcomes of trial participants. However, missing data due to incomplete response or patient attrition, and sampling uncertainty are common concerns in econometric analysis of clinical trials. Missing data are a particular problem for comparative effectiveness trials of substance use disorder interventions. Multiple imputation and inverse probability weighting are 2 widely recommended methods to address missing data bias, and the nonparametric bootstrap is recommended to address uncertainty in predicted mean cost and effectiveness between trial interventions. Although these methods have been studied extensively by themselves, little is known about how to appropriately combine them and about the potential pitfalls and advantages of different approaches. We provide a review of statistical methods used in 29 economic evaluations of substance use disorder intervention identified from 4 published systematic reviews and a targeted search of the literature. We evaluate how each study addressed missing data bias, whether the recommended nonparametric bootstrap was used, how these 2 methods were combined, and conclude with recommendations for future research.

The long-run effects of diagnosis related group payment on hospital lengths of stay in a publicly funded health care system: Evidence from 15 years of micro data.

Diagnosis Related Group (DRG) payment systems are a common means of paying for hospital services. They reward greater activity and therefore potentially encourage more rapid treatment. This paper uses 15 years of administrative data to examine the impact of a DRG system introduced in England on hospital lengths of stay. We utilize different econometric models, exploiting within and cross jurisdiction variation, to identify policy effects, finding that the reduction of lengths of stay was greater than previously estimated and grew over time. This constitutes new and important evidence of the ability of financing reform to generate substantial and persistent change in healthcare delivery.

Parental decision and intent towards COVID-19 vaccination in children with asthma: an econometric analysis.

OBJECTIVE: Vaccination will be instrumental in controlling the COVID-19 pandemic, and vaccination of children will be necessary to achieve herd immunity. Given that children with chronic health conditions may be at increased risk of COVID-19, it is crucial to understand factors influencing parental decisions about whether to have their child vaccinated. The study objectives were to measure parental intent to have their child with asthma vaccinated against COVID-19 and identify the determinants of their vaccination decision. STUDY DESIGN: This study is based on a cross-sectional exploratory observational online survey assessing parents' risk perception in the context of COVID-19. METHODS: In this study conducted in August 2020, the primary outcome was parent's answer to the question on their intention to get their child vaccinated if a vaccine against COVID-19 was available. Participants were also asked about their intention to get vaccinated themselves. Independent variables studied included sociodemographic, clinical data (e.g. presence of other chronic diseases), psychological, cognitive and risk perception related to COVID-19. Simultaneous equations models (3SLS) and seemingly unrelated regressions model (SUR) were carried out to identify factors associated with intention to have the child vaccinated and participants' intention to get vaccinated themselves against COVID-19. RESULTS: A total of 305 participants completed the survey. Overall, 19.1% of participants reported being unlikely or very unlikely to vaccinate their child against COVID-19 if a vaccine was available. Similarly, 21.0% were unlikely or very unlikely to get vaccinated themselves. The following factors were significantly associated with parents' decision to have their child vaccinated: parental level of education (p = 0.003), employment status (p < 0.001), sex of the child (p = 0.019), presence of other chronic diseases (p = 0.028), whether or not the child had been vaccinated against influenza in the past (p < 0.001), parental anxiety (p = 0.046), and consultation with a health professional since the beginning of the pandemic (p = 0.009). There was a strong relationship between likelihood of not intending to have one's child vaccinated and personal intent not to get vaccinated. CONCLUSION: These findings are essential in planning for the communication and dissemination of COVID-19 vaccination information to parents, especially for children with asthma or other chronic medical conditions.

Effective access to health care in Mexico.

OBJECTIVES: This paper assesses the impact of effective access on out-of-pocket health payments and catastrophic health expenditure. Effective access cannot be attained unless both health services and financial risk protection are accessible, affordable, and acceptable. Therefore, it represents a key determinant in the transition from fragmented health systems to universal coverage that many low- and middle-income countries face. METHODS: We use a definition of effective access as the utilization of health insurance when available. We conducted a cross-sectional analysis using the 2018 Mexican National Health Survey (ENSANUT) at the household level. The analysis is performed in two stages. The first stage is a multinomial analysis that captures the factor associated with choosing effective access against the alternative of paying privately. The second stage consists of an impact analysis regarding the decision of not choosing effective access in terms of out-of-pocket (OOP) health payments and catastrophic health expenditures (CHE). The analysis corrects for both the decision to buy insurance and the decision to pay for health care. RESULTS: We found that, on average, not choosing effective access increases OOP health payments by around 2300 pesos annually. Medicine payments are the most common factor in this increase. Nevertheless, outpatient and medicines health care are the main drivers of the increase in OOP health payments in all insurance beneficiaries. Not having effective access increases the probability of CHE health expenditures by 2.7 p.p. for the case of Social Security Insurance and 4.0 p.p. for Social Government insurance. Household enrolled in Prospera program for the poor are more likely to choose effective access while having household heads with more education and assets value does the opposite. Diabetes illnesses are associated with a higher probability of effective access. CONCLUSION: Improving effective access is a middle step that cannot be disregarded when seeking universal coverage because OOP health payments and catastrophic outcomes are direct consequences. Public insurance in general, has around 50% effective access which remains a challenge in terms of health services utilization and health public policy design, calling for the need of better coordination across insurance types and pooling mechanisms to increase sustainability of needed health services.

Spatial influence on the distribution of downhill skiers in Sweden.

This study investigates empirically how natural snow depth affects the number of downhill skiers. Data include the number of skier visits for the 32 largest ski resorts in Sweden from the 1998/1999 to the 2018/2019 seasons. Results of spatial dynamic estimations show that an increase in natural snow depth in the ski area has a significant negative impact on the number of skier visits in the short term, although the magnitude is small. This implies that a snow deficit leads to increased demand for downhill skiing both directly and indirectly (in the neighbouring areas). The variable snow depth in the neighbouring ski areas is not significantly different from zero, indicating that no spatial substitution takes place. There is, however, a strong positive relationship between skier visits to neighbouring areas, revealing that ski resorts are complements rather than substitutes. The long-term influence of snow depth is not significant, implying that the ski business is independent of variations in snow depth. Instead, the number of skier visits is mainly determined by past visits, revealing a high degree of persistence.

Use of medical services in Chile: How sensitive are the results to different econometric models?

BACKGROUND: We compared different econometric specifications to model the use of medical services in Chile, focussing on visits to general practitioners and specialist physicians. METHODS: The evaluated models are the Poisson, Negative Binomial, Zero Inflated Poisson and Negative Binomial, two-step Hurdle model, sample-selection Poisson, and Latent Class model. These models were estimated using Chilean data for the years 2009 and 2015, separated by gender. RESULTS: Unlike previous literature that supported the use of the latent class model, our results show that the latent class model is not always the model with the best goodness of fit. Furthermore, the model with the best fit is not necessarily the model with the best predictive power. For instance, depending on the year and medical services, either the latent class model or the sample-selection Poisson model performs better than the other models. The results also show that the selection of the econometric model may have implications for the estimated influence that variables such as age, income, or affiliation to the public versus private sector have on the use of medical services. CONCLUSION: Using Chilean data, we have tested that the selection of an econometric method to model the use of medical services is not a problem with a unique answer. We recommend performing a sensitivity analysis of goodness of fit and predictive power between gender, healthcare services, or different years of datasets in future applications to be sure about the best model specification in each context.

Gambling Outlets Location in Urban Areas: A Case Study of Madrid.

Cities are certainly a key factor in the location of gambling facilities. This paper aims to map the location of gambling outlets in urban areas and to examine potential links between neighborhoods socioeconomic and demographic characteristics and gambling supply, taking into account spatial dependencies of neighboring areas. This correlation is of interest because neighborhood characteristics may attract sellers, and because the presence of gambling sellers may cause changes in neighborhood demographics. Using detailed official data from the city of Madrid for the year 2017, three spatial econometric approaches are considered: spatial autoregressive (SAR) model, spatial error model (SEM) and spatial lag of X (explicative variables) model (SLX). Empirical analysis finds a strong correlation between neighborhoods characteristics and co-location of gambling outlets, highlighting a specific geographic patterning of distribution within more disadvantaged urban areas. This may have interesting implications for gambling stakeholders and for local governments when it comes to the introduction and/or increase of gambling availability.