Road congestion and air pollution -Analysis of spatial and temporal congestion effects.

Urban traffic congestion has resulted in several adverse outcomes, including reduced traffic efficiency, increased noise pollution, and heightened exhaust emissions. It has also emerged as a significant indicator of urban health concerns. This article primarily delves into an examination of the pollution stemming from congestion. To accomplish this, the study focuses on two specific aspects of congestion measurement: long-term spatial constraints (limited travel routes) and short-term time delays (time wasted due to congestion). Expanding on this, the article explores the potential solutions to mitigate pollution effects through measures such as optimizing space utilization through public transportation systems like subways and strategically scheduling travel during holidays. These considerations are incorporated within the article's scope. Additionally, in order to address endogeneity concerns, the research conducts instrumental variable effectiveness tests from both temporal and spatial perspectives. The outcomes highlight the degradation of air quality and the increase in total traffic congestion in both the long and short term, while also indicating the presence of genuine methods to alleviate these issues. Consequently, effective collaborative efforts for prevention and control are imperative to combat environmental and traffic pollution. Moreover, optimizing sustainable urban development plans to enhance land utilization plays a pivotal role in minimizing the external costs associated with long-distance commuting.

Safety effects of bypass lanes at unsignalized three-leg junctions.

Bypass lanes are a low-cost measure to increase capacity at unsignalized T-junctions without left-turn lanes that allow through-traffic to pass left-turning vehicles on the right. There is very limited knowledge about the safety effects of bypass lanes. We found six previous studies that could be summarized by means of meta-analysis, and the results show an average accident reduction of 10 percent. However, the results from previous studies are inconsistent and may be biased. Therefore, the present study has estimated safety effects of by-pass lanes in Norway, based on a sample of 2,227 T-junctions (incl. 94 with bypass-lanes) for which relevant data was available for a period of up to 10 years. We developed accident prediction models and conducted before-after analyses. The accident prediction models show that junctions with bypass lanes have 82 percent more accidents than junctions without bypass lanes, when controlling for endogeneity. Endogeneity occurs when the implementation of a measure is conditional on the frequency of crashes, as has been the case with bypass lanes. The before-after analysis shows that average accident numbers decrease after the installation of bypass lanes. However, when controlling for regression-to-the-mean (RTM), average accident numbers increase. RTM means that accident numbers would have been likely to decrease even without any measure because they had been exceptionally high in the before period. The control for potential biases in our study is likely to contribute to the discrepancy between results from our study and previous studies, most of which have not controlled for the same potential biases. We conclude therefore that bypass lanes, although favorable for capacity, are likely to be unfavorable for safety when compared to other unsignalized T-junctions without left-turn lanes. Unfavorable safety effects may partly be due to site specific conditions, such as road alignment and sight conditions, that contribute to rear-end collision risk or inappropriate driver behavior. However, this does not necessarily mean that bypass lanes never should be used. For example, at junctions where a bypass lane may solve capacity problems, and where site-specific conditions are favorable, bypass lanes may still be an acceptable solution.

Risk self-perception and occupational accidents.

INTRODUCTION: This study analyzes the relationship between measures of occupational accidents and workers' perception of risk in the workplace using nationally representative data on workers' characteristics and a complete record of occupational accidents. METHODS: Regression analyzes addressing both the ordinal nature of the dependent variable and causality were conducted to control for different sociodemographic factors influencing workers' perceptions of occupational risks. Special attention was paid to the risk level of the worker's workgroup, existence of family responsibilities, organizational safety culture, and measures of accident rates. RESULTS: Individuals showed different perceptions of risk based on their personal and work characteristics. Significant associations were observed between each variable of interest and risk perception. Overall, the results remain robust across specifications addressing both simultaneity and ordinality. CONCLUSIONS: Employees' "reading" of hazards was not fully aligned with objective information on occupational accidents but depended on individual characteristics. Having family responsibilities or being unionized increased workers' risk perception, whereas belonging to a workgroup with higher accident rates reduced it. PRACTICAL APPLICATIONS: Knowing how workers perceive risk and how this perception deviates from statistical information on accidents are essential for management to accurately design safety measures. In this regard, specific characteristics such as age, having dependents in the family, or the typology of the workers' workgroup should be taken into account. Greater knowledge of preventive measures will improve the way workers perceive risk, and ultimately contribute to reducing the likelihood of occupational accidents.

The impact of urban integration on the intention of settlement and residence among migrants: The empirical evidence from China.

Promoting urban integration is the key to improving the efficiency of labor allocation in developing countries and promoting coordinated regional development. Using the data of the migrants' observation sample in 2012-2017 released by China Migrants Dynamic Survey, this paper draws on theories related to urban integration and labor migration to study the urban integration status, intention of settlement and residence in China's agricultural household labor force in depth, as well as the correlation between the two. The study finds a significant decline in the intention of settlement and a gradual increase in the intention of residence among the migrants. The empirical regressions suggest that good urban integration, especially psychological integration, mainly contributes to their intention of settlement and residence, while the intention of residence is also more influenced by the social and economic integration dimensions. The mediating effect tests reveal that income level and housing stability are key pathways to economic integration, public medical participation and accessibility to medical services are key pathways to health integration, social status and local attachment are key pathways to social integration, and identity affiliation and psychological assimilation are key pathways to psychological integration. Future urbanization in developing countries requires not only further reform of the household registration system but also government, business, and social organizations at all levels to reduce the difficulties of urban integration in economic, health, social, and psychological aspects, enhance the degree of urban integration of the migrants, increase the intention of the migrants to settle and stay in different places and avoid the "migratory bird" migration of labor between regions. The "migratory bird" migration between regions is avoided.

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.

The links between parental smoking and childhood obesity: data of the longitudinal study of Australian children.

Childhood obesity is one of the most concerning public health issues globally and its implications in mortality and morbidity in adulthood are increasingly important. This study uses a unique dataset of Australian children aged 4-16 to examine the impact of parental smoking on childhood obesity. It confirms a significant link between parental smoking (stronger for mothers) and higher obesity risk in children, regardless of income, age, family size, or birth order. Importantly, we explore whether heightened preference for unhealthy foods can mediate the effect of parental smoking. Our findings suggest that increased consumption of unhealthy foods among children can be associated with parental smoking.

Model averaging estimation of panel data models with many instruments and boosting.

Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other is that the gain of using FE-2SLS may not exceed its loss when the endogeneity is weak. In this paper, an L2Boosting regularization procedure for panel data models is proposed to tackle the many instruments issue. We then construct a Stein-like model-averaging estimator to take advantage of FE and FE-2SLS-Boosting estimators. Finite sample properties are examined in Monte Carlo and an empirical application is presented.

Modified inverse propensity weighting method to alleviate estimation errors in the model with multiple endogenous variables.

Multiple mental health disorders affect on decisions of people. The disorders are also outcomes of other factors. Health studies commonly follow an inverse propensity weight (IPW) method to address estimation errors associated with the presence of one confounder or covariate number exceeding the recommended sample size. However, approaches of IPW appropriate to alleviate the estimation error associated with multiple confounders distributed unequally in the study samples were not explained in our search literature. This study used longitudinal cohort data from Christchurch Health and Development Study and demonstrated IPW approach to address two confounders with similar natures in terms of etiological process. In our sample, some individuals had no mental health disorder at all, while others had either one of depression or anxiety or both. The methodological step to evaluate a new IPW approach include * Estimated IPWs from all possible combinations of the major depression and anxiety disorder: (a) IPW based on anxiety factor only assuming both mental health problems resulted from the same etiological processes; (b) IPW based on major depression factor only assuming both mental health problems resulted from the same etiological processes; (c) IPW assuming three (independent) categories of etiological processes: neither; either; both of major depression or anxiety disorder, (d) IPW assuming four (independent) categories of etiological processes: neither; major depression only; any anxiety disorder only; both. (e) No IPW or control model (no confounding problem.•Estimated outcome model with one each IPW at a time and one without IPw (control model).•Compared fit statistics of all estimated models.•The IPW derived assuming four categories of etiological processes produced the robust based fit statistics criteria. The study showed significant effects of both mental health problems on investment but the anxiety revealed a stronger effect than that of major depression.

The impact of fiscal decentralization on economic growth: A comparative analysis of selected African and OECD countries.

Fiscal decentralization, widely believed to be one essential tool to boost economic growth, is typically presented as something that works for developed countries. However, this needs to be investigated, which is what this paper aims to do. This paper investigates the relationship between fiscal decentralization and economic growth, specifically focusing on its applicability in both developed and developing countries. Using cross-sectional data from 23 African and 23 OECD countries, we employ two-stage estimation methods, including two-stage least squares (2SLS), Generalized Method of Moments (GMM), and Limited Information Maximum Likelihood (LIML), to address concerns related to endogeneity. The inclusion of instruments representing country size, ethnoreligious diversity, and administrative structure enhances the analysis. The dependent variable is based on average values for the five-year period between 2015 and 2019, while the explanatory variables are derived from data in 2017, representing the midpoint of the 2013-2021 period. The results highlight the effectiveness of the 2SLS method in estimating the relationship between fiscal decentralization, control variables, instrument variables, and per capita GDP. Empirical findings indicate significant positive impacts of both expenditure decentralization and revenue decentralization on per capita GDP, holding true for both developed and developing countries, with a slightly stronger effect observed in the latter. These results underscore the potential benefits of fiscal decentralization across diverse economies and offer valuable insights for policymakers. The study uses cross-sectional data from 23 African and 23 OECD countries and applies two-stage estimation methods to effectively address the endogeneity issue in the modeling. A variety of such methods in two-stage least squares (2SLS), Generalized Method of Moments (GMM), and Limited Information Maximum Likelihood (LIML) are applied and compared against the ordinary least squares (OLS) method. Variables that represent the size, the ethnoreligious diversity and the type of administrative structure of the countries are employed as instruments. The 2SLS method proved to excellently estimate the relationship between the fiscal decentralization terms as well as other control and instrument variables and the per capita GDP. The findings from the empirical analysis showed that both expenditure decentralization and revenue decentralization have significant positive impacts on per capita GDP of a country. This relationship holds not only for developed countries but also for developing ones, even to a slightly better degree in the latter.

Addressing endogeneity between hazardous actions and motorcyclist injury severity by integrating generalized propensity score approach and instrumental variable model.

Motorcyclist hazardous actions (e.g., particularly speed too fast or failing to stop in assured clear distance (ACD)) are commonly identified as risk factors that significantly impact the motorcyclist injury severity. However, endogenous effects resulting from motorcyclist hazardous actions have seldom been considered, which may cause the biased estimates. Specifically, two important sources of endogeneities (i.e., endogeneity arising from observed confounding factors and endogeneity caused by unobserved confounders) tend to yield a biased relationship between hazardous actions and motorcyclist injury severity. To jointly account for two sources of endogeneities and provide more robust estimates, the study tries to assess the effects of speed-too-fast and failing to stop in ACD on motorcyclist injury severity via a hybrid method by integrating the generalized propensity score approach with instrumental variable model. Specifically, we adopt a generalized propensity score matching method to reduce the endogeneity bias arising from observed confounders. Furthermore, the matched data are used to develop an instrumental variable model with random parameters to handle the endogeneity resulting from unobserved confounders and unobserved heterogeneity, which consists of random parameters binary logit models modelling the motorcyclist hazardous actions in the first stage and a random parameters logit model with heterogeneity in means modelling the motorcyclist injury severity in the second stage. The proposed approach is estimated based on Michigan motorcycle crash data from 2015 to 2018. Results suggest that alcohol use leads motorcyclists to engage in speed-too-fast, while alcohol use and signal control cause motorcyclists to be involved in failing to stop in ACD. Middle-aged and elderly motorcyclists, alcohol use, speed too fast, speed limit ≥50 mph, wet surface, and head-on/angle crashes significantly increase the injury severity of motorcyclists. Moreover, failing to stop in ACD produces a random parameter with heterogeneity in means, while intersection increases the mean effects of failing to stop in ACD on motorcyclist minor injury. These findings further provide insights for a better understanding of hazardous actions and motorcyclist injury severity via the impact analysis of various explanatory variables.

How important is it to avoid indices of deprivation that include health variables in analyses of health inequalities?

OBJECTIVES: This study aimed to quantify the difference in mortality inequalities using the Scottish Index of Multiple Deprivation (SIMD) and the Income and Employment Index (IEI; a subindex of SIMD, which excludes health) as ranking measures in Scotland. STUDY DESIGN: This ecological study was a cross-sectional analysis of routine administrative data. METHODS: Data from the 2020 SIMD and the subindex using data from only the Income and Employment domains, the IEI, were obtained. The correlation between data zones, percentage of data zones that changed deprivation tenth and differences in the Slope Index of Inequality (SII) and Relative Index of Inequality (RII) for Standardised Mortality Ratios (SMRs) across tenths were compared when data zones were ranked by SIMD and IEI. RESULTS: There was a close correlation between data zones ranked by SIMD and IEI (R2 = 0.96). When data zones were ranked by IEI, 18.7% of data zones moved to a lower deprivation tenth, and 20.8% of data zones moved to a higher deprivation tenth, compared with SIMD. However, only a negligible number of data zones moved two or more tenths. The SMRs across deprivation tenths were very similar between the SIMD and IEI, as were the summary health inequality measures of SII (87.3 compared with 85.7) and RII (0.88 and 0.86). CONCLUSION: Although there is a logical problem in using deprivation indices that include health outcomes to rank areas to calculate the scale of health inequalities, the impact of using an alternative subindex containing only data from the income and employment domains is minimal. For population-wide analyses of health inequalities in Scotland, the SIMD does not introduce a substantial bias in the health inequalities summary measures despite substantial movement of small areas between ranked population tenths. Although not examined here, this is likely to be relevant to other similar indices across the United Kingdom.

Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.

Existing methods for estimation of dynamic treatment regimes are mostly limited to intention-to-treat analyses-which estimate the effect of randomization to a particular treatment regime without considering the compliance behavior of patients. In this article, we propose a novel nonparametric Bayesian Q-learning approach to construct optimal sequential treatment regimes that adjust for partial compliance. We consider the popular potential compliance framework, where some potential compliances are latent and need to be imputed. The key challenge is learning the joint distribution of the potential compliances, which we accomplish using a Dirichlet process mixture model. Our approach provides two kinds of treatment regimes: (1) conditional regimes that depend on the potential compliance values; and (2) marginal regimes where the potential compliances are marginalized. Extensive simulation studies highlight the usefulness of our method compared to intention-to-treat analyses. We apply our method to the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) study , where the goal is to construct optimal treatment regimes to engage patients in therapy.

Empirical examination of interdependent relationship between usage of seatbelt restraint system and driver-injury severity of single-vehicle crashes in Thailand using a joint econometric analysis.

OBJECTIVES: The paper aims to examine the interdependent relationship between the usage of the seatbelt restraint system and severities of the driver-injury in single-vehicle crashes. METHODS: This paper developed a comprehensive joint econometric structure - a joint random parameters binary probit-binary probit model - that allows for the simultaneous examination of injury severity of the driver in a crash, and taking into account the fact that seat belt use can be endogenous to the outcomes of driver injury. The developed model is tested using data on drivers-injury severities involved in single-vehicle crashes in Thailand from 2012-2017. RESULTS: In terms of the interdependent relationship between seatbelt use status and driver-injury severities, the findings suggest that drivers who do not use seat belts may demonstrate more dangerous or aggressive driving behaviors (such as speeding), subsequently increasing their likelihood of involvement in severe or fatal crashes. Additionally, the result also shows that drivers who are involved in speeding-related crashes are less likely to wear a seatbelt and have a higher risk of sustaining severe and fatal injuries. The findings also reveal that in crashes, drivers who are young, or operating trucks are less likely to be wearing their seat belts. The study also indicates that severe and fatal crashes are associated with factors such as elderly drivers, alcohol involvement, unbelted drivers, fatigue, depressed medians, and barrier medians. Conversely, a crash in a U-turn area, driving a passenger car, pickup truck, or large truck, or colliding with a guardrail reduces the likelihood of severe and fatal injuries. CONCLUSIONS: Neglecting the hidden endogenous effect in statistical analyses could result in an overestimation of the impact of seat belt usage on crash-injury outcomes. The findings of this study can provide valuable insights for relevant authorities aiming to improve driver safety.

Anti-colonial thought and global social theory.

From the late 1980s onward, global social theory has been introduced to a new perspective variously called indigeneity, endogeneity, Orientalism, Eurocentrism, post-colonial, decolonial, and Southern sociology/social sciences. This study argues that the above-mentioned trends should be collectively termed anti-colonial social theory as all of these explore the relationship between colonialism and knowledge production. The study divides the growth of anti-colonial social theory in terms of two phases and relates it to changing geopolitics of the 20th century. It argues that these distinct trends manifest a united stance in its ontological-epistemic articulation. It also argues that anti-colonial social theory can play a relevant role in a knowledge system divided through colonial/imperial relationships, given its theorization on the same.

Effect of supplementary private health insurance on inpatient utilisation: Evidence from Malaysia.

Supplementary private health insurance (PHI) provides better access to healthcare, improves health outcomes, potentially lowers the costs for health systems and supports the social security system. Improperly regulated PHI, however, may aggravate inequity of access towards preferential care and encourage moral hazard among PHI purchasers, altering the health-seeking behaviour, which is often observed through the pattern of health care utilisation. We investigated the effect of PHI ownership on private inpatient care utilisation, its frequency of admission and length of stay by conducting secondary data analysis of the Malaysian National Health Morbidity Survey (NHMS) 2015 data, a nationally representative community health survey. Malaysian adults 18 years of age and above who utilised inpatient healthcare facilities were included. In this cross-sectional study, we addressed the endogeneity effect of health insurance by employing instrumental variable estimation and a two-stage residual inclusion analysis. We found a significant increase in private inpatient utilisation among those who owned PHI compared to those who did not (ß = 4.39, p < 0.001). There was no significant difference in the frequency of admission and length of stay. The increase in private inpatient utilisation among PHI owners may reflect the demand for timely care and hospitality provided by the private sector, potentially exacerbating the moral hazard behaviour among PHI owners. Further exploration of this issue could impact future healthcare systems financing designs and PHI regulation.

Assessing the risk of endogeneity bias in health and mortality inequalities research using composite measures of multiple deprivation which include health-related indicators: A case study using the Scottish Index of Multiple Deprivation and population health and mortality data.

The inclusion of health-related indicators in composite measures of multiple deprivation introduces a risk of endogeneity bias when using the latter in health inequalities research. This bias may ultimately result in the inappropriate allocation of healthcare resources and maintenance of preventable health inequalities. Mitigation strategies to avoid this bias include removing the health-related indicators or using single constituent domains (such as income or employment class) in isolation. These strategies have not been widely validated. This study used population-level health and mortality data with a contemporary composite measure of multiple deprivation (Scottish Index of Multiple Deprivation; SIMD) to assess these mitigation strategies. The differences between deprivation methods (original, health excluded, and income domain) were negligible. The results of quantitative research on health inequalities are unlikely to be affected by endogeneity bias.

The Effect of Ecological Management on Regional Health Inequality.

Ecological management has been implemented to improve individual well-being. However, it remains unclear whether this management has improved health inequality over time. Aiming to examine whether health inequality is caused by ecological management in China, we harnessed a macro-level dataset from 2001 to 2019 across 31 Chinese provinces-combined with gene and dietary culture data-and utilized a bilateral approach to pair provincial data. Empirical results of system Generalized Method of Moments (sys-GMM) estimations in benchmark and extensive models which suggest a negative and statistically significant causal effect of ecological management on health inequality. Specifically, ecological management contributes to decreasing the inequality in the population death rate, the death rate among pregnant women, the underweight newborn rate, the child malnutrition rate, and the infectious disease mortality. The results are robust to weak instruments in the sys-GMM setting and a delayed effect of ecological management. Additionally, the heterogeneity analysis shows that the causal effect of ecological management on decreasing regional health inequality is more significant and higher for subsamples in identical regions than in different regions.

Can the Digital Economy Promote the Upgrading of Urban Environmental Quality?

As the core of economic development, the digital economy plays an essential role in promoting urban environmental quality. In this study, we constructed a comprehensive indicator system using two dimensions, i.e., the internet and digital finance, to measure the development situation of the urban digital economy, and we used principal component analysis to assess it. From the three perspectives of ecological environment state, ecological environment pollution degree, and ecological environment governance ability, the entropy method was used to measure the quality of the urban environment. On the basis of panel data from 275 cities (prefecture-level and above) in China from 2011 to 2019, we empirically analyzed the impact of the digital economy on urban environmental quality using the two-way fixed effect model and spatial Dubin model. The research shows that the digital economy significantly promotes urban environmental quality upgrades. This conclusion still holds when considering endogeneity. This effect is mainly achieved by promoting technological innovation, optimizing the industrial structure, and enhancing market competition. Further research demonstrated that the digital economy does not significantly impact the improvement of environmental quality in small- and medium-sized cities, but has a positive effect on environmental quality upgrading in large cities. The development of the digital economy promoted urban environmental quality upgrading in the region. However, the development of the digital economy has no significant impact on environmental quality upgrading in surrounding areas.

The Impact of Labor Force Participation on Elderly Health in China.

In the context of the deepening of population aging and the trial implementation of a progressive retirement delay policy in China, understanding the relationship between the labor force participation and health status of the elderly will not only enrich relevant research but also help the elderly better achieve their goals of active aging and aging. Using the 2018 China Health and Retirement Longitudinal Study, this paper first established multiple linear regression models to analyze the impact of labor force participation on the health status of elderly people in China and then established simultaneous equation models using households living on minimum living allowances and the community average of labor participation as instrumental variables to deal with the endogeneity caused by two-way causality. The findings confirmed significant positive correlations between labor force participation and physical and mental health, while caring for grandchildren and participating in social activities were found to be negatively moderated the relationship between labor force participation and the physical and mental health of older adults. The impact of labor force participation on the physical health status of older men and the mental health status of older women may be greater. In addition, labor force participation may have a greater impact on the physical health of the rural elderly, and its impact on mental health was not found to be statistically significant between urban and rural areas.

Further compelling evidence for safety-in-numbers: It is more than meets the eye.

In the extant road safety literature, estimating safety-in-numbers is dominated by conventional cross-sectional methods in which active mode (pedestrian or cyclist) volume together with motorised traffic volume are present in regression models explaining active mode safety directly. There is "direct" evidence for safety-in-numbers when the coefficient associated with active mode volume is negative (safety improves as volume increases) or when it is smaller than one (safety decreases at a lower rate compared to the rate of increase in active mode volume). In this article we extend the concept of safety-in-numbers in the traffic safety field, introducing "indirect" safety-in-numbers, which constitutes a new form of evidence for this phenomenon. We provide empirical evidence to support this, discussing that using an approach based on heterogeneity in mean modelling-a form of random parameters (slopes) models-it is possible to reveal "indirect" safety-in-numbers effects. Therefore, such models can reveal further compelling evidence for safety-in-numbers. Accurate knowledge of safety-in-numbers effects (both direct and indirect) and their underlying mechanisms can help provide robust motives for promoting active travel and will have valuable implications for the design of road safety interventions.