The Experts below are selected from a list of 26145 Experts worldwide ranked by ideXlab platform
Mei Po Kwan - One of the best experts on this subject based on the ideXlab platform.
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assessment of sociodemographic disparities in environmental exposure might be erroneous due to Neighborhood Effect averaging implications for environmental inequality research
Environmental Research, 2021Co-Authors: Junghwan Kim, Mei Po KwanAbstract:Abstract The Neighborhood Effect averaging problem (NEAP) is a major methodological problem that might affect the accuracy of assessments of individual exposure to mobility-dependent environmental factors (e.g., air/noise pollution, green/blue spaces, or healthy food environments). Focusing on outdoor ground-level ozone as a major air pollutant, this paper examines the NEAP in the evaluation of sociodemographic disparities in people’s air pollution exposures in Los Angeles using one-day activity-travel diary data of 3790 individuals. It addresses two questions: (1) How does the NEAP affect the evaluation of sociodemographic disparities in people’s air pollution exposures? (2) Which social groups with high residence-based exposures do not experience Neighborhood Effect averaging? The results of our spatial regression models indicate that assessments of sociodemographic disparities in people’s outdoor ground-level ozone exposures might be erroneous when people’s daily mobility is ignored because of the different manifestations of Neighborhood Effect averaging for different social/racial groups. The results of our spatial autologistic regression model reveal that non-workers (e.g., the unemployed, homemakers, the retired, and students) do not experience downward averaging: they have significantly lower odds of experiencing downward averaging that could have attenuated their high exposures experienced in their residential Neighborhoods while traveling to other Neighborhoods (thus, being doubly disadvantaged). Therefore, to avoid erroneous conclusions in environmental inequality research and inEffective public policies, it would be critical to take the NEAP into account in future studies of sociodemographic disparities related to mobility-dependent environmental factors.
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how Neighborhood Effect averaging might affect assessment of individual exposures to air pollution a study of ozone exposures in los angeles
Annals of the American Association of Geographers, 2021Co-Authors: Junghwan Kim, Mei Po KwanAbstract:The Neighborhood Effect averaging problem (NEAP) can be a serious methodological problem that leads to erroneous assessments when studying mobility-dependent exposures (e.g., air or noise pollution...
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examining ethnic exposure through the perspective of the Neighborhood Effect averaging problem a case study of xining china
International Journal of Environmental Research and Public Health, 2020Co-Authors: Yiming Tan, Mei Po Kwan, Zifeng ChenAbstract:An increasing number of studies have observed that ignoring individual exposures to non-residential environments in people's daily life may result in misleading findings in research on environmental exposure. This issue was recognized as the Neighborhood Effect averaging problem (NEAP). This study examines ethnic segregation and exposure through the perspective of NEAP. Focusing on Xining, China, it compares the Hui ethnic minorities and the Han majorities. Using 2010 census data and activity diary data collected in 2013, the study found that NEAP exists when examining ethnic exposure. Respondents who live in highly mixed Neighborhoods (with high exposures to the other ethnic group) experience lower activity-space exposures because they tend to conduct their daily activities in ethnically less mixed areas outside their home Neighborhoods (which are more segregated). By contrast, respondents who live in highly segregated Neighborhoods (with low exposures to the other ethnic group) tend to have higher exposures in their activity locations outside their home Neighborhoods (which are less segregated). Therefore, taking into account individuals' daily activities in non-residential contexts in the assessment of environmental exposure will likely lead to an overall tendency towards the mean exposure. Using Tobit models, we further found that specific types of activity places, especially workplaces and parks, contribute to NEAP. Ignoring individual exposures in people's activity places will most likely result in misleading findings in the measurement of environmental exposure, including ethnic exposure.
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who could not avoid exposure to high levels of residence based pollution by daily mobility evidence of air pollution exposure from the perspective of the Neighborhood Effect averaging problem neap
International Journal of Environmental Research and Public Health, 2020Co-Authors: Mei Po Kwan, Yanwei ChaiAbstract:It has been widely acknowledged that air pollution has a considerable adverse impact on people’s health. Disadvantaged groups such as low-income people are often found to experience greater negative Effects of environmental pollution. Thus, improving the accuracy of air pollution exposure assessment might be essential to policy-making. Recently, the Neighborhood Effect averaging problem (NEAP) has been identified as a specific form of possible bias when assessing individual exposure to air pollution and its health impacts. In this paper, we assessed the real-time air pollution exposure and residential-based exposure of 106 participants in a high-pollution community in Beijing, China. The study found that: (1) there are significant differences between the two assessments; (2) most participants experienced the NEAP and could lower their exposure by their daily mobility; (3) three vulnerable groups with low daily mobility and could not avoid the high pollution in their residential Neighborhoods were identified as exceptions to this: low-income people who have low levels of daily mobility and limited travel outside their residential Neighborhoods, blue-collar workers who spend long hours at low-end workplaces, and elderly people who face many household constraints. Public policies thus need to focus on the hidden environmental injustice revealed by the NEAP in order to improve the well-being of these environmentally vulnerable groups.
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the Neighborhood Effect averaging problem neap an elusive confounder of the Neighborhood Effect
International Journal of Environmental Research and Public Health, 2018Co-Authors: Mei Po KwanAbstract:Ignoring people's daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people's exposures to and the health impact of environmental factors. This paper identifies and describes a phenomenon called Neighborhood Effect averaging, which may significantly confound the Neighborhood Effect as a result of such neglect when examining the health impact of mobility-dependent exposures (e.g., air pollution). Several recent studies that provide strong evidence for the Neighborhood Effect averaging problem (NEAP) are discussed. The paper concludes that, due to the observed attenuation of the Neighborhood Effect associated with people's daily mobility, increasing the mobility of those who live in disadvantaged Neighborhoods may be helpful for improving their health outcomes.
Douglas S Massey - One of the best experts on this subject based on the ideXlab platform.
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geographic Effects on intergenerational income mobility
Economic Geography, 2015Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:abstractResearch on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions—most notably access to high-quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom-quartile Neighborhood would have been raised in a top-quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect, and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models, and replicat...
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geographic Effects on intergenerational income mobility
Economic Geography, 2015Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:Research on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions—most notably access to high-quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom-quartile Neighborhood would have been raised in a top-quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect, and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models, and replicate the basic results using aggregated metropolitan-level statistics of intergenerational income elasticities based on millions of Internal Revenue Service records.
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geographic Effects on intergenerational income mobility
2014Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:Research on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions — most notably access to high quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom quartile Neighborhood would have been raised in a top quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models and replicate the basic results using aggregated metropolitan level statistics of intergenerational income elasticities based on millions of IRS records.
Yanwei Chai - One of the best experts on this subject based on the ideXlab platform.
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who could not avoid exposure to high levels of residence based pollution by daily mobility evidence of air pollution exposure from the perspective of the Neighborhood Effect averaging problem neap
International Journal of Environmental Research and Public Health, 2020Co-Authors: Mei Po Kwan, Yanwei ChaiAbstract:It has been widely acknowledged that air pollution has a considerable adverse impact on people’s health. Disadvantaged groups such as low-income people are often found to experience greater negative Effects of environmental pollution. Thus, improving the accuracy of air pollution exposure assessment might be essential to policy-making. Recently, the Neighborhood Effect averaging problem (NEAP) has been identified as a specific form of possible bias when assessing individual exposure to air pollution and its health impacts. In this paper, we assessed the real-time air pollution exposure and residential-based exposure of 106 participants in a high-pollution community in Beijing, China. The study found that: (1) there are significant differences between the two assessments; (2) most participants experienced the NEAP and could lower their exposure by their daily mobility; (3) three vulnerable groups with low daily mobility and could not avoid the high pollution in their residential Neighborhoods were identified as exceptions to this: low-income people who have low levels of daily mobility and limited travel outside their residential Neighborhoods, blue-collar workers who spend long hours at low-end workplaces, and elderly people who face many household constraints. Public policies thus need to focus on the hidden environmental injustice revealed by the NEAP in order to improve the well-being of these environmentally vulnerable groups.
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the anatomy of health supportive Neighborhoods a multilevel analysis of built environment perceived disorder social interaction and mental health in beijing
International Journal of Environmental Research and Public Health, 2019Co-Authors: Yinhua Tao, Jie Yang, Yanwei ChaiAbstract:Mental health is an exceedingly prevalent concern for the urban population. Mounting evidence has confirmed the plausibility of high incidences of mental disorders in socioeconomically disadvantaged Neighborhoods. However, the association between the Neighborhood built environment and individual mental health is understudied and far from conclusive, especially in developing countries such as China. The underlying mechanism requires in-depth analysis combining potential intermediates such as perceived environmental disorder and supportive social relationships. Using a health survey conducted in Beijing in 2017, this study investigates for the first time a socio-environmental pathway through which perceived disorder and social interaction account for the relationship between the built environment and mental health under the very notion of the Neighborhood Effect. The results from multilevel structural equation models indicate that individual mental health is influenced by the Neighborhood-scale built environment through three pathways, independent of Neighborhood socioeconomic disadvantages: (1) proximity to parks is the sole indicator directly linked to mental health; (2) population density, road connectivity and proximity to parks are indirectly associated with mental health through interactions with neighbors; and (3) population density, road connectivity and facility diversity are partially associated with perceived Neighborhood disorder, which is indirectly correlated with mental health through interactions with neighbors. This study is a preliminary attempt to disentangle the complex relationships among the Neighborhood environment, social interaction and mental health in the context of developing megacities. The relevant findings provide an important reference for urban planners and administrators regarding how to build health-supportive Neighborhoods and healthy cities.
Jonathan T Rothwell - One of the best experts on this subject based on the ideXlab platform.
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geographic Effects on intergenerational income mobility
Economic Geography, 2015Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:abstractResearch on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions—most notably access to high-quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom-quartile Neighborhood would have been raised in a top-quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect, and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models, and replicat...
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geographic Effects on intergenerational income mobility
Economic Geography, 2015Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:Research on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions—most notably access to high-quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom-quartile Neighborhood would have been raised in a top-quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect, and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models, and replicate the basic results using aggregated metropolitan-level statistics of intergenerational income elasticities based on millions of Internal Revenue Service records.
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geographic Effects on intergenerational income mobility
2014Co-Authors: Jonathan T Rothwell, Douglas S MasseyAbstract:Research on intergenerational economic mobility often ignores the geographic context of childhood, including Neighborhood quality and local purchasing power. We hypothesize that individual variation in intergenerational mobility is partly attributable to regional and Neighborhood conditions — most notably access to high quality schools. Using restricted Panel Study of Income Dynamics and census data, we find that Neighborhood income has roughly half the Effect on future earnings as parental income. We estimate that lifetime household income would be $635,000 dollars higher if people born into a bottom quartile Neighborhood would have been raised in a top quartile Neighborhood. When incomes are adjusted to regional purchasing power, these Effects become even larger. The Neighborhood Effect is two-thirds as large as the parental income Effect and the lifetime earnings difference increases to $910,000. We test the robustness of these findings to various assumptions and alternative models and replicate the basic results using aggregated metropolitan level statistics of intergenerational income elasticities based on millions of IRS records.
Michael J. Oakes - One of the best experts on this subject based on the ideXlab platform.
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twenty years of Neighborhood Effect research an assessment
Current Epidemiology Reports, 2015Co-Authors: Michael J. Oakes, Kate E Andrade, Ifrah M Biyoow, Logan T CowanAbstract:This paper reviews the magnitude and empirical findings of social epidemiological Neighborhood Effects research. An electronic keyword literature search identified 1369 empirical and methodological Neighborhood Effects papers published in 112 relevant journals between 1990 and 2014. Analyses of temporal trends were conducted by focus, journal type (e.g., epidemiology, public health, or social science), and specific epidemiologic journal. Select papers were then critically reviewed. Results show an ever-increasing number of papers published, notably since the year 2000, with the majority published in public health journals. The variety of health outcomes analyzed is extensive, ranging from infectious disease to obesity to criminal behavior. Papers relying on data from experimental designs are thought to yield the most credible results, but such studies are few and findings are inconsistent. Papers relying on data from observational designs and multilevel models typically show small statistically significant Effects, but most fail to appreciate fundamental identification problems. Ultimately, of the 1170 empirically focused Neighborhood Effects papers published in the last 24 years, only a handful have clearly advanced our understanding of the phenomena. The independent impact of Neighborhood contexts on health remains unclear. It is time to expand the social epidemiological imagination.
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Invited Commentary Invited Commentary: Repeated Measures, Selection Bias, and Effect Identification in Neighborhood Effect Studies
2014Co-Authors: Michael J. OakesAbstract:Research on Neighborhood Effects faces enormous methodological challenges, with selection bias being near the top of the list. In this issue of the Journal (Am J Epidemiol. 2014;000(00):0000–0000), Professor Jokela addresses this issue with novel repeated measures data and models that decompose putative Effects into those within and between persons. His contribution shows that within-person Neighborhood Effects are quite modest and that there is evidence of selection bias between persons. Like all research, the work rests on assumptions. Unfortunately, such assumptions are difficult to substantiate or validate in this context. A consequentialist epidemi-ologic perspective compels further innovation and a larger social epidemiologic imagination. causal; counterfactual; dynamic; methodology Professor Jokela’s new article (1) is a thoughtful and important contribution to the social epidemiologic literature addressing Neighborhood Effects. The research uses rich repeated-measures data, defensible Neighborhood quality measures, reasonable health measures, and an interesting set of analyses aimed at illuminating the problem of social selection, which has vexed researchers for many years
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the mis estimation of Neighborhood Effects causal inference for a practicable social epidemiology
Social Science & Medicine, 2004Co-Authors: Michael J. OakesAbstract:The resurgence of interest in the Effect of Neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel Neighborhood Effect studies. We show that identifying useful independent Neighborhood Effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public’s health.
