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Journal ArticleDOI

Air Pollution and Student Performance in the U.S.

TL;DR: In this paper , the relationship between air pollution and test scores was investigated in over 10,000 U.S. school districts and they found that declines in particulate pollution exposure raised test scores and reduced the black-white test score gap by 0.06 and 0.01 standard deviations, respectively.
Abstract: We combine satellite-based pollution data and test scores from over 10,000 U.S. school districts to estimate the relationship between air pollution and test scores. To deal with potential endogeneity we instrument for air quality using (i) year-to-year coal production variation and (ii) a shift-share instrument that interacts fuel shares used for nearby power production with national growth rates. We find that each one-unit increase in particulate pollution reduces test scores by 0.02 standard deviations. Our findings indicate that declines in particulate pollution exposure raised test scores and reduced the black-white test score gap by 0.06 and 0.01 standard deviations, respectively.
Citations
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Journal ArticleDOI
TL;DR: In this paper , the authors used standardized achievement data at the school-district-level for 3rd-through 8th-grade students for the entire United States from 2009-2016, and showed that variation in ambient PM2.5 concentrations and particularly polluted days reduce student learning.

1 citations

ReportDOI
TL;DR: A summary of the most prominent recent economic literature on the effects of air pollution, the main policy lessons that can be drawn from it, and the areas in which more research would be especially valuable is provided in this article .
Abstract: Air pollution is an increasing cause of concern among the scientific community, policymakers and the general public. This interest has led to a sharp increase in the number of scientific papers on air pollution. This paper provides a summary of the most prominent recent economic literature on the effects of air pollution, the main policy lessons that can be drawn from it, and the areas in which more research would be especially valuable. The literature has found sizable negative effects of air pollution on health and mortality. There is also some evidence that air pollution may have negative non-health effects, reducing labour supply and productivity, although the evidence is more mixed on the latter aspect. The literature also suggests that effects on both health and non-health dimensions may be heterogeneous in a number of dimensions, most prominently age, with more negative effects for the elderly. Finally, more research is needed on which policies to tackle air pollution would be more cost-effective.
References
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Posted Content
TL;DR: In this article, the authors developed estimation methods that use the amount of selection on the observables in a model as a guide to the amount that should be selected on the unobservables in order to identify the effect of the endogenous variable.
Abstract: We develop estimation methods that use the amount of selection on the observables in a model as a guide to the amount of selection on the unobservables. We show that if the observed variables are a random subset of a large number of factors that influence the endogenous variable and the outcome of interest, then the relationship between the index of observables that determines the endogenous variable and the index that determines the outcome will be the same as the relationship between the indices of unobservables that determine the two variables. In some circumstances this fact may be used to identify the effect of the endogenous variable. We also propose an informal way to assess selectivity bias based on measuring the ratio of selection on unobservables to selection on observables that would be required if one is to attribute the entire effect of the endogenous variable to selection bias. We use our methods to estimate the effect of attending a Catholic high school on a variety of outcomes. Our main conclusion is that Catholic high schools substantially increase the probability of graduating from high school and, more tentatively, college attendance. We do not find much evidence for an effect on test scores.

2,489 citations

Journal ArticleDOI
TL;DR: The typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares as discussed by the authors.
Abstract: The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares. Next, we show how the Bartik instrument weights each of the exposure designs. Finally, we discuss how to assess the plausibility of the research design. We illustrate our results through two applications: estimating the elasticity of labor supply, and estimating the elasticity of substitution between immigrants and natives.

529 citations

Journal ArticleDOI
TL;DR: This work develops geoscience-derived estimates of PM2.5 composition from a chemical transport model and satellite observations of aerosol optical depth and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation.
Abstract: An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM2.5) would offer valuable information for epidemiological studies and health impact assessments. We develop geoscience-derived estimates of PM2.5 composition from a chemical transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R2 = 0.57-0.96), with the strongest agreement for sulfate (R2 = 0.96), nitrate (R2 = 0.90), and ammonium (R2 = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM2.5) concentrations since 2000 have been most heavily influenced by regional changes in sulfate and organic matter. Regionally, the relative importance of several chemical components are found to change with PM2.5 concentration, such as higher PM2.5 concentrations having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM2.5 chemical components.

406 citations

Journal ArticleDOI
TL;DR: This model uses convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation and allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term.
Abstract: A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R2 of 0....

365 citations

Journal ArticleDOI
TL;DR: Global estimates of annual PM2.5 concentrations and trends for 1998-2018 are developed using advances in satellite observations, chemical transport modeling, and ground-based monitoring, identifying significant trends for eastern North America, Europe, and globally.
Abstract: Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m3/yr), Europe (-0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m3/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

359 citations

Trending Questions (2)
Does air pollution affect school performance?

Air pollution does affect school performance. Each one-unit increase in particulate pollution reduces test scores by 0.02 standard deviations.

How does air pollution affect student achievement?

Air pollution has a negative impact on student test scores, with each unit increase in particulate pollution reducing scores by 0.02 standard deviations.