Significance Most of the population in developing countries live in places with unsafe air. Utilizing variations in transitory and cumulative air pollution exposures for the same individuals over time in China, we provide evidence that polluted air may impede cognitive ability as people become older, especially for less educated men. Cutting annual mean concentration of particulate matter smaller than 10 μm (PM10) in China to the Environmental Protection Agency’s standard (50 μg/m3) would move people from the median to the 63rd percentile (verbal test scores) and the 58th percentile (math test scores), respectively. The damage on the aging brain by air pollution likely imposes substantial health and economic costs, considering that cognitive functioning is critical for the elderly for both running daily errands and making high-stake decisions.

Abstract This paper examines the effect of both cumulative and transitory exposures to air pollution for the same individuals over time on cognitive performance by matching a nationally representative longitudinal survey and air quality data in China according to the exact time and geographic locations of the cognitive tests. We find that long-term exposure to air pollution impedes cognitive performance in verbal and math tests. We provide evidence that the effect of air pollution on verbal tests becomes more pronounced as people age, especially for men and the less educated. The damage on the aging brain by air pollution likely imposes substantial health and economic costs, considering that cognitive functioning is critical for the elderly for both running daily errands and making high-stake decisions.

While a large body of literature has shown that air pollution harms human health, in terms of life expectancy (1), illness and hospitalization (2), child health (3), health behavior (4), and dementia (5⇓–7), knowledge about the potential consequences of air pollution on cognitive abilities is more limited. A few existing studies on the impact of air pollution on cognition have mainly focused on young students (8⇓⇓–11). It is unclear whether their findings hold for the whole population or not, in particular for older cohort. Our paper fills this knowledge gap by examining the pollution–cognition relationship by age in China based on a nationally representative longitudinal dataset at the individual level.

We find that air pollution impairs verbal tests, and the effect becomes stronger as people age, especially for less educated men. Cognitive decline or impairment are risk factors of Alzheimer’s disease and other forms of dementia for elderly persons. As the most expensive form of cognitive decline, Alzheimer’s disease alone costs $226 billion of health services and 18 billion labor hours of unpaid caregiving in 2015 (6). Moreover, given that senior citizens have to make a host of complex high-stake economic decisions, such as purchasing health insurance and planning retirement, the decay in cognitive ability induced by air pollution will likely impair the quality of the important decisions (12). The damage on the aging brain by air pollution likely imposes substantial health and economic cost, which has been neglected in the policy discourse. Therefore, the finding on the detrimental effect of air pollution on the aging brain has important policy implications.

On the technical level, our paper has tried to overcome several common challenges facing this strand of empirical studies. First, we address the potential problem of omitted variables, which may be correlated with both cognition and exposure to air pollution, on estimation bias by using a panel data at the individual level. Most studies, except for those of Ebenstein et al. (10) and Marcotte (13), fail to account for individual-level heterogeneity due to data limitation. For instance, Ham et al. (8) only control for school-grade fixed effects; Bharadwaj et al. (14) include only sibling fixed effects. In this study, because we have access to a longitudinal dataset, the China Family Panel Studies (CFPS), we can remove individual-level unobservable factors.

Second, we have matched exposure to local environmental stressors with individual cognitive performance according to the exact time of test taking. This is more precise than in previous studies, for instance, that of Ham et al. (8), who match yearly air pollution with average standardized test scores at the school-grade level. Third, most existing studies consider either the effects of transitory or cumulative exposure to air pollution, but rarely both effects simultaneously, except for Marcotte (13). For example, Ham et al. (8) and Ebenstein et al. (10) focus on contemporaneous exposure; Bharadwaj et al. (14), Molina (15), and Sanders (16) examine the effect of cumulative exposure. We are among the first to examine the cognitive impact of cumulative exposure to air pollution while controlling for contemporaneous exposure. By controlling for the latter, we can evaluate the relative importance of transitory and accumulative effects. We find that the accumulative effect dominates.

Given that cognitive ability shapes human behavior and decision making, our result provides supporting evidence on the findings about the negative effect of air pollution on decision making (7, 17), risk attitude (11), and behavior (11, 18). The damage on cognitive ability by air pollution also likely impedes the development of human capital. In fact, a few studies have found that exposure to air pollution lowers educational attainment (10, 16) and results in lower labor productivity (19⇓⇓–22).

Air pollution is a ubiquitous problem in developing countries. According to the global ambient air pollution database compiled by the World Health Organization (www.who.int/phe/health_topics/outdoorair/databases/cities/en/), the top 20 most polluted cities are all in developing countries. Almost all of the cities (98%) in low- and middle-income countries with more than 100,000 residents fail to meet World Health Organization air quality guidelines. Therefore, the research findings on China, the largest developing country with severe air pollution, can also shed light on other developing countries.

The remainder of the paper is organized as follows. Data Sources describes the data, and Econometric Model lays out the empirical strategy. Empirical Results presents our main findings. Conclusions provides some conclusions. In SI Appendix, we also discuss the scientific background of this study and potential mechanisms in detail.

Data Sources The dataset for this analysis is based on several sources. The cognitive test scores come from the CFPS, a nationally representative survey of Chinese families and individuals. The waves 2010 and 2014 contain the same cognitive ability module, that is, 24 standardized mathematics questions and 34 word-recognition questions. All of these questions are sorted in ascending order of difficulty, and the final test score is defined as the rank of the hardest question that a respondent is able to answer correctly. The survey also provides exact information about the geographic locations and dates of interviews for all respondents, which enables us to match test scores with local air quality data more precisely. Air quality is measured using the air pollution index (API), which is calculated based on daily readings of three air pollutants, namely sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and particulate matter smaller than 10 μm (PM10). The API ranges from 0 to 500, with larger values indicating worse air quality. Daily API observations are obtained from the city-level air quality report published by the Chinese Ministry of Environmental Protection. The report includes 86 major cities in 2000 and covers most of the cities in China in 2014. Our analysis also includes rich weather measures on the interview date, enabling us to separate the impact of air pollution from general weather patterns. The weather data are derived from the National Centers for Environmental Information of the US National Oceanic and Atmospheric Administration. The dataset contains daily records of rich weather conditions from 402 monitoring stations in China. We match city-level API with CFPS samples in the following way. If a CFPS county is within an API reporting city, we use the city’s API readings as the county’s readings. If a CFPS county is not located in any cities with API readings, we match it to the nearest API reporting city within a radius of 40 km according to the distance between the CFPS county centroid and the city boundaries. In SI Appendix, Part 2: Description of Data, we show the results are robust to a wide range of matching radiuses and alternative matching strategies. The final dataset used in this study includes 31,959 observations. SI Appendix describes the data and the matching procedure in greater detail.

Econometric Model Our baseline econometric specification is as follows: Score i j t = α 1 P j t + α 2 ⋅ 1 k ∑ n = 0 k − 1 P j , t − n + X ′ i j t β + W ′ j t ϕ + T ′ j t γ + λ i + δ j + η t + f ( t ) + ε i j t . [1]The dependent variable Score ijt is the cognition test scores of respondent i in county j at date t. P jt is the contemporaneous air quality measure at date t. The key variable ( 1 / k ) ∑ n = 0 k − 1 P j , t − n is the mean API reading in the past k days, which measures cumulative exposure. X ijt is a set of the observable demographic correlates of the respondents. We also control for a vector of contemporaneous weather conditions W jt and a vector of county-level characteristics T jt to account for factors that are correlated with both test scores and air quality. λ i denotes individual fixed effects. δ j represents county fixed effects, which cannot be wiped out by individual fixed effects since some respondents do not live in the same counties across the two waves. η t indicates month, day of week, and postmeridiem hour fixed effects. f(t) is the quadratic monthly time trend that ranges from 1 (January 2010) to 60 (December 2014). ε ijt is the error term. SEs are clustered at the county level. By conditioning on the individual fixed effects, the key parameters are identified by making use of variations in exposure to air pollution for the same respondent in the 2010 and 2014 surveys. SI Appendix, Fig. S1 displays the monthly distribution of interview times in the two waves of the CFPS survey. Although a majority of interviews were conducted in July and August when college students were employed as numerators, the survey spans all months and seasons, providing us with large temporal variations. There is a concern that the results are mainly driven by the skewed sample distribution in the summer months, when air pollution is not as serious as in winter. SI Appendix, Fig. S11 and Table S12 also show that our findings still hold if giving an interview in winter greater weight than that in nonwinter so that the two periods share the same weight. The study was approved by the institutional review board (IRB) at Peking University (Approval IRB00001052-14010). All participants gave informed consent in accordance with policies of the IRB at Peking University.

Conclusions This paper estimates the contemporaneous and cumulative impacts of air pollution on cognition by matching the scores of verbal and math tests given to people age 10 and above in a nationally representative survey with local air quality data according to the exact dates and locations of the interviews. We find that accumulative exposure to air pollution impedes verbal test scores. As people age, the negative effect becomes more pronounced, especially for men. The gender gap is particularly large for the less educated. Our findings about the damaging effect of air pollution on cognition, particularly on the aging brain, imply that the indirect effect on social welfare could be much larger than previously thought. A narrow focus on the negative effect on health may underestimate the total cost of air pollution.

Acknowledgments We appreciate the Institute of Social Science Survey at Peking University for providing us with the CFPS data, and the Qingyue Open Environmental Data Center for the support on environmental data processing. We are grateful for comments from participants at various seminars and conferences: Institute of Labor Economics (IZA) (2016), Cornell (2016), University of Pennsylvania (2016), Yale (2016), Peking University (2016), Shanghai JiaoTong University (2016), University of Minnesota (2016), Tsinghua University (2016), US Council on Foreign Relations (2016), Keio University (2017), Shanghai University of Finance and Economics (2017), and Renmin University of China (2018). This study is funded by the Yale Macmillan Center Faculty Research Fund, the US Federal PEPPER Center Scholar Award (P30AG021342), two NIH/National Institute on Aging Grants (1 R03 AG048920 and K01AG053408), the China Postdoctoral Science Foundation Grants (2017M620653 and 2018T110057), and the Fundamental Research Funds for the Central Universities. The views expressed herein and any remaining errors are the authors’ and do not represent any official agency.

Footnotes Author contributions: X.C. and Xiaobo Zhang designed research; Xin Zhang, X.C., and Xiaobo Zhang performed research; Xin Zhang analyzed data; and Xin Zhang, X.C., and Xiaobo Zhang wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1809474115/-/DCSupplemental.