News Release

Study highlights the negative impact of air pollution on employees’ productivity

Peer-Reviewed Publication

KeAi Communications Co., Ltd.

Estimation coefficients for the impact of air pollution on labour productivity along API intervals.

image: Estimation coefficients for the impact of air pollution on labour productivity along API intervals. view more 

Credit: Dandan Zhang

Recent studies have highlighted the link between high levels of air pollution and poor health; for example, even short exposure to air pollutants can increase the risk of respiratory infections, heart disease and even lung cancer. Research has also shown that air pollution can negatively impact human capital; the attributes valued by employers, such as good communication skills, punctuality and emotional well-being.

However, little is known about the impact of air pollutants on labour productivity, largely because researchers attempting to quantify the relationship face two key challenges: 1) to ensure the measure is reliable, they need access to hours worked and payroll details over an extended period, which can be difficult to obtain, 2) it’s not easy to differentiate between the causal effect of air pollution on productivity and an employee’s own desire to work.

To overcome these obstacles, a research group in China used a unique dataset from a prison factory. Workers in the factory are not allowed to self-select their jobs and must attend work daily. In addition, they receive ‘piece-rate’ wages linked to the volume of units they complete, rather than the hours they work. Monthly wage records are also well-documented.

The study, which was published in the KeAi journal China Economic Quarterly International, found that, on average, a 10-unit increase in the API or air pollution index (a quantitative measure that describes ambient air quality) leads to a 4% reduction in the monthly piece-rate wages of inmates. This negative impact varies per demographic group: older and younger workers are more sensitive to air pollution than middle-aged workers, and workers with higher educational levels are more sensitive than those with lower educational levels. There is also a significant non-linear relationship between air pollution and labour productivity, which suggests that productivity falls rapidly as the API rises.

According to the study‘s corresponding author, Professor Dandan Zhang of Peking University’s China Center for Economic Research: “Understanding the broader impacts of air pollution on human behaviours is critical for optimal environmental policy design. Our findings emphasise the crucial policy implications for the protection of blue-collar workers from air pollution.”

She adds: “The non-linear relationship we found suggests that even short-term measures to alleviate extreme pollution and reduce its frequency can greatly mitigate the negative impact of air pollutants on labour productivity. For example, in recent years, warning systems have been introduced in Chinese cities such as Beijing, Xi'an and Jinan, which issue alerts when unfavourable air diffusion conditions are likely to trigger extreme air pollution. This allows the authorities to introduce short-term emergency measures, including driving restrictions, shutdown of construction sites, and restrictions on production of heavy industrial enterprises. It has been argued that these emergency measures may be accompanied by high economic costs, but this study suggests that the costs can be compensated, to some extent, by reduced losses in labour productivity.”


Contact the corresponding author: Dandan Zhang,

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 100 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

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