Evidence map›Paper›PMID 34886582›Full record

ArticleInternational journal of environmental research and public health2021

The Labor Productivity Consequences of Exposure to Particulate Matters: Evidence from a Chinese National Panel Survey.

Qi He, Xinde James Ji

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. The Spatial Effect of Air Pollution Governance on Labor Productivity: Evidence from 262 Chinese Cities.International journal of environmental research and public health · 2022
    Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qi HeInternational Business School, Brandeis University, Waltham, MA 02453, USA.ORCID 0000-0002-0427-6210
Xinde James JiDepartment of Economics, Brandeis University, Waltham, MA 02453, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing body of literature has documented the negative impacts of air pollution on labor productivity, especially the effects of fine particulate matter. In this paper, we build on this literature by dissecting two channels of how particulate matter affects labor productivity: decreasing labor supply through damaging the physical functioning of the human body, and decreasing the marginal productivity of labor through damaging the cognitive functioning of the human brain. Using the household panel survey from the China Health and Nutrition Survey (CHNS) spanning 2000 to 2015 and combining that information with remotely sensed data on exposure to particulate matter (PM2.5), namely, the most harmful air pollution, we find a significantly negative effect of PM2.5 (instrumented by thermal inversion) on labor productivity. We also find that workers who are male, without a college degree, and are employed in outdoor occupations are mainly affected by PM2.5 through decreasing working hours, whereas college-educated workers employed in indoor occupations are mainly affected by PM2.5 through decreasing unit wages. We provide suggestive evidence that health impacts are behind our measured labor-productivity losses as we find significantly lower metrics in physical activity and increasing disease prevalence under higher exposure to PM2.5.

Indexed as

Air PollutantsAir PollutionAir Pollution, IndoorChinaEnvironmental MonitoringHumansMaleParticulate MatterAir PollutantsParticulate Matterair pollutionCHNSlabor productivityparticulate matter

Identifiers

PMID34886582
PMCPMC8657613

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.