ArticleFrontiers in psychology2026
Industry-level emotional-labor exposure proxy and employee well-being: the statistical indirect role of workplace equity perceptions.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Emotional labor is often examined as an individual emotion-regulation strategy, but national labor-force data can also capture the industry contexts in which emotional demands are more likely to occur. This study examines whether workplace equity perceptions are statistically consistent with an indirect association between an industry-level emotional-labor exposure proxy (IELR) and employee well-being. Methods: The analysis used the CLDS 2018 Individual Questionnaire file that is short for CLDS 2018 IID.dta in this study from the China Labor-force Dynamics Survey(CLDS) by the Center for Social Science Survey at Sun Yat-sen University in Guangzhou, China, Which contains 16,537 records and 1,517 variables. All 50 variables used in the analysis script were available. After retaining working respondents with industry data and applying a common complete analytical sample rule with at least three of five workplace-equity items, the main sample included 9,361 respondents. The paid-employee robustness sample included 1,963 respondents after native missing values for labor-contract and overtime variables were preserved. IELR was coded as a five-level industry exposure proxy, not as an individual emotional-labor scale. Province fixed effects were included in the reported models, and bootstrap confidence intervals were calculated for both the main and paid-employee samples. Results: In the main sample, IELR was positively associated with workplace equity perceptions (β = 0.063, Conclusion: Workplace equity is the more stable and substantively important correlate of well-being. IELR should be interpreted only as an exploratory industry-level exposure proxy, and all findings remain associational because the dataset is cross-sectional.
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