The Relationship Between Corruption Perception and Depression: A Multiple Mediation Model

Abstract

Background

Corruption perception is an important risk factor for depression. On the psychological level, corruption perception will cause negative emotions to individuals. On the physiological level, higher corruption perception may mean a more unfair social environment, which is not conducive to individuals’ health. However, the mechanism linking corruption perception and depression has not been fully understood.

Objective

To investigate how corruption perception affects depression, this study used trust in government and online news consumption as mediators to construct a multiple mediation model.

Methods

The data used in this study were derived from the 2016 wave and 2018 wave of China Family Panel Studies (CFPS). After eliminating samples with missing values, this study finally included 7845 samples. This study used Stata version 16.0 and a longitudinal research design to investigate the relationship between corruption perception and depression.

Results

The results revealed that the increase on corruption perception could aggravate depression (β = 0.037, p < 0.05). Meanwhile, trust in government partially mediated the effect of corruption perception on depression (indirect effect = 0.030, p < 0.001). Notably, online news consumption partially masked the effect of corruption perception on depression (indirect effect = −0.003, p < 0.01).

Conclusion

Trust in government and online news consumption may be two important mediators between corruption perception and depression. More attention should be paid to the relationship between corruption perception and depression, and mental health promotion interventions could be tailored to alleviate depression in the future.

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Keywords:

• corruption perception
• depression
• trust in government
• online news consumption

Data Sharing Statement

All the datasets can be accessed at the Peking University Open Research Data after being authorized.

Disclosure

The author reports no conflicts of interest in this work.