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16ZDA045). All remaining errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Purpose This paper aims to examine whether labour unions influence labour conflicts and this mechanism is different in China compared with other countries. Design/methodology/approach This paper uses the data from the China Employer–Employee Survey that interviewed 1,208 firms and 10,087 workers in 2016 as the measurement of variables, and it uses Logit regression model to do the empirical research. Findings Unions cannot significantly influence labour conflicts. More active unions and unions whose leaders are appointed by the firms’ management are associated with a higher incidence of labour conflicts. Originality/value This paper finds a new mechanism that explains the relationship between unions and labour conflicts. The existing literature states that unions may increase labour conflicts via “monopoly power” and may also mitigate labour conflicts via “voice mechanisms”. This paper’s findings show that the positive correlation between unions and labour conflicts may be explained by the lack of “voice mechanism” rather than “monopoly power”. The findings imply that labour unions should represent the interest of workers to mitigate the increasing labour conflicts.

The smartphone has become an indispensable electric device for most people since it can assist us in finishing many tasks such as paying and reading. Therefore, the security of smartphones is the most crucial issue to illegal users who cannot access legal users' privacy information. This paper studies identity authentication using user action. This scheme does not rely on the password or biometric identification. It checks user identity just by user action features. We utilize sensors installed in smartphones and collect their data when the user waves the phone. We collect these data, process them and feed them into neural networks to realize identity recognition. We invited 13 participants and collected about 350 samples for each person. The sampling frequency is set at 200 Hz, and DenseNet is chosen as the neural network to validate system performance. The result shows that the neural network can effectively recognize user identity and achieve an authentication accuracy of 96.69 percent.