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What explains protectionism in trade in services? The international economy is increasingly driven by cross-border services flows, and trade in services is recognized as the next engine of growth by international organizations and governments. However, the reality is that services remain characterized by ubiquitous but also differential protection. This article advances an employee-as-voter theory to account for this variation. The arguments are tested on a recently-compiled dataset covering 27 OECD countries in 14 services sectors over seven years (2014-20). I first estimate a multilevel OLS model as the basel ine, and then verify the results by using three machine learning algorithms including supporting vector regression, extreme gradient boosting, and deep neural network. The results support the thesis that the wage gap between the wage level of a sector and the national average explains the variation in the protection level. However, the effect of the wage gap is reduced under conditions of political polarization. Meanwhile, the analysis finds no evidence that a government’s ideology on economic affairs influences the supply of protection in trade in services. The machine learning algorithms also identify new patterns that are not captured by the linear structure of the multilevel OLS model. This research presents a fresh, bottom-up perspective to understand the important but overlooked issue of services trade.