The Routine-Biased Technological Change (RBTC) has been regarded as a relatively novel technology-based explanation of social changes affecting job and wage polarization. In this paper, we investigate wage inequality between routine and non-routine workers along the wage distribution in Italy. Thanks to unique survey data, we can estimate the wage differential using both the actual and the perceived level of routine intensity of jobs to classify workers. We adopt semi-parametric decomposition techniques to quantify the importance of worker characteristics in explaining the gaps. We also employ non-parametric techniques to account for self-selection bias. We find evidence of a significant U-shaped pattern in the wage gap, according to both definitions, with non-routine workers always earning significantly more than routine workers. Results show that worker characteristics fully explain the gap in the case of perceived routine, while they account for no more than 50% of the gap across the distribution in the case of actual routine. Thus, the results highlight the importance of taking into account workers’ perceptions to reduce the set of omitted vaiables when analyzing determinants of wage inequality.

偏向常规的技术变革（RBTC）被认为是对影响工作和工资两极分化的社会变革的一种相对新颖的基于技术的解释。在本文中，我们调查了意大利工资分配中常规工人和非常规工人之间的工资不平等。得益于独特的调查数据，我们可以利用实际和感知的日常工作强度水平来估计工资差异，从而对工人进行分类。我们采用半参数分解技术来量化工人特征在解释差距方面的重要性。我们还采用非参数技术来解释自选择偏差。根据这两种定义，我们发现工资差距存在显着的 U 形模式的证据，非常规工人的收入总是明显高于常规工人。结果表明，工人特征充分解释了感知例行公事情况下的差距，而在实际例行公事情况下，他们所占的分布差距不超过 50%。因此，结果强调了在分析工资不平等的决定因素时考虑工人的看法以减少遗漏变量的重要性。