Econ Papers Daily / 期刊论文
低碳转型中的新兴技能与工资差距：来自在线招聘数据的证据
Emerging skills and wage gaps in the low-carbon transition: evidence from online job vacancy data
摘要
Standard occupational classifications obscure identifying which jobs directly con tribute to decarbonization. Using U.S. online job vacancy data, we develop a trans parent, skill-based approach that identifies low-carbon jobs within occupations, by combining advanced natural language processing with text linked to established green classifications. We show that low-carbon job creation is more prevalent in low-skilled occupations, yet low-carbon jobs systematically require more complex skill sets than comparable generic jobs in the same occupations. These higher skill requirements are associated with a modest wage premium that declines over time and is largely driven by firm fixed effects. Reskilling patterns and low-carbon wage premia vary substantially across occupations, and the latter are markedly smaller than the high-carbon wage premia, especially in STEM occupations. Finally, low carbon jobs are more spatially correlated with high-carbon employment than just renewable energy jobs, but are also more prevalent in wealthier areas.
中文摘要
标准职业分类难以明确识别哪些工作直接促进脱碳。利用美国在线职位空缺数据，我们开发了一种透明、基于技能的方法，通过结合先进自然语言处理与既定绿色分类相关文本，识别职业内部的低碳工作。研究表明，低碳岗位创造更多集中于低技能职业，然而低碳工作系统性地要求比同一职业中类似普通岗位更复杂的技能组合。这些更高的技能要求与适度的工资溢价相关，该溢价随时间递减，且主要由企业固定效应驱动。技能重塑模式与低碳工资溢价在不同职业间差异显著，且后者明显小于高碳工资溢价，尤其在STEM职业中。最后，低碳工作与高碳就业的空间相关性高于仅可再生能源岗位，但低碳工作在更富裕地区也更为普遍。