广义AKM：工资分解中的灵活控制与交互作用
Generalized AKM: Flexible Controls and Interactions in Wage Decompositions
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中文总结 AI 辅助
该研究开发广义AKM框架用于工资分解，保留原方差成分，经实证分析发现控制项纳入对企业效应和匹配效应的影响大于其灵活程度，工人效应对方差基函数更敏感。
中文摘要 AI 辅助
工资差异在多大程度上归因于工人、企业及其匹配，取决于工资如何针对观测特征进行调整。标准AKM分解采用已知的线性调整方式。我们开发了广义AKM（Generalized AKM）框架，该框架允许存在未知的平滑协变量函数和组特定的非线性交互作用，同时保留原始方差成分。我们证明了在存在异方差误差和多个固定效应的情况下，该框架的一致性和渐近正态性，并确定了二次型所需的更强平滑性。在葡萄牙的雇主-雇员数据中，加入工人和企业投入控制项后，经偏差校正的工人效应方差从总工资方差的0.551降至0.474，企业效应方差从0.144降至0.121，匹配效应从0.080降至0.047。在三个组特定的非线性基函数下，企业效应方差保持在0.114至0.117之间，匹配效应保持在0.041至0.042之间，而工人效应方差的范围为0.474至0.491。控制项的纳入对企业效应方差和匹配效应的影响，比控制项纳入的灵活程度更大；工人效应对方差基函数的敏感性更高。
英文摘要
How much wage dispersion is attributed to workers, firms, and their sorting depends on how wages are adjusted for observed characteristics. Standard AKM decompositions impose a known linear adjustment. We develop Generalized AKM, a framework that permits an unknown smooth covariate function and group-specific nonlinear interactions while preserving the original variance components. We prove consistency and asymptotic normality with heteroskedastic errors and many fixed effects, and characterize the stronger smoothness required for quadratic forms. In Portuguese employer-employee data, adding worker and firm-input controls lowers the bias-corrected worker-effect variance from $0.551$ to $0.474$ of total wage variance, firm-effect variance from $0.144$ to $0.121$, and sorting from $0.080$ to $0.047$. Across three group-specific nonlinear bases, firm-effect variance remains between $0.114$ and $0.117$ and sorting between $0.041$ and $0.042$, while worker-effect variance ranges from $0.474$to $0.491$. Which controls enter matters more for firm variance and sorting than how flexibly they enter; worker variance remains more sensitive to the basis.
