Twisting the demand curve: Digitalization and the older workforce
扭转需求曲线:数字化与年长劳动力AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要以企业—工人面板估计软件投资收入效应,并报告年龄异质性与收入不平等变化。
扭转需求曲线:数字化与年长劳动力AI 解读 ↓
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Software represents a major and fast-growing share of firms’ capital investment, impacting demand for labor and what workers do on their jobs. Using U.S. Census Bureau panel data that link firms and workers, this paper estimates the effect of firm software capital on the earnings of workers by age group. We extend the AKM framework to include job-spell fixed effects that account for potential correlation between the worker–firm match and employee age, as well as including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, capitalized software investment raises worker earnings. However, it does so at a rate that declines after the age of 50, to about zero beyond 65. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and earnings in high-wage firms relative to low-wage firms, thereby widening earnings inequality within and across firms.
处理效应模型的面板数据分位数回归
In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020 Ishihara, T. (2020), “Identification and Estimation of Time-Varying Nonseparable Panel Data Models Without Stayers,” Journal of Econometrics, 215, 184–208. DOI: https://doi.org/10.1016/j.jeconom.2019.08.008.[Crossref] , [Google Scholar]) explores identification of the nonseparable panel data model under these…
条件张量扩散:分布式反事实学习与推断
Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of missing control outcomes. We develop \emph{Counterfactual Tucker Diffusion} (\CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score…
测地线双重差分
Summary Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes residing in non-Euclidean spaces remain limited. Existing methods for such outcomes have primarily focused on univariate distributions, leveraging linear operations in the space of quantile functions, but these approaches cannot be directly extended to outcomes in general metric spaces. In…
高维混杂下纵向数据的异质性分位数处理效应估计(第二版)
Causal inference plays a fundamental role in various real-world applications. However, in the motivating non-small cell lung cancer (NSCLC) study, it is challenging to estimate the treatment effect of chemotherapy on circulating tumor DNA (ctDNA). First, the heterogeneous treatment effects vary across patient subgroups defined by baseline characteristics. Second, there exists a broad set of demographic, clinical and molecular variables act as potential confounders. Third,…
随机对象与函数型数据的测地线合成控制
We introduce a geodesic synthetic control method for causal inference with panel outcomes that are random objects in a geodesic metric space. Examples include distributions, compositions, networks, symmetric positive-definite matrices, trees and functional data, among other data types that require a geometry beyond Euclidean vector spaces. The proposed method replaces Euclidean weighted averages by weighted Fréchet means and defines treatment effects as geodesics connecting…
Abstract We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way…