Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico
本地收益大、净收益小:波多黎各首次行业进入的直接与溢出效应AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要使用市镇—行业季度面板开展双重差分,并显式估计空间网络干扰与溢出。
本地收益大、净收益小:波多黎各首次行业进入的直接与溢出效应AI 解读 ↓
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I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the municipio--industry level. Methodologically, I combine staggered-adoption difference-in-differences estimators that are robust to heterogeneous treatment timing with an imputation-based event-study approach, and I use a doubly robust difference-in-differences framework that explicitly allows for interference through pre-specified exposure mappings on a contiguity graph. The estimates show large and persistent direct gains in covered employment and wage bill in the treated municipality--industry cells over 0--16 quarters. Same-industry neighbors experience sizable short-run gains that reverse over the medium run, while within-municipality cross-industry and neighbor all-industries spillovers are small and imprecisely estimated. Once these spillovers are taken into account and spatially robust inference and sensitivity checks are applied, the net regional 0--16 quarter effect on covered employment is positive but modest in magnitude and estimated with considerable uncertainty. The results imply that first sizable entries generate substantial local gains where they occur, but much smaller and less precisely measured net employment gains for the broader regional economy, highlighting the importance of accounting for spatial spillovers when evaluating place-based policies.
网络依赖与干扰下双重差分的高效非参数估计
Differences-in-differences (DiD) is a causal inference method for observational longitudinal data that assumes parallel expected potential outcome trajectories between treatment groups under the counterfactual scenario where all units receive a specific treatment. In this paper DiD is extended to allow for: (i) non-identically distributed treatment effects and exposure probabilities; (ii) interference, where treatment of one unit can affect outcomes in neighboring units; and…
二部网络干扰下的双重差分:环境政策健康效应的准实验评估
Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of emissions control technologies on power plants. However, assessing the health impacts of these measures, particularly over longer periods of time, is complicated by several factors. First, the units that potentially receive the…
存在干扰时合成控制的稳健回归方法
Synthetic control methods are widely used for policy evaluation, but most existing approaches rule out interference among units, compromising validity when such effects are present. We develop a framework that accommodates contaminated donor pools and unknown interference patterns through two stages: factor-model adjustment for unobserved confounding, followed by robust regression in which direct and interference effects appear as a sparse outlier component. We study two…
时空因果推断的潜在因子面板方法
Unmeasured confounding can severely bias causal effect estimates from spatiotemporal observational data, especially when the confounders do not vary smoothly in time and space. In this work, we develop a method for addressing unmeasured confounding in spatiotemporal contexts by building on models from the panel data literature and methods in multivariate causal inference. Our method is based on a factor confounding assumption, which posits that effects of unmeasured…
存在溢出效应的合成控制方法:识别与贝叶斯推断
The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption (SUTVA), ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends standard SCM to account for spillovers and estimate both treatment and spillover effects. The approach…
空间与时间处理效应边界的统一框架:理论与识别
This paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both spatial and temporal dimensions. We formalize the concept of treatment effect boundaries as structural parameters characterizing regime transitions where causal effects cease to operate. Building on reaction-diffusion models of information propagation, we establish conditions under which spatial and temporal boundaries share common dynamics governed by…