| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26174 |
| 来源ID | Working Paper 26174 |
| Selection into Identification in Fixed Effects Models, with Application to Head Start | |
| Douglas L. Miller; Na’ama Shenhav; Michel Z. Grosz | |
| 发表日期 | 2019-08-26 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Many papers use fixed effects (FE) to identify causal impacts of an intervention. In this paper we show that when the treatment status only varies within some groups, this design can induce non-random selection of groups into the identifying sample, which we term selection into identification (SI). We begin by illustrating SI in the context of several family fixed effects (FFE) applications with a binary treatment variable. We document that the FFE identifying sample differs from the overall sample along many dimensions, including having larger families. Further, when treatment effects are heterogeneous, the FFE estimate is biased relative to the average treatment effect (ATE). For the general FE model, we then develop a reweighting-on-observables estimator to recover the unbiased ATE from the FE estimate for policy-relevant populations. We apply these insights to examine the long-term effects of Head Start in the PSID and the CNLSY. Using our reweighting methods, we estimate that Head Start leads to a 2.6 percentage point (p.p.) increase (s.e. = 6.2 p.p.) in the likelihood of attending some college for white Head Start participants in the PSID. This ATE is 78% smaller than the traditional FFE estimate (12 p.p). Reweighting the CNLSY FE estimates to obtain the ATE produces similar attenuation in the estimated impacts of Head Start. |
| 主题 | Econometrics ; Estimation Methods ; Health, Education, and Welfare ; Education ; Poverty and Wellbeing ; Labor Economics ; Demography and Aging |
| URL | https://www.nber.org/papers/w26174 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/583847 |
| 推荐引用方式 GB/T 7714 | Douglas L. Miller,Na’ama Shenhav,Michel Z. Grosz. Selection into Identification in Fixed Effects Models, with Application to Head Start. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w26174.pdf（955KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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