arXiv 22 Mar 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2303.12667 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes semi-instrumental variables (semi-IVs) as an alternative to instrumental variables (IVs) to identify the causal effect of a binary (or discrete) endogenous treatment. A semi-IV is a less restrictive form of instrument: it affects the selection into treatment but is excluded only from one, not necessarily both, potential outcomes. Having two continuously distributed semi-IVs, one excluded from the potential outcome under treatment and the other from the potential outcome under control, is sufficient to nonparametrically point identify marginal treatment effect (MTE) and local average treatment effect (LATE) parameters. In practice, semi-IVs provide a solution to the challenge of finding valid IVs because they are often easier to find: many selection-specific shocks, policies, prices, costs, or benefits are valid semi-IVs. As an application, I estimate the returns to working in the manufacturing sector on earnings using sector-specific characteristics as semi-IVs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Heckman and Sedlacek (1985) Heterogeneity, aggregation, and market wage functions: an empirical model of self-selection in the labor market | 1.000 | 11 | 3 | 100% |
| 2 | Carneiro, Heckman and Vytlacil (2011) Estimating Marginal Returns to Education | 1.000 | 9 | 4 | 100% |
| 3 | Heckman and Vytlacil (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects | 1.000 | 5 | 4 | 100% |
| 4 | Mogstad, Torgovitsky and Walters (2021) The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables | 0.928 | 5 | 3 | 80% |
| 5 | Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.903 | 19 | 6 | 74% |
| 6 | Heckman and Vytlacil (2007) Chapter 71 Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econo… | 0.894 | 7 | 3 | 71% |
| 7 | Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.885 | 13 | 5 | 69% |
| 8 | Heckman and Sedlacek (1990) Self-selection and the distribution of hourly wages | 0.874 | 7 | 2 | 100% |
| 9 | Andresen (2018) Exploring marginal treatment effects: Flexible estimation using Stata | 0.874 | 6 | 2 | 100% |
| 10 | Angrist, Imbens and Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 121 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing Exclusion and Shape Restrictions in Potential Outcomes Models | 0.405 | 1 | 1 |