On Quantile Treatment Effects, Rank Similarity, and Variation of Instrumental Variables (2311.15871v1)
Abstract: This paper investigates how certain relationship between observed and counterfactual distributions serves as an identifying condition for treatment effects when the treatment is endogenous, and shows that this condition holds in a range of nonparametric models for treatment effects. To this end, we first provide a novel characterization of the prevalent assumption restricting treatment heterogeneity in the literature, namely rank similarity. Our characterization demonstrates the stringency of this assumption and allows us to relax it in an economically meaningful way, resulting in our identifying condition. It also justifies the quest of richer exogenous variations in the data (e.g., multi-valued or multiple instrumental variables) in exchange for weaker identifying conditions. The primary goal of this investigation is to provide empirical researchers with tools that are robust and easy to implement but still yield tight policy evaluations.
- Abadie, A., J. Angrist, and G. Imbens (2002): “Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings,” Econometrica, 70, 91–117.
- Blundell, R., A. Gosling, H. Ichimura, and C. Meghir (2007): “Changes in the distribution of male and female wages accounting for employment composition using bounds,” Econometrica, 75, 323–363.
- Calafiore, G. and M. C. Campi (2005): “Uncertain convex programs: randomized solutions and confidence levels,” Mathematical Programming, 102, 25–46.
- Chernozhukov, V. and C. Hansen (2005): “An IV model of quantile treatment effects,” Econometrica, 73, 245–261.
- ——— (2013): “Quantile models with endogeneity,” Annu. Rev. Econ., 5, 57–81.
- Chesher, A. (2003): “Identification in nonseparable models,” Econometrica, 71, 1405–1441.
- ——— (2005): “Nonparametric identification under discrete variation,” Econometrica, 73, 1525–1550.
- D’Haultfœuille, X. and P. Février (2015): “Identification of nonseparable triangular models with discrete instruments,” Econometrica, 83, 1199–1210.
- Dong, Y. and S. Shen (2018): “Testing for rank invariance or similarity in program evaluation,” Review of Economics and Statistics, 100, 78–85.
- Frandsen, B. R. and L. J. Lefgren (2018): “Testing rank similarity,” Review of Economics and Statistics, 100, 86–91.
- Han, S. (2021): “Identification in nonparametric models for dynamic treatment effects,” Journal of Econometrics, 225, 132–147.
- Han, S. and S. Yang (2023): “A Computational Approach to Identification of Treatment Effects for Policy Evaluation,” arXiv preprint arXiv:2009.13861.
- Heckman, J. (1990): “Varieties of selection bias,” The American Economic Review, 80, 313–318.
- Heckman, J. J., J. Smith, and N. Clements (1997): “Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts,” The Review of Economic Studies, 64, 487–535.
- Hettich, R. and K. O. Kortanek (1993): “Semi-infinite programming: theory, methods, and applications,” SIAM review, 35, 380–429.
- Imbens, G. W. and J. D. Angrist (1994): “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475.
- Jun, S. J., J. Pinkse, and H. Xu (2011): “Tighter bounds in triangular systems,” Journal of Econometrics, 161, 122–128.
- Maasoumi, E. and L. Wang (2019): “The gender gap between earnings distributions,” Journal of Political Economy, 127, 2438–2504.
- Manski, C. F. (1990): “Nonparametric bounds on treatment effects,” The American Economic Review, 80, 319–323.
- ——— (1994): “The selection problem,” in Advances in Econometrics, Sixth World Congress, ed. by C. Sims, vol. 1, 143–70.
- ——— (1997): “Monotone treatment response,” Econometrica: Journal of the Econometric Society, 1311–1334.
- Manski, C. F. and J. V. Pepper (2000): “Monotone instrumental variables: With an application to the returns to schooling,” Econometrica, 68, 997–1010.
- Mogstad, M., A. Santos, and A. Torgovitsky (2018): “Using instrumental variables for inference about policy relevant treatment parameters,” Econometrica, 86, 1589–1619.
- Mogstad, M., A. Torgovitsky, and C. R. Walters (2021): “The causal interpretation of two-stage least squares with multiple instrumental variables,” American Economic Review, 111, 3663–98.
- Pomatto, L., P. Strack, and O. Tamuz (2020): “Stochastic dominance under independent noise,” Journal of Political Economy, 128, 1877–1900.
- Rubin, D. B. (1974): “Estimating causal effects of treatments in randomized and nonrandomized studies.” Journal of Educational Psychology, 66, 688.
- Shaikh, A. M. and E. J. Vytlacil (2011): “Partial identification in triangular systems of equations with binary dependent variables,” Econometrica, 79, 949–955.
- Torgovitsky, A. (2015): “Identification of nonseparable models using instruments with small support,” Econometrica, 83, 1185–1197.
- Vuong, Q. and H. Xu (2017): “Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity,” Quantitative Economics, 8, 589–610.
- Vytlacil, E. (2002): “Independence, monotonicity, and latent index models: An equivalence result,” Econometrica, 70, 331–341.