Tutorial for ‘LaLonde (1986) after Nearly Four Decades: Lessons Learned’

Welcome!

This is a Quarto Book (Rbookdown previously) tutorial condensing the analysis from Imbens & Xu (2024).

Note

Replication: For an exact replication, please refer to the source GitHub repository, which contains the data and code necessary to replicate Imbens & Xu (2024): “LaLonde (1986) After Nearly Four Decades: Lessons Learned.” [arXiv].

We reflect on five key lessons from the methodological literature following LaLonde’s 1986 study:

• The central role of unconfoundedness
• The importance of overlap in covariate distributions
• The focus of propensity scores in constructing modern estimators
• Credibility through validation exercises
• Investigation of alternative estimands

Revisiting LaLonde’s analysis, we present how modern methods could enhance causal inference using data compiled by Rajeev Dehejia and Sadek Wahba (Dehejia and Wahba 1999, 2002) and survey data of lottery players in Imbens, Rubin, and Sacerdote (2001). Hereafter, we denote the former dataset as the LaLonde-Dehejia-Wahba (LDW) data, and the latter as IRS data. Our findings highlight that causal inference with observational data demands rigorous ( i ) examination of the assignment process, ( ii ) assessment of overlap, and ( iii ) execution of validation checks. This tutorial streamlines the methodology used in Imbens & Xu (2024), making it easier for readers to replicate the study and apply its techniques in their own research. In addition, we supplement a complementary application of our analytics with the original LaLonde (1986) data after the LDW analysis to further demonstrate and validate our methodologies.

Reference: Imbens, Guido and Yiqing Xu (2024). “LaLonde (1986) after Nearly Four Decades: Lessons Learned.” arXiv:2406.00827.

Acknowledgement: We thank Zihan Xie and Jinwen Wu for their excellent research assistance, which makes this tutorial possible.

Data Files

Imbens and Xu (2024) uses the following datasets, which are based on LaLonde (1986), Dehejia and Wahba (1999), Calónico and Smith (2017), and Imbens, Rubin, and Sacerdote (2001).

Data.files	Details	File_Type	Experimental
nsw.dta	NSW experimental data, used in LaLonde (1986)	Stata	Yes
nsw_dw.dta	Subset of NSW experimental data, used in Dehejia & Wahba (1999)	Stata	Yes
cps_controls.dta	CPS-SSA-1 controls, used in both papers	Stata	No
psid_controls.dta	PSID-1 controls, used in both papers	Stata	No
lottery.RData	Data of lottery winners, used in Imbens, Rubin & Sacerdote (201)	R	No
NSW_AFDC_CS.dta	Reconstructed NSW AFDC female samples	Stata	Both

Calónico, Sebastian, and Jeffrey Smith. 2017. “The Women of the National Supported Work Demonstration.” Journal of Labor Economics 35 (S1): S65–97.

Dehejia, Rajeev H, and Sadek Wahba. 1999. “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association 94 (448): 1053–62.

———. 2002. “Propensity Score-Matching Methods for Nonexperimental Causal Studies.” Review of Economics and Statistics 84 (1): 151–61.

Firpo, Sergio. 2007. “Efficient Semiparametric Estimation of Quantile Treatment Effects.” Econometrica 75 (1): 259–76.

Imbens, Guido W, Donald B Rubin, and Bruce I Sacerdote. 2001. “Estimating the Effect of Unearned Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players.” American Economic Review, 778–94.

LaLonde, Robert J. 1986. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” The American Economic Review, 604–20.