2014
DOI: 10.1177/1536867x1401400411
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Lee (2009) Treatment-Effect Bounds for Nonrandom Sample Selection

Abstract: Nonrandom sample selection may render estimated treatment effects biased even if assignment of treatment is purely random. Lee (2009, Review of Economic Studies, 76: 1071-1102 proposes an estimator for treatment-effect bounds that limit the possible range of the treatment effect. In this approach, the lower and upper bound correspond to extreme assumptions about the missing information that are consistent with the observed data. In contrast to conventional parametric approaches to correcting for sample-selecti… Show more

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Cited by 37 publications
(23 citation statements)
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“…Unfortunately, though, a satisfactory exclusion restriction is not possible, so Heckman estimates would be unconvincing. Instead, I include calculations of Lee’s bounds, nonparametric limits of the treatment effect only on those who are not influenced into selection by treatment ( LEE, 2009; TAUCHMANN, 2013). There are no exclusion restriction requirements.…”
Section: Methodsmentioning
confidence: 99%
“…Unfortunately, though, a satisfactory exclusion restriction is not possible, so Heckman estimates would be unconvincing. Instead, I include calculations of Lee’s bounds, nonparametric limits of the treatment effect only on those who are not influenced into selection by treatment ( LEE, 2009; TAUCHMANN, 2013). There are no exclusion restriction requirements.…”
Section: Methodsmentioning
confidence: 99%
“…Specifically, we trimmed our treatment group observations by 11 or 12 percent at the state level (depending on the variable) and by 19 percent at the metropolitan level. We used the user‐created command “leebounds” in STATA to calculate the bounds (Tauchmann, ), consistent with recommendations by Lee (). Even these extreme assumptions about missing data yield consistent statistically significant positive estimates for the impact of pre‐K on math test scores and honors course taking, and statistically significant negative estimates for grade retention (see Table )…”
Section: Robustness Checksmentioning
confidence: 99%
“…The second set of columns of Table 3 present the difference in means, the standard errors and p-values, as well as confidence interval for the differences in means, for each game outcome when the treatment or control samples are trimmed to have equal representation. The method implemented is that of Lee (2009) using the Stata package leebounds (Tauchmann, 2013). The Lee bounds method has become a standard way of assessing the robustness of statistical findings to differential attrition.…”
Section: Impact Of Program On Reading Outcomesmentioning
confidence: 99%