Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality

Carneiro, Pedro; Lee, Sokbae

doi:10.1016/j.jeconom.2009.01.011

Cited by 105 publications

(126 citation statements)

References 45 publications

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“…In particular, here the two education groups are college and non-college, the specification is pooled across genders, and controls are interacted with gender. 16 We present the results by gender.…”

Section: Counterfactuals In the Presence Of Equilibrium Effectsmentioning

confidence: 99%

“…This is due to the fact that in the counterfactual scenario out-of-work income is lower, thus increasing employment rates, and as a result 16 The fit of the model used in this subsection is shown in Figures 12 and 13.…”

Section: Counterfactuals In the Presence Of Equilibrium Effectsmentioning

confidence: 99%

“…Imbens and Rubin (1997) study identification and estimation of unconditional distributions of potential outcomes in a treatment effects model with a binary instrument, and achieve identification for compliers (as in Abadie, 2003, and in Abadie, Angrist and Imbens, 2002). Carneiro and Lee (2009) uses the framework of Heckman and Vytlacil (2005) to identify and estimate distributions of potential outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Quantile selection models: with an application to understanding changes in wage inequality

Arellano

Bonhomme

2015

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We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated "check" function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.JEL code: C13, J31.

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Section: Counterfactuals In the Presence Of Equilibrium Effectsmentioning

confidence: 99%

Section: Counterfactuals In the Presence Of Equilibrium Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantile selection models: with an application to understanding changes in wage inequality

Arellano

Bonhomme

2015

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“…We explain how each command is used, what each command does, the available command options, and saved results. In Section 9 we illustrate the use of all four of these commands using data from the National Longitudinal Survey of Youth of 1979 (NLSY79), as in Carneiro and Lee (2009). Specifically, we use these commands to estimate and perform inference on returns to education using monotone treatment response and monotone instrumental variable bounds developed by Manski and Pepper (2000).…”

Section: Command Description Clrtestmentioning

confidence: 99%

“…We use the same data extract as Carneiro and Lee (2009) from the National Longitudinal Survey of Youth of 1979 (NLSY79).…”

Section: Examplesmentioning

confidence: 99%

Implementing intersection bounds in Stata

Chernozhukov¹,

Lee²,

Rosen³

et al. 2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract. We present the clrbound, clr2bound, clr3bound, and clrtest commands for estimation and inference on intersection bounds as developed by Chernozhukov et al. (2013). The intersection bounds framework encompasses situations where a population parameter of interest is partially identified by a collection of consistently estimable upper and lower bounds. The identified set for the parameter is the intersection of regions defined by this collection of bounds. More generally, the methodology can be applied to settings where an estimable function of a vector-valued parameter is bounded from above and below, as is the case when the identified set is characterized by conditional moment inequalities. Terms of use: Documents inThe commands clrbound, clr2bound, and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid confidence sets. clrtest performs an intersection bound test of the hypothesis that a collection of lower intersection bounds is no greater than zero. The command clrbound provides bound estimates for one-sided lower or upper intersection bounds on a parameter, while clr2bound and clr3bound provide two-sided bound estimates based on both lower and upper intersection bounds. clr2bound uses Bonferroni's inequality to construct two-sided bounds that can be used to perform asymptotically valid inference on the identified set or the parameter of interest, whereas clr3bound provides a generally tighter confidence interval for the parameter by inverting the hypothesis test performed by clrtest. More broadly, inversion of this test can also be used to construct confidence sets based on conditional moment inequalities as described in Chernozhukov et al. (2013). The commands include parametric, series, and local linear estimation procedures, and can be installed from within STATA by typing "ssc install clrbound".

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HETEROGENEITY IN ACTION: THE ROLE OF PASSIVE PERSONALIZATION IN COMPARATIVE EFFECTIVENESS RESEARCH

et al. 2013

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Despite the goal of comparative effectiveness research (CER) to inform patient-centered care, most studies fail to account for the patient-centeredness of care that already exist in practice, which we denote as passive personalization (PP). Because CER studies describe the average effectiveness of treatments rather than heterogeneity in how individual patients respond to therapies, clinical or coverage policies that respond to CER results may undermine PP in clinical practice and generate worse outcomes. We study this phenomenon empirically in the context of use of antipsychotic drugs in Medicaid patients with schizophrenia using novel instrumental variable methods. We find strong support for PP in clinical practice and demonstrate that the average effects from a CER study cannot be replicated in practice because of the presence of PP. In contrast, providing physicians with evidence to further personalize treatment can produce significant benefits.

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Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality

Cited by 105 publications

References 45 publications

Quantile selection models: with an application to understanding changes in wage inequality

Quantile selection models: with an application to understanding changes in wage inequality

Implementing intersection bounds in Stata

HETEROGENEITY IN ACTION: THE ROLE OF PASSIVE PERSONALIZATION IN COMPARATIVE EFFECTIVENESS RESEARCH

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