scholarly journals Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations

2012 ◽  
Author(s):  
Margherita Fort
Keyword(s):  
Treatment Effect ◽  
Conditional Quantile ◽  
Quantile Treatment Effect

scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

scholarly journals Quantile treatment effects in difference in differences models with panel data

10.3982/qe935 ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1579-1618 ◽  
Author(s):  
Brantly Callaway ◽  
Tong Li
Keyword(s):  
Panel Data ◽  
Treatment Effect ◽  
Initial Level ◽  
Treated Group ◽  
Average Treatment Effect ◽  
Potential Outcomes ◽  
Significant Heterogeneity ◽  
Local Labor Markets ◽  
Difference In Differences ◽  
Quantile Treatment Effect

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences Assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTT is more complicated than the ATT though because it depends on the unknown dependence (or copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTT is identified. We use our method to estimate the effect of increasing the minimum wage on quantiles of local labor markets' unemployment rates and find significant heterogeneity.

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