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.

scholarly journals A Generalized Regression-adjustment Estimator for Average Treatment Effects from Panel Data

2016 ◽  
Vol 16 (4) ◽  
pp. 826-836 ◽  
Author(s):  
David M. Drukker
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Potential Outcomes ◽  
Treatment Assignment ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Regression Adjustment ◽  
Simple Regression

I illustrate that the simple regression-adjustment estimator is inconsistent for the average treatment effect when the random effects affecting treatment assignment are correlated with the random effects that affect the potential outcomes. I present a simple parametric estimator that is consistent in this case.

scholarly journals Treatment Effects in Strategic Management: With an Application to Choosing Early Stage Venture Capital

2021 ◽  
Author(s):  
Jorge Guzman
Keyword(s):  
Venture Capital ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Early Stage ◽  
Average Treatment Effect ◽  
Significant Heterogeneity ◽  
Operational Effectiveness ◽  
Resource Interactions ◽  
Determinant Function

This paper uses the Rubin Causal Model to formalize the treatment effects of a firm choice on its performance. Building from Porter, a firm choice can shape profitability through both strategy and operational effectiveness, but they are distinct in how they do so. The strategic treatment effect is the benefit that is predictable from a firm's characteristics (i.e., resources) and their joint configuration. The strategic determinant function is a mapping of resources to treatment effects, and the role of resource interactions in it determines the importance of coherence for a strategy. Under unconfoundedness, the strategic treatment effect, strategic determinant function, and coherence can be estimated in high-dimensional observational data using machine learning. I present an application estimating the gains from choosing venture capital as early stage financing versus other forms of capital. The results highlight the advantage of considering strategic benefits in this choice. For equity outcomes, there is no average treatment effect of early stage VC, but there is significant heterogeneity: some entrepreneurs can benefit substantially from raising early stage VC, while others be negatively affected. This heterogeneity is predictable from founder, industry and location characteristics. The estimated role of coherence in this choice is moderate. The formalizations in this paper also show that several additional assumptions are required when assessing strategic benefits compared to the usual causal inference. R code to replicate these functions will be included.

scholarly journals Evaluación de impacto académico y social en egresados universitarios

2019 ◽  
Vol 30 (3) ◽  
pp. 695-712
Author(s):  
Gabriel González ◽  
Luisa Díez-Echavarría ◽  
Elkin Zapa ◽  
Danilo Eusse
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Educación Superior ◽  
Educacion Superior ◽  
Average Treatment

Las instituciones de educación superior deben formar a sus estudiantes según requerimientos del contexto en que se desenvuelven, ya que, sobre la base de su desempeño, es donde se medirá si las políticas de desarrollo socioeconómico son efectivas. Para lograrlo, es necesario identificar el impacto de esa educación en sus egresados, y hacer los ajustes necesarios que generen mejora continua. El objetivo de este artículo es estimar el impacto académico y social de egresados del Instituto Tecnológico Metropolitano – Medellín, a través de un análisis multivariado y la estimación del modelo Average Treatment Effect (ATE). Se encontró que la educación ofrecida a esta población ha generado un impacto académico, asociado a los estudios de actualización, y dos impactos sociales, asociados a la situación laboral y al nivel de ingresos percibidos por los egresados. Se recomienda usar esta metodología en otras instituciones, ya que suele arrojar resultados más informativos y precisos que los estudios tradicionales de caracterización, y se puede medir el efecto de cualquier estrategia.

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