Flexpaneldid: A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration

Abstract

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Promise for Whom? “Free-College” Programs and Enrollments by Race and Gender Classifications at Public, 2-Year Colleges

“…As a final robustness check, we implemented DiD using the flexible conditional DiD approach (Dettmann et al, 2020). The results from that analysis appear in the online Appendix (A5).…”

“…As a final robustness check, we estimated the average treatment effect on the treated (ATT) using an alternative DiD estimation approach, the flexible conditional DiD (Dettmann et al, 2020). This method builds on recent approaches to reduce bias in DiD, particularly when dealing with heterogeneous treatment effects (e.g., Callaway & Sant’Anna, 2019; de Chaisemartin & d’Haultfoeuille, 2019; Goodman-Bacon, 2018).…”

“…This method builds on recent approaches to reduce bias in DiD, particularly when dealing with heterogeneous treatment effects (e.g., Callaway & Sant’Anna, 2019; de Chaisemartin & d’Haultfoeuille, 2019; Goodman-Bacon, 2018). The flexible conditional DiD is appropriate when there are differential treatment times (e.g., Promise programs implemented in different years), as well as potential dynamic treatment effects (effects varying over time), which could bias estimates in a two-way fixed-effects approach (Dettmann et al, 2020).…”

“…The flexible conditional DiD method involves two stages: a preprocessing phase using a matching technique followed by the DiD estimation (Dettmann et al, 2020). This approach differs from the traditional DiD approach in that each treated unit is assigned individual controls.…”

“…In this approach, treated units can only be matched to controls observed at the same time. While this reduces bias, it can also reduce the sample size considerably (Dettmann et al, 2020). We implemented this approach to estimate average treatment effects in the 5 years following Promise program implementation (adding years limits the sample further in the data preprocessing phase).…”