이중차분법(DID), 통제집단합성법(SCM), 통제집단합성- 이중차분법(SDID)의 비교 연구: 새벽배송 진입효과 분석 = A Comparative Study of Difference-in-Differences (DID), Synthetic Control Method (SCM), Synthetic Differencein- Differences (SDID): Effects of Early-Morning Delivery

다국어 초록 (Multilingual Abstract)

Purpose: The purpose of this study is to compare and analyze the strengths and weaknesses of three widely usedquasi-experimental methods in causal inference research: Difference-in-Differences (DID), Synthetic ControlMethod (SCM), and Synthetic Difference-in-Differences (SDID). The study specifically examines the impact ofEarly-Morning delivery services on existing offline commercial districts as a case study to empirically evaluatethe accuracy and applicability of each method.
Research design, data, and methodology: To compare the three methodologies DID, SCM, and SDID this studyanalyzed the impact of Early-Morning delivery services on offline supermarket sales. The treatment groupconsisted of the Seo-gu and Yuseong-gu districts in Daejeon, while the control group included selected districts inGwangju and Busan. Using daily credit card panel data, the analysis covered sales changes from May 2, 2019,two years before the entry of dawn delivery services, to October 31, 2021, six months after their introduction.
The DID method compared post-treatment sales changes between the treatment and control groups. The SCMmethod created a synthetic control group by assigning weights to the control group to closely match the treatmentgroup during the pre-treatment period, then compared post-treatment sales changes between the treatment andsynthetic control groups. Similarly, the SDID method generated a synthetic control group with the weights ofSCM and the time weights to minimize differences between the pre- and post-treatment periods, and thencompared post-treatment sales changes between the treatment and synthetic control groups. This approachallowed for the analysis of treatment effects based on differences observed after the intervention among thetreatment, control, and synthetic control groups.
For analyses based on the number of control groups, selections were made in both similar and dissimilar orders by comparing the pre-treatment trends of Seo-gu and Yuseong-gu in Daejeon with those of potential controlgroups using Euclidean distance. The comparative analysis based on the pre-entry period fixed the post-treatmentperiod to 6 months after the entry of the dawn delivery service, while the pre-treatment period was divided into 2years, 1 year, 6 months, and 3 months before the entry. This setup allowed the study to examine cases where thepre-treatment period was longer than, equal to, or shorter than the post-treatment period.
Results: The analysis results indicated that the introduction of Early-Morning delivery services had a negativeimpact on offline supermarket sales across all three models: DID, SCM, and SDID. However, the SCM and SDIDmodels, by constructing synthetic control groups, maximized the similarity between the treatment and controlgroups, resulting in more reliable counterfactual estimates and providing more stable estimates than the DIDmodel. This is because DID may produce biased results if the pre-trends of the treatment and control groups arenot similar. Specifically, the SCM model delivered stable results even when the number of control groups wassmall and when the parallel trend assumption was not met. The SDID model produced more stable results as thepre-intervention period extended.
Conclusions: In conclusion, the DID model is appropriate when the parallel trend assumption is satisfied;however, when this assumption is not met, the SCM or SDID models are more suitable. SCM and SDID areparticularly effective in scenarios where the parallel trend is not satisfied or the number of control groups is small.
Additionally, the SDID model has proven to be useful for analyzing long-term data. The study highlights that theperformance of each method can vary depending on the situation, emphasizing the importance of selecting theappropriate methodology based on the specific circumstances of the analysis. Consequently, these modelscontribute to more accurate causal inference in policy evaluation and can aid in decisi...