Replication Data for: Dynamic Synthetic Controls: Accounting for Varying Speeds in Comparative Case Studies (doi:10.7910/DVN/DIUPUA)

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Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
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Replication Data for: Dynamic Synthetic Controls: Accounting for Varying Speeds in Comparative Case Studies

doi:10.7910/DVN/DIUPUA

Harvard Dataverse

2024-09-19

1

Cao, Jian; Chadefaux, Thomas, 2024, "Replication Data for: Dynamic Synthetic Controls: Accounting for Varying Speeds in Comparative Case Studies", https://doi.org/10.7910/DVN/DIUPUA, Harvard Dataverse, V1, UNF:6:bRGPrNqOQXtHwotEDa55dw== [fileUNF]

Replication Data for: Dynamic Synthetic Controls: Accounting for Varying Speeds in Comparative Case Studies

doi:10.7910/DVN/DIUPUA

Cao, Jian (Trinity College Dublin, The University of Dublin)

Chadefaux, Thomas (Trinity College Dublin, The University of Dublin)

Harvard Dataverse

Cao, Jian

Code Ocean

https://doi.org/10.7910/DVN/DIUPUA

Social Sciences

Synthetic controls are widely used to estimate the causal effect of a treatment. However, they do not account for the different speeds at which units respond to changes. Reactions may be inelastic or “sticky” and thus slower due to varying regulatory, institutional, or political environments. We show that these different reaction speeds can lead to biased estimates of causal effects. We therefore introduce a dynamic synthetic control approach that accommodates varying speeds in time series, resulting in improved synthetic control estimates. We apply our method to re-estimate the effects of terrorism on income (Abadie and Gardeazabal 2003), tobacco laws on consumption (Abadie, Diamond, and Hainmueller 2010), and German reunification on GDP (Abadie, Diamond, and Hainmueller 2015). We also assess the method’s performance using Monte-Carlo simulations. We find that it reduces errors in the estimates of true treatment effects by up to 70% compared to traditional synthetic controls, improving our ability to make robust inferences. An open-source R package, dsc, is made available for easy implementation.

• Number of cases: 13770

• No. of variables per record: 17

• Type of File: text/tab-separated-values

UNF:6:bRGPrNqOQXtHwotEDa55dw==

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• Year - Year
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• MeasureDesc - MeasureDesc
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• Data_Value_Unit - Data_Value_Unit
• Data_Value_Type - Data_Value_Type
• GeoLocation - GeoLocation
• Source - Source
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Summary Statistics: Valid 13770.0; Min. 1970.0; Max. 2014.0; Mean 1992.0; StDev 12.987644759786905

Variable Format: numeric

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Summary Statistics: Mean 2.5339751680878535E7; Valid 13770.0; StDev 1.0679841311267953E8; Min. 0.02; Max. 1.543947044E9

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Summary Statistics: Mean 3.5; Min. 1.0; Max. 6.0; StDev 1.707887143569595; Valid 13770.0

Variable Format: numeric

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