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Replication Data for: Using Multiple Imputation and Matching to Improve Network Effects on Corporate Tax Policy Interdependence

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Using Multiple Imputation and Matching to Improve Network Effects on Corporate Tax Policy Interdependence
 
Identifier https://doi.org/10.7910/DVN/71PPUF
 
Creator Perez, Zach
Park, In Young
Uji, Azusa
 
Publisher Harvard Dataverse
 
Description By using a spatial lag model, Cao (2010) tests the effects of economic and cultural similarities among countries on international diffusion of corporate tax policy and by extension. Our analysis attempts to improve upon the methods set forth by Cao, and further his investigation of cross-national policy interdependence. First, using recently developed statistical methods, we impute missing data to present a more thorough regression analysis. Second, we attempt to estimate the causal effects of structural equivalence with matching methods, and we show that strong covariate correlations confound matching techniques.
 
Subject Social Sciences
 
Contributor Perez, Zach