The National Tax Journal encourages authors of empirical papers to provide data and analysis sufficient for replication. Upon acceptance, authors should post the following on NTJ’s Dataverse: - Code and programs that can be used for replication, - Dataset(s) used to run the final models, and - “Readme” files detailing how data and programs are combined to generate the final analysis. Such files should include a description of how intermediate data files were used to create the final dataset. Authors who choose to post replication files for NTJ accepted papers are strongly encouraged to do so prior to submitting final files to the journal office for publication so that the link to the data can be included in the published paper. In that case, the following line (with unique identifier information) should be included at the end of the paper’s acknowledgment note: Data are provided through Dataverse at https://doi.org/XX.XXXX/XXX/XXXXXX
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Python Source Code - 5.4 KB - MD5: 625d8969ec4f114087b187d3692e7c4f
reads data from PSZ and AS spreadsheets, calculates and prints Table 1
Jun 17, 2025
St. Clair, Travis; Chan, Sewin; Grace Brang, 2025, "Replication Data for Fiscal Stimulus or Debt Relief: The Effect of Federal Pandemic Aid on State and Local Pensions", https://doi.org/10.7910/DVN/QX8UA1, Harvard Dataverse, V1, UNF:6:8ZD3QS3i4hKaHVWezp3JCQ== [fileUNF]
Replication code and data for Fiscal Stimulus or Debt Relief: The Effect of Federal Pandemic Aid on State and Local Pensions
Tabular Data - 15.3 KB - 19 Variables, 51 Observations - UNF:6:d+q6a7fKF4Csf/ln1q2eKQ==
Tabular Data - 2.5 KB - 4 Variables, 110 Observations - UNF:6:9Y12CyGj1ETaCMmoFj9Wfw==
Tabular Data - 6.9 KB - 11 Variables, 63 Observations - UNF:6:NV8kuEOjhEQIvfa786DxWw==
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