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Repeated information of benefits reduce COVID-19 vaccination hesitancy: Experimental evidence from Germany

Authors: Burger, Max; Mayer, Matthias; Steimanis, Ivo;

Repeated information of benefits reduce COVID-19 vaccination hesitancy: Experimental evidence from Germany

Abstract

This replication package contains the raw data and code to replicate the findings reported in the paper. The data are licensed under a Creative Commons Attribution 4.0 International Public License. The code is licensed under a Modified BSD License. See LICENSE.txt for details. Software requirements All analysis were done in Stata version 16: Add-on packages are included in scripts/libraries/stata and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in scripts/libraries/stata/stata.trk. Instructions Save the folder ‘replication_PLOS’ to your local drive. Open the master script ‘run.do’ and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive Run the master script ‘run.do’ to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials Datasets Wave 1 – Survey experiment: ‘wave1_survey_experiment_raw.dta’ Wave 2 – Follow-up Survey: ‘wave2_follow_up_raw.dta' Map: shape-files ‘plz2stellig.shp’ ‘OSM_PLZ.shp’, area codes ‘Postleitzahlengebiete-_OSM.csv’_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’) Pretest: ‘pre-test_corona_raw.dta’ For Appendix S7: ‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’ (all links to the sources can be found in the script ‘06_AppendixS7.do’) For Appendix S10: ‘vaccination_landkreis_raw.dta’ (all links to the sources can be found in the script ‘07_AppendixS10.do’) Descriptions of scripts 1_1_clean_wave1.do This script processes the raw data from wave 1, the survey experiment 1_2_clean_wave2.do This script processes the raw data from wave 2, the follow-up survey 1_3_merge_generate.do This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables 02_analysis.do This script estimates regression models in Stata, creates figures and tables, saving them to results/figures and results/tables 03_robustness_checks_no_exclusion.do This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in results/tables 04_figure2_germany_map.do This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany. 05_figureS1_dogmatism_scale.do This script creates Figure S1 using data from a pretest to adjust the dogmatism scale. 06_AppendixS7.do This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany. 07_AppendixS10.do This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.

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Keywords

vaccination intentions, repeated information, vaccination hesitancy, vaccination action, survey experiment

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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