National Culture and the Demand for Physical Money During the First Year of the COVID-19 Pandemic
Description
This dataset contains data used in the paper titled "National Culture and the Demand for Physical Money During the First Year of the COVID-19 Pandemic." In it I searched if "national culture" variables had significant impacts on the money demand during the first year of the COVID-19 pandemic. The main file of this dataset is the "20220923_final_dataset.xlsx" spreadsheet. Sheet "trimed_dataset_final" contains all the data used in the final regressions presented in the paper. In sheet "full_dataset_active", partial data on countries that did not make it into final analyses due to data gaps is also presented. Sheets "CiC", "GDP", "CPI'", "IR", "SI," and "NC" hold all the data on dependent and independent variables I have managed to collect. The origin of each data point is presented in the "data_sources_table." It should be noted that all data in this spreadsheet underwent transformation as described in the paper. However, all raw data is also included in this dataset (folder 'raw_data'). Data on CiC were primarily gathered from the IMF International Financial Statistics database (henceforth: IMF IFS; database: FASMBC_XDC), but for some countries the data were supplemented with the data from National Central Banks’. Data on NC was taken from the Hofstede Insights consultancy (https://www.hofstede-insights.com/). Data on GDP was primarily obtained from the IMF IFS (database: NGDP_XDC). Data gaps in this area were filled, if possible, directly from the National Statistical Offices’ (NSOs’) datasets. Because the study is based on monthly data, all quarterly data obtained was temporally disaggregated using the Denton-Cholette method (without an indicator series). Data on CPI was obtained primarily from Bank for International Settlements (BIS) datasets (https://www.bis.org/statistics/cp.htm) and IMF IFS databases (database: PCPI_IX) [BIS data had precedence]. Data gaps in this area were filled, if possible, directly from the National Statistical Offices’ datasets. Data on IR was obtained primarily from Bank for International Settlements (BIS) datasets (https://www.bis.org/statistics/cbpol.htm) and IMF IFS databases (databases: FPOLM_PA, FIMM_PA, and FIDR_PA) [BIS data had precedence]. Data gaps in this area were filled, if possible, directly from the National Central Banks’ datasets. Furthermore, data on IR were compiled using hierarchy suggested by the monetary transmission mechanism's: 'central bank policy rates' were given precedence; if those were unavailable,'money market rates' were used; and if those were also unavailable, data on 'deposit rates' were collected. Data on SI was taken directly from the COVID-19 Government Response Tracker (https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker or https://github.com/OxCGRT/covid-policy-tracker/tree/master/data).
Files
Steps to reproduce
Calculations were performed in R. The script used for the calculation of OLS regressions (as well as Table 3 and Figure 1) is called "20220923_R_script_OLS.R"). Data should be replicable in other software by using data collected in sheet "trimed_dataset_final" of the "20220923_final_dataset" spreadsheet.