Political trust plays a critical role in understanding key political questions, including regime support, democratic legitimacy, policy preferences, and political behavior. However, the lack of comparable, cross-national data has limited scholars’ ability to analyze the relationship of political trust with quantities of interest and to generalize findings across different countries and time periods. To address this gap, this paper introduces the Trust in Government (TGOV) Dataset, a time-series cross-sectional resource covering 116 countries from 1973 to 2020.
This article focuses on a preliminary step in any ex-post data harmonization project—wrangling the pre-harmonized data—and suggests a practical routine for helping researchers reduce human errors in this often-tedious work. The routine includes three steps: (1) Team-based concept construct and data selection; (2) Data entry automation; and (3) “Second-order” opening—a “Tao” of data wrangling. We illustrate the routine with the examples of pre-harmonizing procedures used to produce the Standardized World Income Inequality Database (SWIID), a widely used database that uses Gini indices from multiple sources to create comparable estimates, and the Dynamic Comparative Public Opinion (DCPO) project, which creates a workflow for harmonizing aggregate public opinion data.