Although political trust is a long-standing interdisciplinary topic, the lack of comparable cross-national time-series data has limited scholars’ ability to analyze its determinants and consequences and to generalize findings across countries and over time. To address this gap, this paper introduces the Trust in National Government (TrustGov) Dataset—a cross-national time-series resource covering 115 countries and territories from 1973 to 2020, harmonizing 1,545 country-year observations from 189 national and cross-national surveys using a Bayesian latent variable model.
Trust in civil servants is essential for effective governance, enabling policy implementation, public service delivery, and compliance. However, the lack of comparable cross-national data on trust in bureaucracy has limited our ability to systematically examine these relationships. To address this gap, we develop the Trust in Civil Servants (TCS) dataset with an advanced latent-variable modeling technique, using 123 national and cross-national surveys from 98 countries and territories (1986–2022). Our measures reveal variations in trust both within and between countries and territories.
We introduce the Digitally Accountable Public Representation (DAPR) Database, an innovative archive that systematically tracks and analyzes the online communications of federal, state, and local elected officials in the U.S. Focusing on X/Twitter and Facebook, the current database includes 28,834 public officials, their demographic information, and 5,769,904 Tweets along with 450,972 Facebook posts, dating from January 2020 to December 2024. The database integrates three interconnected datasets: metadata on elected officials, weekly aggregated X data, and weekly aggregated Facebook data.