QoG Expert Survey
The Quality of Government Expert Survey (QoG Expert Survey) is a research project aimed at documenting the organizational design of public bureaucracies and bureaucratic behavior in countries around the world. The third wave of the QoG Expert Survey covers 117 countries and is based on a web survey of 996 experts.
Citation
When using QoG Expert Survey Dataset, make sure to cite:
Nistotskaya, Marina, Stefan Dahlberg, Carl Dahlström, Aksel Sundström, Sofia Axelsson, Cem Mert Dalli & Natalia Alvarado Pachon. 2021. The Quality of Government Expert Survey 2020 Dataset: Wave III. University of Gothenburg: The Quality of Government Institute, http://www.qog.pol.gu.se DOI: 10.18157/qoges2020
When using QoG Expert Survey Report, make sure to cite:
Nistotskaya, Marina, Stefan Dahlberg, Carl Dahlström, Aksel Sundström, Sofia Axelsson, Cem Mert Dalli & Natalia Alvarado Pachon. 2021. The Quality of Government Expert Survey 2020 (Wave III): Report. University of Gothenburg: The QoG Working Paper Series 2021:2.
The general purpose of the QoG Expert Survey is to measure the structure and behaviour of public administration across countries. The survey covers a variety of topics which are seen as relevant to the structure and functioning of the public administration according to the literature, but on which we lack quantitative indicators for a large number of countries. The QoG Expert Survey 2020 is the third wave of the QoG Expert Survey, following the first wave in 2008-2012 and the second wave in 2014.
The QoG Expert Survey 2020 produced ten country-level indicators, pertaining to bureaucratic structure (meritocratic recruitment, security of tenure, closedness) and bureaucratic behavior (political interference into day-to-day bureaucratic decision-making and impartiality). The data is based on the assessments of experts from 117 countries, carefully selected for their contextual subject-matter knowledge. The experts took part in the research pro bono. The main innovation of the third wave is the use of anchoring vignettes and Item-Response Theory (IRT)-based aggregation techniques to produce point estimates that account and adjust for systematic differences in expert subjective assessments and variation in expert reliability. The resulting indicators are internally coherent and also correlate well with other well-established measures for the same concepts. The strength of the association between the data from 2020 and the two previous waves of the survey suggests that the data is likely to measure the same underlying phenomena, while offering enough variability over time to be used in time-series analysis.
For a detailed description of the study and its methodology, please see the survey report.
