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.
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.
Last updated by source: 2021-03-15
| Dataset type: | Cross-section |
| Dataset level: | Country |
(Nistotskaya et al.,
2021)
Country-level estimate for Entry at the lowest level only, scaled between 0 and 1. Highest score refers to cases where entry to bureaucratic positions is possible at the lowest level of hierarchy only, and positions at middle and higher levels of hierarchy are filled by individuals from within the bureaucracy.
More about this variableCountry-level estimate for Entry via examination, scaled between 0 and 1. Countries in which formal examination is usually part of the hiring process have higher scores.
More about this variableCountry-level estimate for Special Laws, scaled between 0 and 1. Higher scores mean that human resource management in public administration is regulated by a set of laws and regulations applicable only to the public sector (including government), which is different from the country’s labor code.
More about this variableClosedness Index is constructed from Entry at the lowest level only, Entry via examination and Special Laws with the help of Principal Component Analysis (PCA). Entry at the lowest level only, Entry via examination and Special Laws variables are load on the same dimension, which predicted scores are used as Closedness Index.
More about this variableCountry-level estimate for Political Interference, constructed with an IRT model that accounts for DIF and variation in expert reliability. Higher values stand for more political interference.
More about this variableLower boundary of 95% credible interval for Political Interference.
More about this variableUpper boundary of 95% credible interval for Political Interference.
More about this variableCountry-level estimate for Impartiality, constructed with an IRT model that accounts for DIF and variation in expert reliability. Higher values stand for more impartiality.
More about this variableLower boundary of 95% credible interval for Impartiality.
More about this variableUpper boundary of 95% credible interval for Impartiality.
More about this variableCountry-level estimate for Patronage, constructed with an IRT model that accounts for differential item functioning (DIF) and variation in expert reliability. Higher values stand for more patronage in recruitment.
More about this variableLower boundary of 95% credible interval for Patronage.
More about this variableUpper boundary of 95% credible interval for Patronage.
More about this variableCountry-level estimate for Merit, constructed with an IRT model that accounts for DIF and variation in expert reliability. Higher values stand for more merit-based appointment.
More about this variableLower boundary of 95% credible interval for Merit.
More about this variableUpper boundary of 95% credible interval for Merit.
More about this variableCountry-level estimate for Tenure, constructed with an IRT model that accounts for DIF and variation in expert reliability. Higher values stand for stronger security of tenure.
More about this variableLower boundary of 95% credible interval for Tenure.
More about this variableUpper boundary of 95% credible interval for Tenure.
More about this variableProfessionalism Index is constructed from Patronage, Merit and Tenure with the help of Principal Component Analysis (PCA). Merit, Patronage and Tenure are load on the same dimension, which predicted scores are used as Professionalism Index.
More about this variable