Abstract
Do aspects of quality of government, broadly defined, such as corruption, impartiality, and quality of public services, vary below the country level? The concept of quality of government (QoG) and various measures to assess it have become more ubiquitous in several social science disciplines. QoG is related with economic and social development, better environmental conditions, and better quality of life. Yet while governance indicators have proliferated in recent years, their focus remains almost universally on analysis at the country level. Moreover, the majority of indices rely on expert assessments, as opposed to the assessments of citizens, who are the on-the-ground consumers of public services. Building on a preliminary round of data collected in 2010, this study, for which data were collected in 2013, presents a novel and comprehensive index that captures the quality of governance for 206 regions in 24 European countries. The ‘European Quality of Government Index’, which will be published free for scholarly use, is built on the largest survey to date focusing on governance at the regional level; over 85,000 citizens were surveyed. The instrument proposed here builds on both perceptions and experiences of citizens in public service areas such as health care, education, and law enforcement. The paper presents final results of the survey, as well as a sensitivity analysis and checks for external and internal validity.
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Notes
Statistics for all NUTS (nomenclature of territorial units for statistics) levels can be found at Eurostat’s data portal (http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/regional_statistics/data/database).
To enable wider comparisons, we include smaller EU28 countries for which there are no NUTS 2 regions in the total EQI data in a distinct methodology explained in a subsequent section.
A full list of each country’s NUTS 1, 2 and 3 levels can be found at http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/correspondence_tables/national_structures_eu.
Changes over time by country can be found at http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/history_nuts.
In addition, we performed extensive sensitivity testing of each of these four pillars of QoG and found the data to be highly robust. For a closer look at the sensitivity tests and results for the EU sample, Charron 010 see Charron (2010).
A full list of scores by region and country (along with confidence intervals) is found in “Appendix”.
There are of course several more-advanced techniques for showing within-unit variation, as discussed by Shankar and Shah (2003), such as Gini or Theil indices. For the sake of simplicity, a simple distribution and min–max differences areused here, as those measures correlate strongly with the min–max for this sample.
Hierarchical clustering with squared Euclidean distancing (Wards linkage) was employed.
We assume a normal distribution of the sample so that we may use the Central Limit Theorem. Basic statistical probability shows that in a sample x, 95 percent of the area of a basic normal bell curve is between our estimates, (µ) 1.96± the standard error around µ. We calculate the margins of error as \( s.e. = {\raise0.7ex\hbox{$\sigma $} \!\mathord{\left/ {\vphantom {\sigma {\sqrt n }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sqrt n }$}} \). The margin of error for each individual region is based around its QoG estimate, \( 1.96 \pm \left( {{\raise0.7ex\hbox{$\sigma $} \!\mathord{\left/ {\vphantom {\sigma {\sqrt n }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sqrt n }$}}} \right) \), with N = 16, because of the 16 indicators in the regional index.
To be precise, there are two ways to go about calculating the margin of error for survey data—using an exact confidence interval or an approximate confidence interval. The former takes into account both sampling and non-sampling errors, while the latter only accounts for random sampling errors. While the exact interval may be more precise, we find the advantages of the approximate confidence interval to far outweigh the drawbacks, in particular with respect to the efficiency of the calculation. Moreover, we have no reason to suspect that there is any bias created by certain groups being excluded or not being forthright in their responses, so compensating for such error is simply beyond our reach. Thus we report an approximate confidence interval for each region’s QoG estimate.
A table showing the full range of EQI estimates in rank order with margins of error by region is included as “Appendix”.
More details about the results of the EQI by question can be found Charron (2013).
Measures recommended by Nardo et al. (2008) in the JRC-OECD handbook on composite indicators. We would like to thank Michaela Saisana for her help in this process.
The pairwise correlation for the pillars quality and impartiality is 0.73, quality and corruption is 0.59, impartiality and corruption, 0.76.
The 2010 data consisted of 34,000 respondents (200 per region) for 172 regions in 18 countries.
The risk of poverty “is defined as having equivalised disposable income (i. e. adjusted for household size and composition) of less than 60 % of national median” (EU Commission Cohesion Report 2010).
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Acknowledgments
Funding for this project comes from the Seventh Framework Programme for Research and Development of the European Union. This research project is part of ANTICORP (http://anticorrp.eu/).
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Charron, N., Dijkstra, L. & Lapuente, V. Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions. Soc Indic Res 122, 315–346 (2015). https://doi.org/10.1007/s11205-014-0702-y
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DOI: https://doi.org/10.1007/s11205-014-0702-y