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.
Similar content being viewed by others
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).
References
Abramo, C. (2008). How much do perceptions of corruption really tell us? Economics: The Open-Access, Open-Assessment E-Journal, 2, 3.
Acemoglu, D., & Robinson, J. (2012). Why nations fail: The origins of power, prosperity, and poverty. New York: Crown Business.
Andersson, S., & Heywood, P. M. (2009). The politics of perception: Use and abuse of transparency international’s approach to measuring corruption. Political Studies, 57(4), 746–767.
Annoni, P., & Dijkstra, L. (2013). EU Regional Competitiveness Index. Luxembourg, Europe.
Charron, N. (2010) Assessing the quality of the quality of government data: A sensitivity test of the world bank government indicators, QoG Working Paper. (http://www.qog.pol.gu.se/digitalAssets/1357/1357980_paper-on-sensititivty-tests-of-world-bank-data.pdf).
Charron, N. (2013). European perceptions of quality of government: A survey of 24 countries. In A. Mungiu-Pippidi & B. Rothenstein (Eds.) Controlling corruption in Europe—The ANTICORP Report no 1. Verlag Barbara Budrich publishers.
Charron, N., Dijkstra, L., & Lapuente, V. (2014). Regional governance matters: Quality of government within European Union Member States. Regional Studies, 48(1), 68–90.
Charron, N., & Lapuente, V. (2013). Why do some regions in Europe have higher quality of government? Journal of Politics, 75(3), 567–582.
Charron, N., Lapuente, V., & Rothstein, B. (2013). Quality of government and corruption form a European perspective: A comparative study of good government in EU regions. Cheltenham, UK: Edward Elgar Publishing.
Goel, R. K., & Nelson, M. A. (1998). Corruption and government size: A disaggregated analysis. Public Choice, 97(1–2), 107–120.
Golden, M. A., & Picci, L. (2005). Proposal for a new measure of corruption, illustrated with Italian data. Economics and Politics, 17(1), 37–75.
Gupta, S., Davoodi, H., & Alonso-Terme, R. (2002). Does corruption affect income inequality and poverty? Economics of Governance, 3(1), 23–45.
Helliwell, J. F., & Huang, H. (2008). How’s your government? International evidence linking good government and well-being. British Journal of Political Science, 38, 595–619.
Holmberg, S., & Rothstein, B. (Eds.). (2012). Good government: The relevance of political science. Cheltenham, UK: Edward Elgar Publishing.
Holmberg, S., Rothstein, B., & Nasiritousi, N. (2009). Quality of government: What you get. Annual Review of Political Science, 12, 135–161.
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2009). The worldwide governance indicators: Methodology and analytical issues. World Bank Policy Research Working Paper, No. 5430.
Kurtz, M. J., & Schrank, A. (2007). Growth and governance: Models, measures, and mechanisms. Journal of Politics, 69(2), 538–554.
León, C.J., Araña, J.E., & de León, J. (2013). Correcting for scale perception bias in measuring corruption: an application to Chile and Spain. Social Indicators Research, 1–19.
Mauro, P. (2004). The persistence of corruption and slow economic growth. IMF staff papers, 1–18.
Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2008). Handbook on constructing composite indicators: Methodology and user guide OECD, European Commission, Joint Research Centre. OECD Publishing.
Norris, P. (2012). Making democratic governance work: How regimes shape prosperity, welfare, and peace. Cambridge, UK: Cambridge University Press.
North, D. (1990). Institutions, institutional change and economic performance. Cambridge, MA: Cambridge University Press.
Rodriguez-Pose, A., & Garcilazo, E. (2013), Quality of government and the returns of investment: Examining the impact of cohesion expenditure in european regions, OECD Regional Development Working Papers, No. 2013/12, OECD Publishing.
Rothstein, B. (2003). Social capital, economic growth and quality of government: The causal mechanism. New Political Economy, 8(1), 49–71.
Rothstein, B., & Teorell, J. (2008). What is quality of government? A theory of impartial government institutions. Governance, 21(2), 165–190.
Rothstein, B., & Uslaner, E. M. (2005). All for all: Equality, corruption, and social trust. World Politics, 58(01), 41–72.
Samanni, M., & Holmberg, S. (2010). Quality of government makes people happy. QoG Working Paper Series 2010:1, The Quality of Government Institute, University of Gothenburg, Sweden.
Sapienza, P., Zingales, L., & Guiso, L. 2006. Does culture affect economic outcomes? (No. w11999). National Bureau of Economic Research.
Shankar, R., & Shah, A. (2003). Bridging the economic divide within countries: A scorecard on the performance of regional policies in reducing regional income disparities. World Development, 31(8), 1421–1441.
Snyder, R. (2001). Scaling down: The subnational comparative method. Studies in Comparative International Development, 36(1), 93–110.
Sundström, A., & Wängnerud, L. (2013). Women’s local political representation within 30 European countries: A comparative dataset on regional figures. QoG Institute Working Paper Series 2013: 18. The Quality of Government Institute, University of Gothenburg, Sweden.
Tabellini, G. (2008). Presidential address institutions and culture. Journal of the European Economic Association, 6(2–3), 255–294.
Tabellini, G. (2010). Culture and institutions: Economic development in the regions of Europe. Journal of the European Economic Association, 8(4), 677–716.
Veenhoven, R. (2002). Why social policy needs subjective indicators. Social Indicators Research, 58(1–3), 33–46.
Veenhoven, R. (2010). Greater happiness for a greater number. Journal of Happiness Studies, 11(5), 605–629.
Welsch, H. (2004). Corruption, growth, and the environment: A cross-country analysis. Environment and Development Economics, 9(5), 663–693.
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/).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-014-0702-y