The Quality of Government: EU Regional Dataset, Version Nov20

Abstract

The QoG EU Regional dataset is a dataset consisting of more than 300 variables covering three levels of European regions - Nomenclature of Territorial Units for Statistics (NUTS): NUTS0 (country), NUTS1(major socio-economic regions) and NUTS2 (basic regions for the application of regional policies).

The unit of analysis in the QoG EU Regional Data is regions of Europe. The NUTS is used as a geocode standard for referencing the subdivisions of European countries. A hierarchy of three sub-national NUTS levels is established by Eurostat for European countries. The subdivisions in some levels do not necessarily correspond to administrative divisions within the country.

The QoG Regional Data is presented in three different forms available in separate datasets. All datasets are available in time-series format. First one (The QoG Regional Data - Long Form) is a dataset where data is presented in the long form. The list of units of analysis contains regions of all NUTS levels.

Two other datasets are presented in the wide form. In the second dataset (The QoG Regional Data - Wide Form NUTS1) includes NUTS1 level as the unit of analysis and variables represent the values for this level and corresponding lower level – NUTS0. As an example, in this dataset the data is presented only for East Sweden (Östra Sverige SE1), as a unit of analysis and have values for lower level of this region - Sweden (SE).

The third dataset (The QoG Regional Data - Wide Form NUTS2) the unit of analysis is NUTS2 level regions and variables provide values as for every unit of analysis, as well as for corresponding lower NUTS levels: NUTS1 and NUTS0. One example of unit of analysis in this dataset is Stockholm (SE11) and data for every variable will be for Stockholm, as well as for lower levels region – East Sweden (Östra Sverige SE1) and Sweden (SE).

In the Codebook, you can find a description of all data sources and variables. Detailed descriptions of all variables are sorted by original data sources. We hope that this will facilitate your search for variables.