WeSISpedia - User contributions [en]

Cultural Spheres Network

2026-08-02T20:55:20Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = No. of chared cultural characteristics
|techname = cult_spheres
|category= Communication
|label = Cultural Spheres Network
|relatedindicators = Culture
|description = With our data set on ‘cultural spheres’ we introduce an innovative way of describing these configurations in a relational way. Given that global cultural clusters of countries do not necessarily have rigid, clear-cut boundaries or ‘fault lines’, we apply valued two-mode social network analysis to define cultural similarities. Following this approach, countries can be tied by sharing a multitude of cultural characteristics. We draw on a variety of variables that describe cultural characteristics like dominant religion(s), dominant language, colonial history, gender relations, civil freedom etc. As a result, we get a fuzzy typology of cultural spheres. This typology consists of yearly valued networks, spanning a time frame of 1789 until 2010. The more of these characteristics two countries share, the more closely connected they are. We assume that configurations of statehood and state policies not only correspond to world regions, but also to cultural spheres that can be characterized empirically by consolidated relations in dynamic subnetworks.
|codingrules =
<ul>
<li>Several binary variables have been used for the construction of this network (they can be found under the subcategory "Cultural Characteristics". We specifically coded them as dichotomous variables i.e. they either have the value 1 or 0. In all cases, 1 depicts some characteristic like Islam being the dominant religion or being colonized by Germany. A value of 0 would mean that this country does not have that characteristic. We end up with a matrix for every single observed year respectively. These matrices show the countries in the rows and the cultural characteristics in the column coded, as described above, as 0 or 1. This is essentially a two-mode network depicting the membership of countries in a number of different cultural categories. From these bipartite networks we projected the one-mode networks with the assumption that if two states share the same characteristic, they have one tie with one another. While doing so, we compute a weighted network by simply adding up the number of similar connections.</li>
<li>For further information see the Technical Paper: Besche-Truthe, Fabian, Helen Seitzer, and Michael Windzio. 2020. ''Cultural Spheres – Creating a dyadic dataset of cultural proximity''. Bremen: SFB 1342. Technical Paper Series, 05/2020. https://doi.org/10.26092/elib/2081.</li>
</ul>
|citation = Besche-Truthe, Fabian, Helen Seitzer, and Michael Windzio. 2020. ''Cultural Spheres – Creating a dyadic dataset of cultural proximity''. Bremen: SFB 1342. Technical Paper Series 05/2020. https://doi.org/10.26092/elib/2081.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>

|revisions = No revisions yet
|sources =
}}

International student mobility - normalized on the nr. of tertiary students received

2026-07-31T18:14:13Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = edu_internat_stud_percent_received
|category= Migration
|label = inbound international students/nr. tertiary students in destination country
|relatedindicators =
<ul>
<li>[[International student mobility]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in destination country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in origin country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students sent]]</li>
</ul>
|description = The data source for the student mobility network consists of all independent countries acknowledged as such by the United Nations (UN) at the time of 31st of January 2019, which have a higher education sector in place. We use data on degree mobility and refer to the one provided by the UNESCO Institute for Statistics (UIS).
|codingrules =
<ul>
<li>UNESCO defines transnational students as "[s]tudents who have crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin" (2010, 264).</li>
<li>UIS asks countries to submit data annually on the numbers and origins of transnational students enrolled in their tertiary education institutions for at least a year. We use this data for the years 1998-2019. However, there is a large amount of missing data and in many cases, it is hard to distinguish whether a) the number of outgoing students is so small that none have been reported, or b) there actually have not been any incoming students from a particular country, or c) whether the data is simply missing. We assume that if there were a high number of students from country O (country of origin) studying in country D (destination country), the UIS would have at least an approximate figure. We thus decided to replace all missing data with the value of zero, since there was no case in which actually zero international students have been reported.</li>
<li>The data has been modified accordingly: Inbound international students as percentage of the total amount of international students received by the destination country. Thus answering the question: What percentage of all international students enrolled in B comes from A? Therefore, we model the importance of A as the origin country for international students in B.</li>
</ul>

|citation = Vögtle, Eva M., and Michael Windzio. 2019. "Looking for freedom? Networks of international student mobility and countries’ levels of democracy." ''The Geographical Journal'' 186 (1): 103–115. http://dx.doi.org/10.1111/geoj.12329.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer (A05), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul>
<li>
UNESCO Institute for Statistics (UIS)</li>
</ul>
}}

International student mobility - normalized on the nr. of tertiary students sent

2026-07-31T18:13:52Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = edu_internat_stud_percent_sent
|category= Migration
|label = inbound international students/nr. tertiary students in destination country
|relatedindicators =
<ul>
<li>[[International student mobility]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in destination country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in origin country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students received]]</li>
</ul>
|description = The data source for the student mobility network consists of all independent countries acknowledged as such by the United Nations (UN) at the time of 31st of January 2019, which have a higher education sector in place. We use data on degree mobility and refer to the one provided by the UNESCO Institute for Statistics (UIS).
|codingrules =
<ul>
<li>UNESCO defines transnational students as "[s]tudents who have crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin" (2010, 264).</li>
<li>UIS asks countries to submit data annually on the numbers and origins of transnational students enrolled in their tertiary education institutions for at least a year. We use this data for the years 1998-2019. However, there is a large amount of missing data and in many cases, it is hard to distinguish whether a) the number of outgoing students is so small that none have been reported, or b) there actually have not been any incoming students from a particular country, or c) whether the data is simply missing. We assume that if there were a high number of students from country O (country of origin) studying in country D (destination country), the UIS would have at least an approximate figure. We thus decided to replace all missing data with the value of zero, since there was no case in which actually zero international students have been reported.</li>
<li>The data has been modified accordingly: Inbound international students as percentage of the total amount of international students from the origin country. Thus answering the question: What percentage of all international students sent from A is enrolled in B? Therefore, we model the importance of B as the destination country for international students from A.</li>
</ul>
|citation = Vögtle, Eva M., and Michael Windzio. 2019. "Looking for freedom? Networks of international student mobility and countries’ levels of democracy." ''The Geographical Journal'' 186 (1): 103–115. http://dx.doi.org/10.1111/geoj.12329.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer (A05), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul>
<li>
UNESCO Institute for Statistics (UIS)</li>
</ul>
}}

International student mobility - normalized on the nr. of tertiary students in origin country

2026-07-31T18:13:18Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = edu_internat_stud_percent_enrol_origin
|category= Migration
|label = inbound international students/nr. tertiary students in sending country
|relatedindicators =
<ul>
<li>[[International student mobility]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in destination country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students sent]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students received]]</li>
</ul>
|description = The data source for the student mobility network consists of all independent countries acknowledged as such by the United Nations (UN) at the time of 31st of January 2019, which have a higher education sector in place. We use data on degree mobility and refer to the one provided by the UNESCO Institute for Statistics (UIS).
|codingrules =
<ul>
<li>UNESCO defines transnational students as "[s]tudents who have crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin" (2010, 264).</li>
<li>UIS asks countries to submit data annually on the numbers and origins of transnational students enrolled in their tertiary education institutions for at least a year. We use this data for the years 1998-2019. However, there is a large amount of missing data and in many cases, it is hard to distinguish whether a) the number of outgoing students is so small that none have been reported, or b) there actually have not been any incoming students from a particular country, or c) whether the data is simply missing. We assume that if there were a high number of students from country O (country of origin) studying in country D (destination country), the UIS would have at least an approximate figure. We thus decided to replace all missing data with the value of zero, since there was no case in which actually zero international students have been reported.</li>
<li>The data has been modified accordingly: inbound international students as percentage of the nr. of tertiary students in origin country. Thus answering the question: What percentage of enrolled tertiary students from A is enrolled in B? Therefore, we model the importance of B as the destination country for the tertiary education system in A.</li>
</ul>

|citation = Vögtle, Eva M., and Michael Windzio. 2019. "Looking for freedom? Networks of international student mobility and countries’ levels of democracy." ''The Geographical Journal'' 186 (1): 103–115. http://dx.doi.org/10.1111/geoj.12329.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer (A05), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul>
<li>
UNESCO Institute for Statistics (UIS)</li>
</ul>
}}

International student mobility - normalized on the nr. of tertiary students in destination country

2026-07-31T18:12:55Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = edu_internat_stud_percent_enrol_destination
|category= Migration
|label = inbound international students/nr. tertiary students in destination country
|relatedindicators =
<ul>
<li>[[International student mobility]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in origin country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students sent]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students received]]</li>
</ul>
|description = The data source for the student mobility network consists of all independent countries acknowledged as such by the United Nations (UN) at the time of 31st of January 2019, which have a higher education sector in place. We use data on degree mobility and refer to the one provided by the UNESCO Institute for Statistics (UIS).

|codingrules =
<ul>
<li>UNESCO defines transnational students as "[s]tudents who have crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin" (2010, 264).</li>
<li>UIS asks countries to submit data annually on the numbers and origins of transnational students enrolled in their tertiary education institutions for at least a year. We use this data for the years 1998-2019. However, there is a large amount of missing data and in many cases, it is hard to distinguish whether a) the number of outgoing students is so small that none have been reported, or b) there actually have not been any incoming students from a particular country, or c) whether the data is simply missing. We assume that if there were a high number of students from country O (country of origin) studying in country D (destination country), the UIS would have at least an approximate figure. We thus decided to replace all missing data with the value of zero, since there was no case in which actually zero international students have been reported.</li>
<li>The data has been modified accordingly: Inbound international students as percentage of the nr. of tertiary students in destination country. Thus asking the question: What is the percentage of enrolled tertiary students in B originating from A? Therefore, we model the importance of A as the origin country for the tertiary education system in B.</li>
</ul>
|citation = Vögtle, Eva M., and Michael Windzio. 2019. “Looking for freedom? Networks of international student mobility and countries’ levels of democracy.” ''The Geographical Journal'' 186 (1): 103–15. http://dx.doi.org/10.1111/geoj.12329.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer (A05), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul>
<li>
UNESCO Institute for Statistics (UIS)</li>
</ul>
}}

International student mobility

2026-07-31T18:12:35Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = edu_internat_student
|category= Migration
|label = Inbound international students
|relatedindicators =
<ul>
<li>[[International student mobility - normalized on the nr. of tertiary students in destination country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students in origin country]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students sent]]</li>
<li>[[International student mobility - normalized on the nr. of tertiary students received]]</li>
</ul>
|description = The data source for the student mobility network consists of all independent countries acknowledged as such by the United Nations (UN) at the time of 31st of January 2019, which have a higher education sector in place. We use data on degree mobility and refer to the one provided by the UNESCO Institute for Statistics (UIS).
|codingrules =
<ul>
<li>UNESCO defines transnational students as "[s]tudents who have crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin" (2010, 264).</li>
<li>UIS asks countries to submit data annually on the numbers and origins of transnational students enrolled in their tertiary education institutions for at least a year. We use this data for the years 1998-2019. However, there is a large amount of missing data and in many cases, it is hard to distinguish whether a) the number of outgoing students is so small that none have been reported, or b) there actually have not been any incoming students from a particular country, or c) whether the data is simply missing. We assume that if there were a high number of students from country O (country of origin) studying in country D (destination country), the UIS would have at least an approximate figure. We thus decided to replace all missing data with the value of zero, since there was no case in which actually zero international students have been reported.</li>
</ul>
|citation = Vögtle, Eva M., and Michael Windzio. 2019. "Looking for freedom? Networks of international student mobility and countries’ levels of democracy." ''The Geographical Journal'' 186 (1): 103–115. http://dx.doi.org/10.1111/geoj.12329.
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer (A05), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul>
<li>
UNESCO Institute for Statistics (UIS)</li>
</ul>
}}

Bilateral migration flow - normalized on migration volume of origin

2026-07-31T18:10:29Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=prop_outgoing_by_dest_and_year
|category=Migration
|label=Bilateral migration flow - normalized on migration volume of origin
|relatedindicators=<ul>
<li>[[Bilateral migration flow]]</li>
<li>[[Bilateral migration flow - normalized on destination population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of destination]]</li>
<li>[[Bilateral migration flow - normalized on origin population]]</li>
</ul>
|description=This dataset contains the bilateral migration stock from 1990 to 2019. The data is available in 5-year intervals in from the original source (UIS) and was interpolated linearly. This dataset contains the migration stock by year and destination as a percentage of origin countries' population (What is the percentage of citizens living in A migrating to B?).
|codingrules=
|citation=
|relatedpublications=
|projectmanagers=Michael Windzio, Helen Seitzer, Fabian Besche-Truthe
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=<ul>
<li>
https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp</li>
</ul>
}}

Bilateral migration flow - normalized on origin population

2026-07-31T18:10:06Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=prop_outgoing_by_orig_pop
|category=Migration
|label=Bilateral migration flow - normalized on origin population
|relatedindicators=<ul>
<li>[[Bilateral migration flow]]</li>
<li>[[Bilateral migration flow - normalized on destination population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of destination]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of origin]]</li>
</ul>
|description=This dataset contains the bilateral migration stock from 1990 to 2019. The data is available in 5-year intervals in from the original source (UIS) and was interpolated linearly. This dataset contains the migration stock by year and destination as a percentage of origin countries' population (What is the percentage of citizens living in A migrating to B?), 1990-2019
|codingrules=
|citation=
|relatedpublications=
|projectmanagers=Michael Windzio, Helen Seitzer, Fabian Besche-Truthe
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=<ul>
<li>
https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp </li>
</ul>
}}

Bilateral migration flow - normalized on migration volume of destination

2026-07-31T18:09:43Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=prop_incoming_by_origin_and_year
|category=Migration
|label=Bilateral migration flow - normalized on migration volume of destination
|relatedindicators=<ul>
<li>[[Bilateral migration flow]]</li>
<li>[[Bilateral migration flow - normalized on destination population]]</li>
<li>[[Bilateral migration flow - normalized on origin population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of origin]]</li>
</ul>
|description=This dataset contains the bilateral migration stock from 1990 to 2019. The data is available in 5-year intervals in from the original source (UIS) and was interpolated linearly. This dataset contains the migration stock by year and origin as a percentage of the overall migration to a destination country per year (What proportion of B's yearly migration comes from A?), 1990-2019
|codingrules=
|citation=
|relatedpublications=
|projectmanagers=Michael Windzio, Helen Seitzer, Fabian Besche-Truthe
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=* https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp
}}

Bilateral migration flow - normalized on destination population

2026-07-31T18:09:20Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=prop_incoming_by_dest_pop
|category=Migration
|label=Bilateral migration flow - normalized on destination population
|relatedindicators=
<ul>
<li>[[Bilateral migration flow]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of destination]]</li>
<li>[[Bilateral migration flow - normalized on origin population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of origin]]</li>
</ul>
|description=This dataset contains the bilateral migration stock from 1990 to 2019. The data is available in 5-year intervals in from the original source (UIS) and was interpolated linearly. This dataset is normalized on the destination popuplation and therefore depicts the percentage of the origin countries' population on the total of the destination countries' population (How big is the percentage of people from A living in B?).
|codingrules=
|citation=
|relatedpublications=
|projectmanagers=Michael Windzio, Helen Seitzer, Fabian Besche-Truthe
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=* https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp
}}

Bilateral migration flow

2026-07-31T18:08:45Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=value labels
|techname=migration_stock_bilateral
|category=Migration
|label=Bilateral migration flow
|relatedindicators=
<ul>
<li>[[Bilateral migration flow - normalized on destination population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of destination]]]</li>
<li>[[Bilateral migration flow - normalized on origin population]]</li>
<li>[[Bilateral migration flow - normalized on migration volume of origin]]</li>
</ul>
|description=This dataset contains the bilateral migration stock from 1990 to 2019. The data is available in 5-year intervals in from the original source (UIS) and was interpolated linearly.
|codingrules=
|citation=
|relatedpublications=
|projectmanagers=Michael Windzio, Helen Seitzer, Fabian Besche-Truthe
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=* https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp
}}

GDP in current NCU, WB

2026-07-31T18:03:21Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cncu_wb
|category= Economic and financial factors
|label = GDP in current NCU
|description= This indicator captures nominal GDP in current national currency units.
|relatedindicators =
<ul>
<li>[[GDP per capita in current PPP-$, WB]]</li>
<li>[[GDP per capita in current USD, WB]]</li>
</ul>
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
|citation = World Bank. 2026. "GDP (current LCU)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CN.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "GDP (current LCU)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
}}

International autonomy

2026-07-31T14:35:53Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_sov_international_vdem
|category=Political factors
|label=International autonomy from other states (V-Dem)
|relatedindicators=<ul>
<li>[[Domestic autonomy]]</li>
<li>[[State authority over territory]]</li>
<li>[[Independent states]]</li>
</ul>
|description=This indicator captures aggregated V-Dem country expert ratings on whether a state is autonomous from the control of other states in conducting its foreign policy. Lower values indicate that a state is less autonomous, higher values suggest stronger autonomy from other states.
|codingrules=Ordinal ratings were aggregated to interval scale applying V-Dem's measurement model (Pemstein et al. 2021). Experts were asked "Is the state autonomous from the control of other states with respect to the conduct of domestic policy?" and the ordinal wordings of the question were (Coppedge et al. 2021, 187):
<ul>
<li>0: Non-autonomous. Foreign policy is controlled by an external power, either de facto or de jure. The most common examples of this are colonial rule and military occupation (e.g. quadripartite occupation of Germany in 1945). Situations in which domestic actors provide de jure cover for de facto control by a foreign power should not be construed as semi-autonomy (e.g. Vichy France). Governments in exile that control underground forces waging unconventional warfare are not considered as mitigating an occupation regime (e.g. countries under German occupation during WWII).</li>
<li>1: Semi-autonomous. An external political actor directly constrains the ability of domestic actors to pursue an independent foreign policy course in some important areas. This may be the product of explicit treaty provisions or well-understood rules of the game from which the subject state cannot withdraw. Examples would include Soviet strictures over rule in so-called "satellite" states in Eastern Europe, and explicitly negotiated postwar settlements (e.g. Austria following WWII).</li>
<li>2: Autonomous. Domestic political actors exercise foreign policy free of the direct control of external political actors. Direct control is meant to exclude the exercise of constraint or the impact of interdependence in the international system. Treaties in which states concede some part of that control to a supra- or international organization voluntarily, and from which there is a possibility of exit should not be interpreted as a violation of autonomy.</li>
</ul>
|citation=Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.
|relatedpublications=<ul>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v11.1</li>
<li>Version 0.003: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.003 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.003 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Domestic autonomy

2026-07-31T14:35:40Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_sov_domestic_vdem
|category=Political factors
|label=Domestic autonomy from other states (V-Dem)
|relatedindicators=<ul>
<li>[[International autonomy]]</li>
<li>[[State authority over territory]]</li>
<li>[[Independent states]]</li>
</ul>
|description=This indicator captures aggregated V-Dem country expert ratings on whether a state is autonomous from the control of other states in conducting its domestic policy. Lower values indicate that a state is less autonomous, higher values suggest stronger autonomy from other states.
|codingrules=Ordinal ratings were aggregated to interval scale applying V-Dem's measurement model (Pemstein et al. 2021). Experts were asked "Is the state autonomous from the control of other states with respect to the conduct of domestic policy?" and the ordinal wordings of the question were (Coppedge et al. 2021, 186-187):
<ul>
<li>0: Non-autonomous. National level authority is exercised by an external power, either by law or in practice. The most common examples of this are direct colonial rule and military occupation (e.g. quadripartite occupation of Germany in 1945). It also includes situations in which domestic actors provide de jure cover for de facto control by a foreign power (e.g. Vichy France). However, control of some part of the territory of a state by an enemy during war is not considered control by external actors if the sovereign government remains on scene and continues to wage conventional war (e.g., the USSR during WW II).</li>
<li>1: Semi-autonomous. An external political actor directly constrains the ability of domestic actors to rule, decides who can or cannot rule through formal rules or informal understandings, or precludes certain policies through explicit treaty provisions or well-understood rules of the game from which the subject state cannot withdraw. Examples include Soviet "satellite" states in Eastern Europe, and situations where colonial powers grant limited powers of national selfgovernment to their possessions (e.g., protectorates and limited home government).</li>
<li>2: Autonomous. Domestic political actors exercise political authority free of the direct control of external political actors.</li>
</ul>

This precludes "restrictions emanating from [freely negotiated] treaties (e.g., NATO), international organizations (e.g., the WTO), or confederations (e.g., the European Union)" (Coppedge et al. 2021, 186).
|citation=Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.
|relatedpublications=<ul>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v11.1</li>
<li>Version 0.003: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.003 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.003 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Deliberative democracy index

2026-07-31T14:27:08Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_democ_delibdem_vdem
|category=Political factors
|label=Deliberative democracy index
|relatedindicators=<ul>
<li>[[Electoral democracy index]]</li>
<li>[[Liberal democracy index]]</li>
<li>[[Participatory democracy index]]</li>
<li>[[Egalitarian democracy index]]</li>
</ul>
|description=This indicator is part of V-Dem's high level democracy indices capturing features of democracy at the most abstract level.
The "Deliberative democracy index" in particular captures to what extent the ideal of '''deliberative''' democracy is achieved. It ranges from low (0) to high (1).

Conceptually, it is defined as follows:
"The deliberative component enshrines the core value that political decisions in pursuit of the
public good should be informed by respectful and reasonable dialogue at all levels rather
than by emotional appeals, solidary attachments, parochial interests, or coercion" (Coppedge et al. 2016, 583).
|codingrules=
<ul>
<li>The index is composed of several components and lower-level indices applying V-Dem's measurement model (Pemstein et al. 2021) which in turn are indices built from a number of indicators. The index itself is "a BFA [Bayesian factor analysis] attempting to measure the extent to which political elites offer public justifications for their positions on matters of public policy, justify their positions in terms of the public good, acknowledge and respect counter-arguments; and how wide the range of consultation is at elite levels (''v2xdl_delib'', based on 5 indicators)" (Coppedge et al. 2016, 583) [emphasis in original; note that variable names in italics refer to V-Dem's nomenclature in their dataset, not in WeSIS].</li>

<li>For the exact index aggregation formula see Coppedge et al. 2021, Appendix A.</li>
</ul>
|citation=Coppedge, Michael, Staffan Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2016. "Measuring high level democratic principles using the V-Dem data." ''International Political Science Review'' 37 (2): 580–593. [https://doi.org/10.1177/0192512115622046 https://doi.org/10.1177/0192512115622046].
|relatedpublications=<ul>
<li>Coppedge, Michael, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2015. ''Measuring High Level Democratic Principles using the V-Dem Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 06/2015. https://gupea.ub.gu.se/server/api/core/bitstreams/1ab35b58-1382-4071-bec8-4ed294405fe1/content.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.</li>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v10 + two erroneous COW codes fixed resulting from country name mismatches</li>
<li>Version 0.003: Updated with data from V-Dem v11.1</li>
<li>Version 0.004: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.004 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.004 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Liberal democracy index

2026-07-31T14:26:52Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_democ_libdem_vdem
|category=Political factors
|label=Liberal democracy index
|relatedindicators=<ul>
<li>[[Electoral democracy index]]</li>
<li>[[Participatory democracy index]]</li>
<li>[[Deliberative democracy index]]</li>
<li>[[Egalitarian democracy index]]</li>
</ul>
|description=This indicator is part of V-Dem's high level democracy indices capturing features of democracy at the most abstract level.
The "Liberal democracy index" in particular captures to what extent the ideal of '''liberal''' democracy is achieved. It ranges from low (0) to high (1).

Conceptually, it is defined as follows:
"The liberal component of democracy embodies the intrinsic value of protecting individual and minority rights against potential 'tyranny of the majority' and state repression more generally. This is achieved through constitutionally protected civil liberties, a strong rule of law, and effective checks and balances that limit the use of executive power" (Coppedge et al. 2016, 582).
|codingrules=
<ul>
<li>The index is composed of several components and lower-level indices applying V-Dem's measurement model (Pemstein et al. 2021) which in turn are indices built from a number of indicators. The index itself is "the mean of three BFAs [Bayesian factor analyses] tapping into (1) equality before the law and individual liberty (''v2x_rol'', based on 14 indicators); (2) judicial constraints on the executive (''v2x_jucon'', based on 5 indicators); and (3) legislative constraints on the executive (''v2x_legcon'', based on 4 indicators)" (Coppedge et al. 2016, 582-3) [emphasis in original; note that variable names in italics refer to V-Dem's nomenclature in their dataset, not in WeSIS].</li>
<li>For the exact index aggregation formula see Coppedge et al. 2021, Appendix A.</li>
</ul>
|citation=Coppedge, Michael, Staffan Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2016. "Measuring high level democratic principles using the V-Dem data." ''International Political Science Review'' 37 (2): 580–593. [https://doi.org/10.1177/0192512115622046 https://doi.org/10.1177/0192512115622046].
|relatedpublications=<ul>
<li>Coppedge, Michael, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2015. ''Measuring High Level Democratic Principles using the V-Dem Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 06/2015. https://gupea.ub.gu.se/server/api/core/bitstreams/1ab35b58-1382-4071-bec8-4ed294405fe1/content.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.</li>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v10 + two erroneous COW codes fixed resulting from country name mismatches</li>
<li>Version 0.003: Updated with data from V-Dem v11.1</li>
<li>Version 0.004: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.004 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.004 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Egalitarian democracy index

2026-07-31T14:26:33Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_democ_delibdem_vdem
|category=Political factors
|label=Deliberative democracy index
|relatedindicators=<ul>
<li>[[Electoral democracy index]]</li>
<li>[[Liberal democracy index]]</li>
<li>[[Participatory democracy index]]</li>
<li>[[Deliberative democracy index]]</li>
</ul>
|description=This indicator is part of V-Dem's high level democracy indices capturing features of democracy at the most abstract level.
The "Egalitarian democracy index" in particular captures to what extent the ideal of '''egalitarian ''' democracy is achieved. It ranges from low (0) to high (1).

Conceptually, it is defined as follows:
"The egalitarian component [...] encapsulates the ideal of power distributed equally
among all citizens regardless of class, ethnicity, sexual orientation or other social groups.
Assuming that material and immaterial inequalities inhibit the actual exercise of formal
rights and liberties, a more equal distribution of resources, education, and health across various
groups should also enhance political equality" (Coppedge et al. 2016, 583).
|codingrules=
<ul>
<li>The index is composed of several components and lower-level indices applying V-Dem's measurement model (Pemstein et al. 2021) which in turn are indices built from a number of indicators.
The index itself is "a BFA [Bayesian factor analysis] based on indicators of both equal power distribution and equal resource distribution (''v2x_egal'', based on 8 indicators)" (Coppedge et al. 2016, 583) [emphasis in original; note that variable names in italics refer to V-Dem's nomenclature in their dataset, not in WeSIS].</li>

<li>For the exact index aggregation formula see Coppedge et al. 2021, Appendix A.</li>
</ul>
|citation=Coppedge, Michael, Staffan Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2016. "Measuring high level democratic principles using the V-Dem data." ''International Political Science Review'' 37 (2): 580–593. [https://doi.org/10.1177/0192512115622046 https://doi.org/10.1177/0192512115622046].
|relatedpublications=<ul>
<li>Coppedge, Michael, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2015. ''Measuring High Level Democratic Principles using the V-Dem Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 06/2015. https://gupea.ub.gu.se/server/api/core/bitstreams/1ab35b58-1382-4071-bec8-4ed294405fe1/content.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.</li>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v10 + two erroneous COW codes fixed resulting from country name mismatches</li>
<li>Version 0.003: Updated with data from V-Dem v11.1</li>
<li>Version 0.004: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.004 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.004 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Electoral democracy index

2026-07-31T14:15:47Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_polity_democ_polyarchy_vdem
|category=Political factors
|label=Electoral democracy index
|relatedindicators=<ul>
<li>[[Liberal democracy index]]</li>
<li>[[Participatory democracy index]]</li>
<li>[[Deliberative democracy index]]</li>
<li>[[Egalitarian democracy index]]</li>
</ul>

|description=This indicator is one of five V-Dem's high level democracy indices capturing features of democracy at the most abstract level.
The "Electoral democracy index" in particular captures to what extent the ideal of '''electoral''' democracy is achieved. It ranges from low (0) to high (1).

Conceptually, it is defined as follows:
"The electoral component of democracy embodies the core value of making rulers responsive
to citizens through competition for the approval of a broad electorate during periodic
elections. We have opted to capture Dahl's (1998) concept of polyarchy, which identifies the
following political institutions as constitutive of modern representative democracy: (1)
elected officials; (2) free, fair and frequent elections; (3) freedom of expression and alternative
sources of information; (4) associational autonomy; and (5) inclusive citizenship (universal
suffrage)" (Coppedge et al. 2016, 582).
|codingrules=
<ul>
<li>The index is composed of several components and lower-level indices applying V-Dem's measurement model (Pemstein et al. 2021) which in turn are indices built from a number of indicators. The index itsels is derived from "the elected executive index (''v2x_accex'', based on 12 indicators), the clean elections index (''v2xel_frefair'', based on 8 indicators), the freedom of expression index (''v2x_freexp_thick'', based on 9 indicators, including 3 for alternative sources of information), the freedom of association index (''v2x_frassoc_thick'', based on 6 indicators), and the suffrage indicator (''v2x_suffr''). All indices range from 0 to 1, and all except that on elected executive (which is a derived construct) are based on point predictions from Bayesian factor analyses (BFA) that all support unidimensionality" (Coppedge et al. 2016, 582) [emphasis in original; note that variable names in italics refer to V-Dem's nomenclature in their dataset, not in WeSIS].</li>
<li>For the exact index aggregation formula see Coppedge et al. 2021, Appendix A.</li>
</ul>
|citation=Coppedge, Michael, Staffan Lindberg, Svend-Erik Skaaning, Jan Teorell. 2016. "Measuring high level democratic principles using the V-Dem data". ''International Political Science Review'' 37 (2): 580-593. [https://doi.org/10.1177/0192512115622046 https://doi.org/10.1177/0192512115622046].
|relatedpublications=<ul>
<li>
Dahl, Robert A. 1998. ''On Democracy''. New Haven: Yale University Press.
</li>
<li>
Teorell, Jan, Michael Coppedge, Staffan Lindberg, and Svend-Erik Skaaning. 2019. "Measuring Polyarchy Across the Globe, 1900–2017." ''Studies in Comparative International Development'' 54 (1): 71–95. [https://doi.org/10.1007/s12116-018-9268-z https://doi.org/10.1007/s12116-018-9268-z].
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. V-Dem Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v10 + two erroneous COW codes fixed resulting from country name mismatches</li>
<li>Version 0.003: Updated with data from V-Dem v11.1</li>
<li>Version 0.004: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.004 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.004 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Combined Polity Score (Revised p4). Polity IV.

2026-07-31T14:13:22Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = polnat_polity_polity2
|category= Political factors
|subcategory = Regime Type
|label = Combined Polity Score
|relatedindicators = <ul><li>[[Institutionalized Democracy. Polity IV. ]]</li><li>[[Institutionalized Autocracy. Polity IV. ]]</li><li>[[Combined Polity Score. Polity IV. ]]</li><li>[[Electoral democracy index ]]</li></ul>
|description = Combined Polity Score: Revised Combined Polity Score: This variable is a modified version of the POLITY variable added in order to facilitate the use of the POLITY regime measure in time-series analyses.
|codingrules = It modifies the combined annual POLITY score by applying a simple treatment, or ““fix,” to convert instances of “standardized authority scores” (i.e., -66, -77, and -88) to conventional polity scores (i.e., within the range, -10 to +10). The values have been converted according to the following rule set:
<ul>
<li> -66 Cases of foreign “interruption” are treated as “system missing.”</li>
<li> -77 Cases of “interregnum,” or anarchy, are converted to a “neutral” Polity score of “0.”</li>
<li> -88 Cases of “transition” are prorated across the span of the transition.
<li> For example, country X has a POLITY score of -7 in 1957, followed by three years of -88 and, finally, a score of +5 in 1961. The change (+12) would be prorated over the intervening three years at a rate of per year, so that the converted scores would be as follows: 1957 -7; 1958 -4; 1959 -1; 1960 +2; and 1961 +5.</li>
<li> Note: Ongoing (-88) transitions in the most recent year are converted to “system
missing” values. Transitions (-88) following a year of independence, interruption (-66),
or interregnum (-77) are prorated from the value “0.” </li> </ul>
|citation = Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2017. ''Dataset Users’ Manual. Polity IV Project. Political Regime Characteristics and Transitions, 1800-2016''. Vienna: Center for Systemic Peace. https://www.systemicpeace.org/inscr/p4manualv2016.pdf.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources =
}}

Public sector corruption index

2026-07-31T11:32:40Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_politics_corruption_publ_vdem
|category=Political factors
|label=Aggregated mid-level index of corruption in the public sector (V-Dem)
|relatedindicators=<ul>
<li>[[Political corruption index]]</li>
<li>[[Executive corruption index]]</li>
<li>[[Regime corruption index]]</li>
<li>[[Corrupt activities in judicial decisions]]</li>
<li>[[Corrupt activities of members of legislature]]</li>
<li>[[Election vote buying]]</li>
</ul>
|description=This indicator is a V-Dem mid-level index focusing on several types of corruption in the public sector. It ranges from low (0) to high (1) with lower values indicating less corruption.

Conceptually, the index captures the extent to which public sector employees routinely "grant favors in exchange for bribes, kickbacks, or other material inducements" and how often "they steal, embezzle, or misappropriate public funds or other state resources for personal or family use" (Coppedge et al. 2021, 297).
|codingrules=
<ul>
<li>The index is composed of several components and lower-level indices applying V-Dem's measurement model (Pemstein et al. 2021) which in turn are indices built from a number of indicators. The index itself is the average of two indicators, namely "public sector bribery (''v2excrptps'') and embezzlement (''v2exthftps'')" (Coppedge et al. 2021, 297) [note that variable names in italics refer to V-Dem's nomenclature in their dataset, not in WeSIS].</li>
</ul>
|citation=McMann, Kelly, Daniel Pemstein, Brigitte Seim, Jan Teorell, and Staffan I. Lindberg. 2016. ''Strategies of Validation: Assessing the Varieties of Democracy Corruption Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 23/2016. http://dx.doi.org/10.2139/ssrn.2727595.
|relatedpublications=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li> </ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v11.1</li>
<li>Version 0.003: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.003 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.003 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Power distributed by sexual orientation

2026-07-31T11:15:58Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=polnat_politics_power_distri_sexual_vdem
|category=Political factors
|label=Distribution of political power by sexual orientation (V-Dem)
|relatedindicators=<ul>
<li>[[Power distributed by socioeconomic position]]</li>
<li>[[Power distributed by social group]]</li>
<li>[[Power distributed by gender]]</li>
</ul>
|description=This indicator contains aggregated V-Dem country expert ratings on whether political power was equally distributed between heterosexuals and lesbian, gay, bisexual,and transgender (LGBT) members. Lower values indicate power distribution in favor of heterosexuals, higher values suggest equal distribution of power.
|codingrules=Ordinal ratings were aggregated to interval scale applying V-Dem's measurement model (Pemstein et al. 2021). Experts were asked "To what extent is political power distributed according to sexual orientation?" and the ordinal wordings of the question were (Coppedge et al. 2021, 206):
<ul>
<li>0: LGBTs are entirely excluded from the public sphere and thus deprived of any real political power (even though they may possess formal powers such as the ballot).</li>
<li>1: LGBTs have much less political power than heterosexuals. LGBTs enjoy formal rights to participate in politics but are subject to informal norms that often serve to exclude them from the halls of power.</li>
<li>2: LGBTs have somewhat less political power than heterosexual citizens.</li>
<li>3: LGBTs have about the same political power as heterosexuals. Each group enjoys a degree of political power that is roughly proportional to their population.</li>
<li>4: LGBTs enjoy somewhat more political power than heterosexuals by virtue of greater wealth, education, and high level of organization and mobilization.</li>
</ul>
|citation=Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. ''V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/static/website/img/refs/codebookv111.pdf.
|relatedpublications=<ul>
<li>
Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2021. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2021. http://dx.doi.org/10.2139/ssrn.3799967.
</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, and Daniel Ziblatt. 2025. ''V-Dem Codebook v15. Varieties of Democracy (V-Dem) Project''. University of Gothenburg: Varieties of Democracy Institute. https://www.v-dem.net/documents/55/codebook.pdf.</li>
<li>Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2025. ''The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data''. Gothenburg: Varieties of Democracy Institute. Working Paper, 21/2025. https://www.v-dem.net/media/publications/wp21_2025.pdf.</li>
</ul>
|projectmanagers=Nils Düpont (INF), Paul Bederke
|datarelease=<ul>
<li>Version 0.001: Initial release</li>
<li>Version 0.002: Updated with data from V-Dem v11.1</li>
<li>Version 0.003: Updated with data from V-Dem v15</li>
</ul>
|revisions=Prior to version 0.003 data for Germany and Yemen were separated adhering to their COW code in a strict sense; as of v0.003 data is now in line with V-Dem's continous ID reflecting the [[Country_codes#A_note_on_.22counterfactual_and_related_unit.22_coding|"related unit coding"]] of WeSIS.
|sources=<ul>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Garry Hindle, Nina Ilchenko, Joshua Krusell, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2021. ''V-Dem [Country-Year/Country-Date] Dataset v11.1 [Dataset]''. https://doi.org/10.23696/vdemds21.</li>
<li>Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Marcus Tannenberg, Eitan Tzelgov, Yi-ting Wang, Felix Wiebrecht, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2025. ''V-Dem [Country-Year/Country-Date] Dataset v15 [Dataset]''. https://doi.org/10.23696/vdemds25.</li>
</ul>
}}

Service employment in percent of total employment, ILO

2026-07-31T09:33:19Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_servempl_perctot_ilo
|category= Economic and financial factors
|label = Service employment in percent of total employment
|relatedindicators = <ul>
<li>[[Agricultural employment in percent of total employment, ILO]]</li>
<li>[[Employment in the industrial sector as percentage of total employment, ILO]]</li>
<li>[[Informal employment in percent of total non-agricultural employment, ILO]]</li>
</ul>
|description = This indicator captures employment in the service sector in percent of total employment.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.SRV.EMPL.ZS.</li>
</ul>
|citation = World Bank. 2026. "Employment in services (% of total employment) (modeled ILO estimate)." ''World Bank Open Data. https://data.worldbank.org/indicator/SL.SRV.EMPL.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "Employment in services (% of total employment) (modeled ILO estimate)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.SRV.EMPL.ZS.</li>
</ul>
}}

Service employment in percent of total employment, ILO

2026-07-31T09:27:38Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_servempl_perctot_ilo
|category= Economic and financial factors
|label = Service employment in percent of total employment
|relatedindicators = <ul>
<li>[[Agricultural employment in percent of total employment, ILO]]</li>
<li>[[Employment in the industrial sector as percentage of total employment, ILO]]</li>
<li>[[Informal employment in percent of total non-agricultural employment, ILO]]</li>
</ul>
|description = This indicator captures employment in the service sector in percent of total employment.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.SRV.EMPL.ZS.</li>
</ul>
|citation = World Bank. 2026. ''Employment in services (% of total employment) (modeled ILO estimate)''. https://data.worldbank.org/indicator/SL.SRV.EMPL.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "Employment in services (% of total employment) (modeled ILO estimate)". ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.SRV.EMPL.ZS.</li>
</ul>
}}

Informal employment in percent of total non-agricultural employment, ILO

2026-07-31T08:59:30Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_tinformalempl_percnaempl_ilo
|category= Economic and financial factors
|label = Informal employment in percent of total non-agricultural employment
|relatedindicators =
<ul>
<li>[[Agricultural employment in percent of total employment, ILO]]</li>
<li>[[Employment in the industrial sector as percentage of total employment, ILO]]</li>
<li>[[Service employment in percent of total employment, ILO]]</li>
</ul>
|description = This indicator captures informal employment in percent of total non-agricultural employment.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.ISV.IFRM.ZS.</li>
</ul>
|citation = World Bank. 2026. "Employment in non-agriculture (% of total informal employment)." ''World Bank Open Data''. https://data.worldbank.org/indicator/SL.ISV.IFRM.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions. Data as of September 2020. This indicator is no longer supported by the World Bank Database and will not be updated (https://data.worldbank.org/indicator/SL.ISV.IFRM.ZS).
|sources = <ul>
<li>World Bank. 2026. "Employment in non-agriculture (% of total informal employment)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.ISV.IFRM.ZS.</li>
</ul>
}}

Employment in the industrial sector as percentage of total employment, ILO

2026-07-31T08:49:21Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_industryempl_perctotal_ilo
|category= Economic and financial factors
|label = Employment in the industrial sector as percentage of total employment
|relatedindicators =
<ul>
<li>[[Agricultural employment in percent of total employment, ILO]]</li>
<li>[[Informal employment in percent of total non-agricultural employment, ILO]]</li>
<li>[[Service employment in percent of total employment, ILO]]</li>
</ul>
|description = This indicator captures employment in the industrial sector (mining and quarrying, manufacturing, construction, and public utilities) as a percentage of total employment based on ILO estimates.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.IND.EMPL.ZS.</li>
</ul>
|citation = World Bank. 2026. "Employment in industry (% of total employment) (modeled ILO estimate)." ''World Bank Open Data''. https://data.worldbank.org/indicator/SL.IND.EMPL.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "Employment in industry (% of total employment) (modeled ILO estimate)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.IND.EMPL.ZS.</li>
</ul>
}}

Agricultural employment in percent of total employment, ILO

2026-07-30T16:21:28Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=Not applicable
|techname=econnat_economics_agricempl_perctot_ilo
|category=Economic and financial factors
|label=Agricultural employment in percent of total employment, ILO
|relatedindicators=
<ul>
<li>[[Employment in the industrial sector as percentage of total employment, ILO]]</li>
<li>[[Informal employment in percent of total non-agricultural employment, ILO]]</li>
<li>[[Service employment in percent of total employment, ILO]]</li>
</ul>
|description=This indicator captures employment in the agricultural sector in percent of total employment.
|codingrules=<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.AGR.EMPL.ZS.</li>
</ul>
|citation = World Bank. 2026. "Employment in agriculture (% of total employment) (modeled ILO estimate)." ''World Bank Open Data''. https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease=<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on World Bank data published 2025.04.15</li></ul>
|revisions=Update based on World Bank data published 2025.04.15
|sources=<ul>
<li>World Bank. 2026. "Employment in agriculture (% of total employment) (modeled ILO estimate)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=SL.AGR.EMPL.ZS.</li>
</ul>
}}

Annual inflation in percent using GDP deflators, WB

2026-07-30T16:02:55Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdpdefl_percgrowth_wdi
|category= Economic and financial factors
|label = Annual inflation in percent using GDP deflators
|relatedindicators =
|description = This indicator captures annual inflation in percent using GDP deflators.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.DEFL.KD.ZG.</li>
</ul>
|citation = World Bank. 2026. "Inflation, GDP deflator (annual %)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "Inflation, GDP deflator (annual %)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.DEFL.KD.ZG.</li>
</ul>
}}

GDP in current USD (WB)

2026-07-30T16:01:00Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cusd_wb
|category= Economic and financial factors
|label = Current GDP in USD (WB)
|relatedindicators =
|description = This indicator captures the Gross Domestic Product (GDP) in current USD.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CD.</li>
</ul>
|citation = World Bank. 2026. "GDP (current US$)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP (current US$)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CD.
</ul>
}}

Annual real GDP growth per capita, WB

2026-07-30T16:00:35Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_percgrowth_wb
|category= Economic and financial factors
|label = Annual real GDP growth per capita
|relatedindicators =
<ul>
<li>[[Annual real GDP growth in percent, WB]]</li>
</ul>
|description = This indicator captures annual real GDP growth per capita.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.KD.ZG.</li>
</ul>
|citation = World Bank. 2026. "GDP per capita growth (annual %)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP per capita growth (annual %)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.KD.ZG.</li>
</ul>
}}

Annual real GDP growth per capita, WB

2026-07-30T16:00:16Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_percgrowth_wb
|category= Economic and financial factors
|label = Annual real GDP growth per capita
|relatedindicators =
<ul>
<li>[[Annual real GDP growth in percent, World Bank]]</li>
</ul>
|description = This indicator captures annual real GDP growth per capita.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.KD.ZG.</li>
</ul>
|citation = World Bank. 2026. "GDP per capita growth (annual %)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP per capita growth (annual %)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.KD.ZG.</li>
</ul>
}}

Annual real GDP growth in percent, WB

2026-07-30T15:59:39Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_percgrowth_wb
|category= Economic and financial factors
|label = Annual real GDP growth in percent
|relatedindicators =
<ul>
<li>[[Annual real GDP growth per capita, World Bank]]</li>
</ul>
|description = This indicator captures annual real GDP growth in percent.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.KD.ZG.</li>
</ul>
|citation = World Bank. 2026. "GDP growth (annual %)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "GDP growth (annual %)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.KD.ZG.</li>
</ul>
}}

Annual real GDP growth per capita, WB

2026-07-30T15:56:20Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_percgrowth_wb
|category= Economic and financial factors
|label = Annual real GDP growth per capita
|relatedindicators =
<ul>
<li>[[Annual real GDP growth in percent, World Bank]]</li>
</ul>
|description = This indicator captures annual real GDP growth per capita.
|codingrules = <ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.KD.ZG.</li>
</ul>
|citation = World Bank. 2026. "GDP per capita growth (annual %)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP per capita growth (annual %)." ''DataBank. World Development Indicators''. https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG.</li>
</ul>
}}

Central government debt in percent of GDP, IMF

2026-07-30T15:53:56Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_cgovdebt_pergdp_imf
|category= Economic and financial factors
|label = Central government debt in percent of GDP
|relatedindicators =
|description = This indicator captures central government debt in percent of GDP.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=GC.DOD.TOTL.GD.ZS.</li>
</ul>
|citation = World Bank. 2026. "Central government debt, total (% of GDP)." ''World Bank Open Data''. https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "Central government debt, total (% of GDP)". ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=GC.DOD.TOTL.GD.ZS.</li>
</ul>
}}

Economic and financial factors

2026-07-30T15:48:33Z

Carmen Cordes:

{|class="wikitable sortable" border="1"
|-
! Indicator name*
! Subcategory I
! Subcategory II
! Subcategory III
! Technical name*
! Scale*
! Short description
! Data origin* 
|-
| [[GDP per capita in 1990 USD, Maddison Data Project]]
| Economics
|
|
| econnat_economics_gdppercap_1990usd_mdp
| Metric
| GDP per Capita in 1990 US-Dollar, Maddison Project Database, version 2018
| 3rd Party Data
|-
| [[GDP in current USD (WB)]]
| Economics
|
|
| econnat_economics_gdp_cusd_wb
| Metric
| GDP in current US-Dollar, World Bank national accounts data
| 3rd Party Data
|-
| [[Agricultural employment in percent of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_agricempl_perctot_ilo
| Metric
| Employment in the agricultural sector in percent of total employment
| 3rd Party Data
|-
| [[Central government debt in percent of GDP, IMF]]
| Finance
|
|
| econnat_economics_cgovdebt_pergdp_imf
| Metric
| Central government debt in percent of GDP
| 3rd Party Data
|-
| [[Employment in the industrial sector as percentage of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_industryempl_perctotal_ilo
| Metric
| Employment in the industrial sector (mining and quarrying, manufacturing, construction, and public utilities) as a percentage of total employment based on ILO estimates
| 3rd Party Data
|-
| [[GDP in current NCU, WB]]
| Economics
|
|
| econnat_economics_gdp_cncu_wb
| Metric
| Nominal GDP in current national currency units
| 3rd Party Data
|-
| [[Annual real GDP growth in percent, WB]]
| Economics
|
|
| econnat_economics_gdp_percgrowth_wb
| Metric
| Annual real GDP growth in percent
| 3rd Party Data
|-
| [[Real GDP in 2010 USD, WB]]
| Economics
|
|
| econnat_economics_gdp_real2010usd_wb
| Metric
| Real GDP in 2010 US-Dollars
| 3rd Party Data
|-
| [[Real GDP in NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdp_realncu_wb
| Metric
| Real GDP in national currency units. Base year for constant national currency unit varies by country
| 3rd Party Data
|-
| [[Annual inflation in percent using GDP deflators, WB]]
| Economics
|
|
| econnat_economics_gdpdefl_percgrowth_wb
| Metric
| Annual inflation in percent using GDP deflators
| 3rd Party Data
|-
| [[GDP deflator index, World Bank]]
| Economics
|
|
| econnat_economics_gdpdefl_indexvby_wb
| Metric
| GDP deflator index with varying base year by country
| 3rd Party Data
|-
| [[GDP per capita in current PPP-$, WB]]
| Economics
|
|
| econnat_economics_gdppercap_cpppd_wb
| Metric
| GDP per capita in current purchasing power parity international Dollars
| 3rd Party Data
|-
| [[GDP per capita in current NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_cncu_wb
| Metric
| GDP per capita in current national currency units
| 3rd Party Data
|-
| [[GDP per capita in current USD, WB]]
| Economics
|
|
| econnat_economics_gdppercap_cusd_wb
| Metric
| GDP per capita in current US-Dollars.
| 3rd Party Data
|-
| [[Annual real GDP growth per capita, WB]]
| Economics
|
|
| econnat_economics_gdppercap_percgrowth_wb
| Metric
| Annual real GDP growth per capita
| 3rd Party Data
|-
| [[Real GDP per capita in 2017 PPP-$, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_real2017pppd_wb
| Metric
| Real GDP per capita in purchasing power parity international Dollars. Base year: 2017
| 3rd Party Data
|-
| [[Real GDP per capita in constant 2010 US-$, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_real2010usd_wb
| Metric
| Real GDP per capita in constant US-Dollars. Base year: 2010
| 3rd Party Data
|-
| [[Real GDP per capita in constant NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_realncu_wb
| Metric
| Real GDP per capita in constant national currency units. Base year varies by country
| 3rd Party Data
|-
| [[Informal employment in percent of total non-agricultural employment, ILO]]
| Labour Market
|
|
| econnat_economics_tinformalempl_percnaempl_ilo
| Metric
| Informal employment in percent of total non-agricultural employment
| 3rd Party Data
|-
| [[Service employment in percent of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_servempl_perctot_ilo
| Metric
| Employment in the service sector in percent of total employment
| 3rd Party Data
|-
| [[Value added in the agricultural sector as a percentage of GDP, World Bank]]
| Economics
|
|
| econnat_economics_agricva_pergdp_wb
| Metric
| Value added in the agricultural sector as a percentage of GDP
| 3rd Party Data
|-
| [[Value added in the industrial sector as a percentage of GDP, World Bank]]
| Economics
|
|
| econnat_economics_industryva_pergdp_wb
| Metric
| Value added in the industrial sector (mining, manufacturing, construction, electricity, water and gas) as a percentage of GDP
| 3rd Party Data
|-
| [[Value added in the manufacturing sector in percent of GDP, World Bank]]
| Economics
|
|
| econnat_economics_manufava_pergdp_wb
| Metric
| Value added in the manufacturing sector in percent of GDP
| 3rd Party Data
|-
| [[Value added in the service sector in percent of GDP, World Bank]]
| Economics
|
|
| econnat_economics_servva_pergdp_wb
| Metric
| Value added in the service sector in percent of GDP.
| 3rd Party Data
|-
| [[Public Social Spending as a % of GDP, OECD]]
| Finance
| Social Spending
|
| socx_public_oecd
| Metric
| Total public spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[Private Social Spending as a % of GDP, OECD]]
| Finance
| Social Spending
|
| socx_private_oecd
| Metric
| Total voluntary and mandatory private spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[Public Social Spending as a % of GDP, ILO]]
| Finance
| Social Spending
|
| socx_public_ilo
| Metric
| Total public spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[GDP per Capita in 2011 US-$, Maddison Data Project]]
| Economics
|
|
| econnat_economics_gdppercap_2011usd_mdp
| Metric
| GDP per Capita in 2011 US-Dollar, Maddison Project Database
| 3rd Party Data
|}

GDP in current NCU, WB

2026-07-30T15:48:06Z

Carmen Cordes: Carmen Cordes moved page GDP in current NCU, World Bank to GDP in current NCU, WB: WeSIS Polishing

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cncu_wb
|category= Economic and financial factors
|label = GDP in current NCU
|description= This indicator captures nominal GDP in current national currency units.
|relatedindicators =
<ul>
<li>[[GDP per capita in current PPP-$, WB]]</li>
<li>[[GDP per capita in current USD, WB]]</li>
</ul>
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
|citation = World Bank. 2026. GDP (current LCU)". ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CN.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "GDP (current LCU)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
}}

GDP in current NCU, World Bank

2026-07-30T15:48:06Z

Carmen Cordes: Carmen Cordes moved page GDP in current NCU, World Bank to GDP in current NCU, WB: WeSIS Polishing

#REDIRECT [[GDP in current NCU, WB]]

GDP in current NCU, WB

2026-07-30T15:47:53Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cncu_wb
|category= Economic and financial factors
|label = GDP in current NCU
|description= This indicator captures nominal GDP in current national currency units.
|relatedindicators =
<ul>
<li>[[GDP per capita in current PPP-$, WB]]</li>
<li>[[GDP per capita in current USD, WB]]</li>
</ul>
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
|citation = World Bank. 2026. GDP (current LCU)". ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CN.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "GDP (current LCU)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CN.</li>
</ul>
}}

GDP per capita in current PPP-$, WB

2026-07-30T15:25:28Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_cpppd_wb
|category= Economic and financial factors
|label = GDP per capita in current PPP-$
|relatedindicators =
<ul>
<li>[[GDP per capita in current NCU, World Bank]]</li>
<li>[[GDP per capita in current USD, WB]]</li>
</ul>
|description = This indicator captures the Gross Domestic Product (GDP) per capita in current purchasing power parity international dollars.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.PP.CD.</li>
</ul>
|citation = World Bank. 2026. "GDP per capita, PPP (current international $)." ''World Bank Data''. https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources = <ul>
<li>World Bank. 2026. "GDP per capita, PPP (current international $)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.PP.CD.</li>
</ul>
}}

GDP per capita in current USD, WB

2026-07-30T15:20:07Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_cusd_wb
|category= Economic and financial factors
|label = GDP per capita in current USD
|relatedindicators =
<ul>
<li>[[GDP per capita in current NCU, World Bank]]</li>
<li>[[GDP per capita in current PPP-$, World Bank]]</li>
</ul>
|description = This indicator captures the Gross Domestic Product (GDP) per capita in current USD.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.CD.</li>
</ul>
|citation = World Bank. 2026. "GDP per capita (current US$)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on World Bank data published 2025.04.15</li></ul>
|revisions = Update based on World Bank data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP per capita (current US$)." ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.PCAP.CD.</li>
</ul>
}}

GDP in current USD (WB)

2026-07-30T15:11:20Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cusd_wb
|category= Economic and financial factors
|label = Current GDP in USD (WB)
|relatedindicators =
|description = This indicator captures the Gross Domestic Product (GDP) in current USD.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CD.</li>
</ul>
|citation = World Bank. 2026. "GDP (current US$)." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "GDP (current US$)". ''DataBank. World Development Indicators''. https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CD.
</ul>
}}

GDP in current USD (WB)

2026-07-30T14:18:56Z

Carmen Cordes:

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdp_cusd_wb
|category= Economic and financial factors
|label = Current GDP in USD (WB)
|relatedindicators = tbd
|description = This indicator captures the Gross Domestic Product (GDP) in current USD.
|codingrules =
<ul>
<li>For detailed information and coding rules see https://databank.worldbank.org/reports.aspx?source=2&type=metadata&series=NY.GDP.MKTP.CD.</li>
</ul>
|citation = World Bank. 2026. "GDP (current US$)". ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease =
<ul><li>Version 0.001: Initial release</li>
<li>Version 0.002: Update based on WB data published 2025.04.15</li></ul>
|revisions = Update based on WB data published 2025.04.15
|sources =
<ul>
<li>World Bank. 2026. "World Development Indicators." ''World Bank Open Data''. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD.</li>
</ul>
}}

Economic and financial factors

2026-07-30T11:11:03Z

Carmen Cordes:

{|class="wikitable sortable" border="1"
|-
! Indicator name*
! Subcategory I
! Subcategory II
! Subcategory III
! Technical name*
! Scale*
! Short description
! Data origin* 
|-
| [[GDP per capita in 1990 USD, Maddison Data Project]]
| Economics
|
|
| econnat_economics_gdppercap_1990usd_mdp
| Metric
| GDP per Capita in 1990 US-Dollar, Maddison Project Database, version 2018
| 3rd Party Data
|-
| [[GDP in current USD (WB)]]
| Economics
|
|
| econnat_economics_gdp_cusd_wb
| Metric
| GDP in current US-Dollar, World Bank national accounts data
| 3rd Party Data
|-
| [[Agricultural employment in percent of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_agricempl_perctot_ilo
| Metric
| Employment in the agricultural sector in percent of total employment
| 3rd Party Data
|-
| [[Central government debt in percent of GDP, IMF]]
| Finance
|
|
| econnat_economics_cgovdebt_pergdp_imf
| Metric
| Central government debt in percent of GDP
| 3rd Party Data
|-
| [[Employment in the industrial sector as percentage of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_industryempl_perctotal_ilo
| Metric
| Employment in the industrial sector (mining and quarrying, manufacturing, construction, and public utilities) as a percentage of total employment based on ILO estimates
| 3rd Party Data
|-
| [[GDP in current NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdp_cncu_wb
| Metric
| Nominal GDP in current national currency units
| 3rd Party Data
|-
| [[Annual real GDP growth in percent, WB]]
| Economics
|
|
| econnat_economics_gdp_percgrowth_wb
| Metric
| Annual real GDP growth in percent
| 3rd Party Data
|-
| [[Real GDP in 2010 USD, WB]]
| Economics
|
|
| econnat_economics_gdp_real2010usd_wb
| Metric
| Real GDP in 2010 US-Dollars
| 3rd Party Data
|-
| [[Real GDP in NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdp_realncu_wb
| Metric
| Real GDP in national currency units. Base year for constant national currency unit varies by country
| 3rd Party Data
|-
| [[Annual inflation in percent using GDP deflators, WB]]
| Economics
|
|
| econnat_economics_gdpdefl_percgrowth_wb
| Metric
| Annual inflation in percent using GDP deflators
| 3rd Party Data
|-
| [[GDP deflator index, World Bank]]
| Economics
|
|
| econnat_economics_gdpdefl_indexvby_wb
| Metric
| GDP deflator index with varying base year by country
| 3rd Party Data
|-
| [[GDP per capita in current PPP-$, WB]]
| Economics
|
|
| econnat_economics_gdppercap_cpppd_wb
| Metric
| GDP per capita in current purchasing power parity international Dollars
| 3rd Party Data
|-
| [[GDP per capita in current NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_cncu_wb
| Metric
| GDP per capita in current national currency units
| 3rd Party Data
|-
| [[GDP per capita in current USD, WB]]
| Economics
|
|
| econnat_economics_gdppercap_cusd_wb
| Metric
| GDP per capita in current US-Dollars.
| 3rd Party Data
|-
| [[Annual real GDP growth per capita, WB]]
| Economics
|
|
| econnat_economics_gdppercap_percgrowth_wb
| Metric
| Annual real GDP growth per capita
| 3rd Party Data
|-
| [[Real GDP per capita in 2017 PPP-$, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_real2017pppd_wb
| Metric
| Real GDP per capita in purchasing power parity international Dollars. Base year: 2017
| 3rd Party Data
|-
| [[Real GDP per capita in constant 2010 US-$, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_real2010usd_wb
| Metric
| Real GDP per capita in constant US-Dollars. Base year: 2010
| 3rd Party Data
|-
| [[Real GDP per capita in constant NCU, World Bank]]
| Economics
|
|
| econnat_economics_gdppercap_realncu_wb
| Metric
| Real GDP per capita in constant national currency units. Base year varies by country
| 3rd Party Data
|-
| [[Informal employment in percent of total non-agricultural employment, ILO]]
| Labour Market
|
|
| econnat_economics_tinformalempl_percnaempl_ilo
| Metric
| Informal employment in percent of total non-agricultural employment
| 3rd Party Data
|-
| [[Service employment in percent of total employment, ILO]]
| Labour Market
|
|
| econnat_economics_servempl_perctot_ilo
| Metric
| Employment in the service sector in percent of total employment
| 3rd Party Data
|-
| [[Value added in the agricultural sector as a percentage of GDP, World Bank]]
| Economics
|
|
| econnat_economics_agricva_pergdp_wb
| Metric
| Value added in the agricultural sector as a percentage of GDP
| 3rd Party Data
|-
| [[Value added in the industrial sector as a percentage of GDP, World Bank]]
| Economics
|
|
| econnat_economics_industryva_pergdp_wb
| Metric
| Value added in the industrial sector (mining, manufacturing, construction, electricity, water and gas) as a percentage of GDP
| 3rd Party Data
|-
| [[Value added in the manufacturing sector in percent of GDP, World Bank]]
| Economics
|
|
| econnat_economics_manufava_pergdp_wb
| Metric
| Value added in the manufacturing sector in percent of GDP
| 3rd Party Data
|-
| [[Value added in the service sector in percent of GDP, World Bank]]
| Economics
|
|
| econnat_economics_servva_pergdp_wb
| Metric
| Value added in the service sector in percent of GDP.
| 3rd Party Data
|-
| [[Public Social Spending as a % of GDP, OECD]]
| Finance
| Social Spending
|
| socx_public_oecd
| Metric
| Total public spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[Private Social Spending as a % of GDP, OECD]]
| Finance
| Social Spending
|
| socx_private_oecd
| Metric
| Total voluntary and mandatory private spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[Public Social Spending as a % of GDP, ILO]]
| Finance
| Social Spending
|
| socx_public_ilo
| Metric
| Total public spending on social protection and assistance as a percentage of GDP
| 3rd Party Data
|-
| [[GDP per Capita in 2011 US-$, Maddison Data Project]]
| Economics
|
|
| econnat_economics_gdppercap_2011usd_mdp
| Metric
| GDP per Capita in 2011 US-Dollar, Maddison Project Database
| 3rd Party Data
|}

GDP per capita in 1990 USD, Maddison Data Project

2026-07-30T11:10:50Z

Carmen Cordes: Carmen Cordes moved page GDP per Capita in 1990 USD, Maddison Data Project to GDP per capita in 1990 USD, Maddison Data Project: WeSIS Polishing

{{IndicatorForm
|datatype = Numeric
|scale = Metric
|valuelabels = Not applicable
|techname = econnat_economics_gdppercap_1990usd_mdp
|category= Economic and financial factors
|label = Real GDP per capita (1990 USD)
|relatedindicators =
|description = This indicator captures real Gross Domestic Product (GDP) per capita in 1990 USD.
|codingrules =
<ul>
<li>Real GDP per capita in 1990 dollars, using the most current time series on growth of GDP per capita and the Maddison benchmark for 1990.</li>
<li>For further information see https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018.
</ul>
|citation = Bolt, Jutta, Robert Inklaar, Herman de Jong, and Jan Luiten van Zanden. 2018. ''Rebasing 'Maddison': new income comparisons and the shape of long-run economic development''. Groningen: Groningen Growth and Development Center. Maddison Project Working Paper, 10/2018. https://www.rug.nl/ggdc/html_publications/memorandum/gd174.pdf.
|relatedpublications =
|projectmanagers = Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Groningen Growth and Development Centre. Faculty of Economics and Business. 2018. ''Maddison Project Database''. https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018.
}}

GDP per Capita in 1990 USD, Maddison Data Project

2026-07-30T11:10:50Z

Carmen Cordes: Carmen Cordes moved page GDP per Capita in 1990 USD, Maddison Data Project to GDP per capita in 1990 USD, Maddison Data Project: WeSIS Polishing

#REDIRECT [[GDP per capita in 1990 USD, Maddison Data Project]]

GDP per capita in 1990 USD, Maddison Data Project

2026-07-30T11:09:03Z

Carmen Cordes:

Human Development Index

2026-07-30T10:32:50Z

Carmen Cordes:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=not applicable
|techname=socstr_hdi_undp
|category=Social structure
|label=Human Development Index
|relatedindicators=<ul>
<li>[[The Property Right Protection Index]]</li>
<li>[[Relative poverty rates: Entire population]]</li>
<li>[[Relative poverty rates: Children (age 0-17)]]</li>
<li>[[Relative poverty rates: Working-age population (age 18-65)]]</li>
<li>[[Relative poverty rates: Retirement-age population (over 65)]]</li>
<li>[[Poverty gap entire population]]</li>
<li>[[Poverty gap at dollar 2.15 a day (2017 PPP) (%)]]</li>
<li>[[Poverty gap at dollar 3.65 a day (2017 PPP) (%)]]</li>
<li>[[Poverty gap at dollar 1.90 a day (2011 PPP) (%)]]</li>
<li>[[Poverty gap at dollar 3.20 a day (2011 PPP) (%)]]</li>
</ul>
|description=The Human Development Index (HDI) is a composite indicator that reflects average performance in three crucial aspects of human development: living a long and healthy life, acquiring knowledge, and enjoying a reasonable standard of living. The HDI is calculated as the geometric mean of normalized indices for each of these three dimensions. It ranges from 0 to 1. The closer the score is to 1, the better the country is performing.
|codingrules=
<ul>
<li>The HDI is derived from four key indicators: life expectancy at birth (in years), expected years of schooling (in years), mean years of schooling (in years), and GNI per capita (in 2017 PPP$). The calculation involves two main steps. First, the minimum and maximum values (goalposts) are established for each indicator to convert them into indices ranging from 0 to 1. These goalposts represent "natural zeros" and "aspirational targets," which help standardize the component indicators. After defining the minimum and maximum values, the dimension index is calculated using the formula: 
Dimension index = (actual value - minimum value) / (maximum value - minimum value). </li>
<li>For the education dimension, this formula is applied to each of the two education indicators, and the arithmetic mean of the resulting indices is taken. Since each dimension index reflects capabilities in the corresponding area, the transformation from income to capabilities is assumed to be concave (Anand and Sen, 2000), meaning that each additional dollar of income has a diminishing impact on expanding capabilities. As a result, for income, the natural logarithm of the actual, minimum, and maximum values is used. </li>
<li>The second step is to aggregate the dimensions: the HDI is the geometric mean of the three dimension indices, calculated as: 
HDI = (IHealth × IEducation × IIncome)^(1/3). </li>

<li>For more information, see "Human Develoment Report 2023/2024 technical notes" (https://hdr.undp.org/sites/default/files/2023-24_HDR/hdr2023-24_technical_notes.pdf) and "Human Development Index (HDI)" (https://hdr.undp.org/data-center/human-development-index#/indicies/HDI) (Accessed August 14, 2024). </li>

<li>Teorell et al. (2024: 922pp) define the variable as "The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate
about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The closer the score is to 1, the better the country is doing. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details. The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO offers the other composite indices as broader proxy on some of the key issues of human development, inequality, gender disparity
and human poverty."</li>
|citation=<ul>
<li> Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, Cam Mert Dalli, Rafael Lopez Valverde, and Paula Nilsson. 2024. ''The Quality of Government Standard Dataset [Dataset]''. https://www.gu.se/en/quality-government/qog-data/data-downloads/standard-dataset.</li>
<li>United Nations Development Program. 2022. ''Human Development Report 2021/2022. Uncertain times, unsettled lives: Shaping our future in a transforming world''. New York: United Nations Development Program. https://hdr.undp.org/content/human-development-report-2021-22.</li>
</ul>
|relatedpublications=<ul>
<li> United Nations Development Programme. 2024. ''Human Development Report 2023-24: Breaking the gridlock: Reimagining cooperation in a polarized world''. New York: United Nations Development Program. https://hdr.undp.org/content/human-development-report-2023-24.</li>
</ul>
|projectmanagers=Responsible for data editing, description (WeSIS) and entry: Andrea Schäfer (2021-2025, Version 0.002), Jean-Yves Gerlitz (2018-2020; Version 0.001), Principal Investigator: Irene Dingeldey, Ulrich Mückenberger, Student assistants: Karolin Meyer (2018-2020), Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease=<ul>
<li>Version 0.001: Initial release with data from The Quality of Government Standard Dataset, version January 2019</li>
<li>Version 0.002: Updated with data from The Quality of Government Standard Dataset, version January 2024</li>
</ul>
|revisions=No revisions yet
|sources=<ul>
<li>Teorell, Jan, Stefan Dahlberg, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, and Richard Svensson. 2019. ''The Quality of Government Standard Dataset [Dataset]''. http://www.qog.pol.gu.se. https://doi.org/10.18157/qogstdjan19.</li>
<li> Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, Cam Mert Dalli, Rafael Lopez Valverde, and Paula Nilsson. 2024. ''The Quality of Government Standard Dataset [Dataset]''. https://www.gu.se/en/quality-government/qog-data/data-downloads/standard-dataset.</li>
<li>United Nations Development Program. 2022. ''Human Development Report 2021/2022. Uncertain times, unsettled lives: Shaping our future in a transforming world''. New York: United Nations Development Program. https://hdr.undp.org/content/human-development-report-2021-22.</li>
</ul>
}}

Social structure

2026-07-28T19:48:01Z

Carmen Cordes:

{|class="wikitable sortable" border="1"
|-
! Indicator Name
! Subcategory I
! Subcategory II
! Subcategory III
! Technical name
! Scale
! Short description
! Data origin*
|-
| [[Employment to population ratio, 15+, male (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_15m
| Metric
| Measures the employment ratio of a country’s male population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Employment to population ratio, 15+, female (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_15f
| Metric
| Measures the employment ratio of a country’s female population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Employment to population ratio, 15+, total (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_15t
| Metric
| Measures the employment ratio of a country’s population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Employment to population ratio, 15-24, male (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_1524m
| Metric
| Measures the employment ratio of a country’s male population aged 15 to 24 (WBG).
| 3rd Party Data
|-
| [[Employment to population ratio, 15-24, female (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_1524f
| Metric
| Measures the employment ratio of a country’s female population aged 15 to 24 (WBG).
| 3rd Party Data
|-
| [[Employment to population ratio, 15-24, total (national estimate)]]
| Labour market participation
| Employment
|
| socstr_emp_pop_1524t
| Metric
| Measures the employment ratio of a country’s population aged 15 to 24 (WBG).
| 3rd Party Data
|-
| [[Labor force participation rate, 15+, male (national estimate)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_pop_15m
| Metric
| Measures the economically active proportion of a country's male population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Labor force participation rate, 15+, female (national estimate)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_pop_15f
| Metric
| Measures the economically active proportion of a country's female population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Labor force participation rate, 15+, total (national estimate)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_pop_15t
| Metric
| Measures the economically active proportion of a country's population aged 15 or older (WBG).
| 3rd Party Data
|-
| [[Labor force with advanced education/total working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu3_wpop_t
| Metric
| Measures the economically active proportion of a country's population aged 15 or older and gained an advanced level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with advanced education/female working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu3_wpop_f
| Metric
| Measures the economically active proportion of a country's female population aged 15 or older and gained an advanced level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with advanced education/male working-age population)]]
| Labour market participation
| Education
|
| socstr_lfpedu3_wpop_m
| Metric
| Measures the economically active proportion of a country's male population aged 15 or older and gained an advanced level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with intermediate education/total working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu2_wpop_t
| Metric
| Measures the economically active proportion of a country's population aged 15 or older and gained an intermediate level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with intermediate education/female working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu2_wpop_f
| Metric
| Measures the economically active proportion of a country's female population aged 15 or older and gained an intermediate level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with intermediate education/male working-age population)]]
| Labour market participation
| Education
|
| socstr_lfpedu2_wpop_15m
| Metric
| Measures the economically active proportion of a country's male population aged 15 or older and gained an intermediate level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with basic education/total working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu1_wpop_t
| Metric
| Measures the economically active proportion of a country's population aged 15 or older and gained a basic level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with basic education/female working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu1_wpop_f
| Metric
| Measures the economically active proportion of a country's female population aged 15 or older and gained a basic level of education (WBG).
| 3rd Party Data
|-
| [[Labor force with basic education/male working-age population]]
| Labour market participation
| Education
|
| socstr_lfpedu1_wpop_15m
| Metric
| Measures the economically active proportion of a country's male population aged 15 or older and gained a basic level of education (WBG).
| 3rd Party Data
|-
| [[Female labour force participation rate (15+) (compiled)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_flfp_15+_comp
| Metric
| Measures the proportion of women economically active (i.e., those employed or employment-seeking) relative to the reference population of women aged 15+ in a given country.
| CRC Compiled Data
|-
| [[Male labour force participation rate (15+) (compiled)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_mlfp_15+_comp
| Metric
| Measures the proportion of men economically active (i.e., those employed or employment-seeking) relative to the reference population of men aged 15+ in a given country.
| CRC Compiled Data
|-
| [[Female to male labour force participation ratio (15+) (compiled)]]
| Labour market participation
| Labour force participation
|
| socstr_lfp_ftmlfpr_15+_comp
| Metric
| Measures the relative difference in female and male labour force participation of a given country.
| CRC Compiled Data
|-
| [[Atkinson Coefficient (epsilon=0.5)]]
| Economic inequality
| Inequality measure
|
| socstr_atkincoef_eps05
| Metric
| Measures income inequality of a country under considerations of further parameters like society's aversion of income inequality (LIS).
| 3rd Party Data
|-
| [[Atkinson Coefficient (epsilon=1)]]
| Economic inequality
| Inequality measure
|
| socstr_atkincoef_eps1
| Metric
| Measures income inequality of a country under considerations of further parameters like society's aversion of income inequality (LIS).
| 3rd Party Data
|-
| [[Gini Coefficient (LIS)]]
| Economic inequality
| Inequality measure
|
| socstr_gini_coef_lis
| Metric
| Measures income inequality of a country's population and ranges between absolute income inequality and equal distribution of income. This Gini Coeffizient is based on calculation from the Luxembourg Income Study Database (LIS).
| 3rd Party Data
|-
| [[Gini (at disposable income post taxes & transfers)]]
| Economic inequality
| Inequality measure
|
| socstr_gini_disinc_oecd
| Metric
| Measures income inequality of disposable income of a country's population after taxes and other transfers. Ranges between absolute income inequality and equal distribution of income. This Gini Coeffizient is based on calculation from the Organisation for Economic Co-operation and Development (OECD).
| 3rd Party Data
|-
| [[Income inequality: S80/S20 disposable income quintile share]]
| Economic inequality
| Inequality measure
|
| socstr_s80s20_disinc_oecd
| Metric
| Measures income inequality as ratio of the disposable income decile S80/S20 (OECD).
| 3rd Party Data
|-
| [[Income inequality: P90/P10 disposable income decile ratio]]
| Economic inequality
| Inequality measure
|
| socstr_p90p10_disinc_oecd
| Metric
| Measures income inequality as ratio of the disposable income decile P90/P10 (OECD).
| 3rd Party Data
|-
| [[Income inequality: P90/P50 disposable income decile ratio]]
| Economic inequality
| Inequality measure
|
| socstr_p90p50_disinc_oecd
| Metric
| Measures income inequality as ratio of the disposable income decile P90/P50 (OECD).
| 3rd Party Data
|-
| [[Income inequality: P50/P10 disposable income decile ratio]]
| Economic inequality
| Inequality measure
|
| socstr_p50p10_disinc_oecd
| Metric
| Measures income inequality as ratio of the disposable income decile P50/P10 (OECD).
| 3rd Party Data
|-
| [[GINI index (WB estimate)]]
| Economic inequality
| Inequality measure
|
| socstr_gini_ndx_wb
| Metric
| Measures income inequality of a country's population and ranges between absolute income inequality and equal distribution of income. This Gini Coeffizient is based on calculation from the World Bank (WB).
| 3rd Party Data
|-
| [[Gini coefficient (WIID)]]
| Economic inequality
| Inequality measure
|
| socstr_gini_coef_wiid4
| Metric
| Measures income inequality of a country's population and ranges between absolute income inequality and equal distribution of income. This Gini Coeffizient is based on calculation from the World Institute for Development Economics Research (UNU-WIDER / WIID).
| 3rd Party Data
|-
| [[Net Income Gini Prediction]]
| Economic inequality
| Inequality measure
|
| socstr_gini_net_income_pred
| Metric
| Measures income inequality of a country's population and ranges between absolute income inequality and equal distribution of income. This Gini Coeffizient is based on a prediction of net income Gini data from a GBM-model trained on WIID Gini data and about 1400 Indicators from WeSIS.
| CRC Compiled Data
|-
| [[Human Development Index]]
| Human Development
| Development indicator
|
| socstr_hdi_undp
| Metric
| Measures average achievement of a country in various dimensions of human development like health, education and living standard (UNDP).
| 3rd Party Data
|-
| [[Relative poverty rates: entire population]]
| Economic inequality
| Poverty measure
|
| socstr_repovratetpop_oecd
| Metric
| Measures relative poverty of the entire population of a country (OECD).
| 3rd Party Data
|-
| [[Relative poverty rates: Children (age 0-17)]]
| Economic inequality
| Poverty measure
|
| socstr_repovratechild_oecd
| Metric
| Measures relative poverty of children aged 0 up to 17 of a country (OECD).
| 3rd Party Data
|-
| [[Relative poverty rates: Working-age population (age 18-65)]]
| Economic inequality
| Poverty measure
|
| socstr_repovratewap_oecd
| Metric
| Measures relative poverty of the working age population aged 18 up to 65 of a country (OECD).
| 3rd Party Data
|-
| [[Relative poverty rates: Retirement-age population (over 65)]]
| Economic inequality
| Poverty measure
|
| socstr_repovratera_oecd
| Metric
| Measures relative poverty of the retirement-age population aged 65 and older of a country (OECD).
| 3rd Party Data
|-
| [[Poverty gap entire population]]
| Economic inequality
| Poverty measure
|
| socstr_povgaptpop_oecd
| Metric
| Measures the poverty gap of a country's entire population (OECD).
| 3rd Party Data
|-
| [[Poverty gap at dollar 1.90 a day (2011 PPP) (%)]]
| Economic inequality
| Poverty measure
|
| socstr_povgap190_11_wb
| Metric
| Measures poverty with a poverty gap set at 1.90 dollars a day which reflects the depth of poverty and its incidence (WB).
| 3rd Party Data
|-
| [[Poverty gap at dollar 2.15 a day (2017 PPP) (%)]]
| Economic inequality
| Poverty measure
|
| socstr_povgap190_11_wb_new
| Metric
| Measures poverty with a poverty gap set at 2.15 dollars a day which reflects the depth of poverty and its incidence (WB).
| 3rd Party Data
|-
| [[Poverty gap at dollar 3.20 a day (2011 PPP) (%)]]
| Economic inequality
| Poverty measure
|
| socstr_povgap320_11_wb
| Metric
| Measures poverty with a poverty gap set at 3.20 dollars a day which reflects the depth of poverty and its incidence (WB).
| 3rd Party Data
|-
| [[Poverty gap at dollar 3.65 a day (2017 PPP) (%)]]
| Economic inequality
| Poverty measure
|
| socstr_povgap320_11_wb_new
| Metric
| Measures poverty with a poverty gap set at 3.65 dollars a day which reflects the depth of poverty and its incidence (WB).
| 3rd Party Data
|-
| [[Gender inequality index]]
| Social inequality
| Inequality measure
|
| socstr_gdi_undp
| Metric
| Measures inequality in gender based on the three dimensions reproductive health, empowerment, and labour market (UNDP).
| 3rd Party Data
|-
| [[Population, total]]
| Population
|
|
| socstr_pop_tot_unctad
| Metric
| Measures the total population at 1st January (UNCTAD).
| 3rd Party Data
|-
| [[Total male population]]
| Population
|
|
| socstr_pop_totma_pp
| Metric
| Measures the male population at 1st January (UNCTAD).
| 3rd Party Data
|-
| [[Total female population]]
| Population
|
|
| socstr_pop_totfe_pp
| Metric
| Measures the female population at 1st January (UNCTAD).
| 3rd Party Data
|-
| [[Total population, both sexes]]
| Population
|
|
| socstr_pop_tot_pp
| Metric
| Measures the total population (all residents regardless of legal status or citizenship), midyear estimates (WB).
| 3rd Party Data
|-
| [[Population per square kilometre]]
| Population
|
|
| socstr_pop_dens_pp
| Metric
| Measures the average population per square km (EC).
| 3rd Party Data
|-
| [[Total population ages 0-14]]
| Population
|
|
| socstr_pop_0014to_pp
| Metric
| Measures the total population between the ages 0 to 14 as a percentage of the total population (WB).
| 3rd Party Data
|-
| [[Total population ages 15-64]]
| Population
|
|
| socstr_pop_1564to_pp
| Metric
| Measures the total population between the ages 15 to 64 as a percentage of the total population (WB).
| 3rd Party Data
|-
| [[Total population ages 65 and above]]
| Population
|
|
| socstr_pop_65upto_pp
| Metric
| Measures the population ages 65 and above as a percentage of the total population (WB).
| 3rd Party Data
|-
| [[Total fertility rate]]
| Population
|
|
| socstr_pop_tfr_gp
| Metric
| Measures the total number of children born to women aged 15 to 49 (OECD).
| 3rd Party Data
|-
| [[Life expectancy at birth]]
| Population
|
|
| socstr_pop_lex_gp
| Metric
| Measures the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life (WB).
| 3rd Party Data
|-
| [[Global Gender Gap Index]]
| Social inequality
| Inequality measure
|
| socstr_si_im_gggi
| Metric
| The index benchmarks national gender gaps on economic, education, health and political criteria (WEF).
| 3rd Party Data
|-
| [[Children in employment, total ]]
| Labour market participation
| Employment
|
| socstr_lm_ep_cept
| Metric
| Measures the share of children involved in economic activity aged 7 – 14 and not attending school (WB).
| 3rd Party Data
|-
| [[Children in employment, female ]]
| Labour market participation
| Employment
|
| socstr_lm_ep_cepf
| Metric
| Measures the share of female children involved in economic activity aged 7 – 14 and not attending school (WB).
| 3rd Party Data
|-
| [[Children in employment, male ]]
| Labour market participation
| Employment
|
| socstr_lm_ep_cepm
| Metric
| Measures the share of male children involved in economic activity aged 7 – 14 and not attending school (WB).
| 3rd Party Data
|-
| [[The Property Right Protection Index]]
| Economic inequality
| Inequality measure
|
| socstr_prp_prp
| Metric
| The Property Rights Index assesses the security of property rights distinct from other components of the rule of law.
| 3rd Party Data
|-
| [[Global Gender Gap Economic Participation and Opportunity Subindex]]
| Social inequality
| Inequality measure
|
| socstr_si_im_gggi_pos
| Metric
| Measures the gendered economic participation and opportunity (WEF).
| 3rd Party Data
|-
| [[Global Gender Gap Political Empowerment Subindex]]
| Social inequality
| Inequality measure
|
| socstr_si_im_gggi_pes
| Metric
| Measures the gendered political empowerment (WEF).
| 3rd Party Data
|-
| [[Global Gender Gap Health and Survival Subindex]]
| Social inequality
| Inequality measure
|
| socstr_si_im_gggi_hss
| Metric
| Measures the gendered health and survival (WEF).
| 3rd Party Data
|}

Gender inequality index

2026-07-28T19:47:18Z

Carmen Cordes: Carmen Cordes moved page Gender Inequality Index to Gender inequality index: WeSIS Polishing

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|valuelabels=<ul><li>0 = both genders are fare equally </li></ul>
<ul><li>1 = one gender fares as porrly as possible in all dimensions </li></ul>
The Gender Inequality Index ranges in between these two values.
|techname=socstr_gdi_undp
|category=Social structure
|label=Gender inequality index
|relatedindicators=<ul><li>[[Human Development Index]]</li></ul>
<ul><li>[[Global Gender Gap Index]]</li></ul>
<ul><li>[[Gender Wage Gap]]</li></ul>
|description=This indicator reflects inequality based on the gender in three dimensions: reproductive health, empowerment and labour market. The Gender Inequality Index is built similiar to the Humand Development Index but shows differences between women and men better.
|codingrules=
<ul>
<li>Reproductive health is measured by maternal mortality rates and adolescent birth rates; empowerment by the proportion of adult females and males aged 25+ with some secondary education and proportion of parliamentary seats occupied of women and men; labour market by the labour force participation rate of both populations aged 15+. </li>
</ul>
|citation=United Nations Development Program. 2018. Gender inequality index. http://hdr.undp.org/en/content/gender-inequality-index-gii (last accessed 28.07.2026).
|relatedpublications=
|projectmanagers=Jean-Yves Gerlitz, Karolin Meyer, Nils Düpont, Carmen Cordes, Marius Bode (INF)
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=<ul>
<li>Teorell, Jan, Stefan Dahlberg, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, and Richard Svensson. 2019. ''The Quality of Government Standard Dataset 2019 [Dataset]''. http://www.qog.pol.gu.se. https://doi.org/10.18157/qogstdjan19.</li>
</ul>
}}

Gender Inequality Index

2026-07-28T19:47:18Z

Carmen Cordes: Carmen Cordes moved page Gender Inequality Index to Gender inequality index: WeSIS Polishing

#REDIRECT [[Gender inequality index]]

Gender inequality index

2026-07-28T19:43:47Z

Carmen Cordes: