WeSISpedia - User contributions [en]

Shortly Colonized by Denmark

2021-11-17T15:03:17Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedDNK
|category= [[Culture|Culture]]
|label = Shortly Colonized by Denmark
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Denmark. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Italy

2021-11-17T15:02:42Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedITA
|category= [[Culture|Culture]]
|label = Shortly Colonized by Italy
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by Italy. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
)
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by the Netherlands

2021-11-17T15:02:10Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedNLD
|category= [[Culture|Culture]]
|label = Shortly Colonized by the Netherlands
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by the Netherlands. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Poland

2021-11-17T15:00:55Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedPOL
|category= [[Culture|Culture]]
|label = Shortly Colonized by Poland
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Poland. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Russia

2021-11-17T15:00:04Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul>
<li>
1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedRUS
|category= [[Culture|Culture]]
|label = Shortly Colonized by Russia
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Russia. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Sweden

2021-11-17T14:59:43Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul>
<li>
1 = Yes</li> <li>0 = No</li></ul>
|techname = cult_short_colonizedSWE
|category= [[Culture|Culture]]
|label = Shortly Colonized by Sweden
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Sweden. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Sweden

2021-11-17T14:59:07Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = </ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedSWE
|category= [[Culture|Culture]]
|label = Shortly Colonized by Sweden
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Sweden. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Russia

2021-11-17T14:58:32Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedRUS
|category= [[Culture|Culture]]
|label = Shortly Colonized by Russia
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Russia. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Sweden

2021-11-17T14:57:59Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedSWE
|category= [[Culture|Culture]]
|label = Shortly Colonized by Sweden
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Sweden. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications =
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Turkey (Ottoman Empire)

2021-11-17T14:57:02Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedTUR
|category= [[Culture|Culture]]
|label = Shortly Colonized by Turkey (Ottoman Empire)
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by Turkey (Ottoman Empire). Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.</li></ul>
}}

Shortly Colonized by the United States of America

2021-11-17T14:56:07Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedUSA
|category= [[Culture|Culture]]
|label = Shortly Colonized by the United States of America
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<ul>

|description = Country has bee shortly colonized by USA. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by India

2021-11-17T14:55:41Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedIND
|category= [[Culture|Culture]]
|label = Shortly Colonized by India
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by India. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25. </li></ul>
}}

Shortly Colonized by Hungary

2021-11-17T14:55:18Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedHUN
|category= [[Culture|Culture]]
|label = Shortly Colonized by Hungary
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by Hungary. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = * Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by the United Kingdom

2021-11-17T14:55:00Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedGBR
|category= [[Culture|Culture]]
|label = Shortly Colonized by the United Kingdom
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by the United Kingdom. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = * Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by France

2021-11-17T14:54:41Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedFRA
|category= [[Culture|Culture]]
|label = Shortly Colonized by France
|relatedindicators =<ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has bee shortly colonized by France. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by Denmark

2021-11-17T14:54:30Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedDNK
|category= [[Culture|Culture]]
|label = Shortly Colonized by Denmark
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Denmark. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by Germany

2021-11-17T14:54:20Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedDEU
|category= [[Culture|Culture]]
|label = Shortly Colonized by Germany
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Germany. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by Colombia

2021-11-17T14:54:08Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedCOL
|category= [[Culture|Culture]]
|label = Shortly Colonized by Colombia
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Colombia. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.

}}

Shortly Colonized by Brazil

2021-11-17T14:53:55Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedBRA
|category= [[Culture|Culture]]
|label = Shortly Colonized by Brazil
|relatedindicators = <ul>
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedESP| Shortly Colonized by Spain ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Brazil. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.

}}

Shortly Colonized by Spain

2021-11-17T14:53:42Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedESP
|category= [[Culture|Culture]]
|label = Shortly Colonized by Spain
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Spain. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = * Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Shortly Colonized by Spain

2021-11-17T14:53:34Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels == <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = cult_short_colonizedESP
|category= [[Culture|Culture]]
|label = Shortly Colonized by Spain
|relatedindicators = <ul>
<li>[[cult_short_colonizedBRA| Shortly Colonized by Brazil ]]
<li>[[cult_short_colonizedCOL|Shortly Colonized by Colombia]]
<li>[[cult_short_colonizedDEU|Shortly Colonized by Germany ]]
<li>[[cult_short_colonizedDNK|Shortly Colonized by Denmark ]]
<li>[[cult_short_colonizedFRA| Shortly Colonized by France ]]
<li>[[cult_short_colonizedGBR| Shortly Colonized by the United Kingdom ]]
<li>[[cult_short_colonizedHUN| Shortly Colonized by Hungary ]]
<li>[[cult_short_colonizedIND| Shortly Colonized by India ]]
<li>[[cult_short_colonizedITA| Shortly Colonized by Italy ]]
<li>[[cult_short_colonizedNLD| Shortly Colonized by the Netherlands ]]
<li>[[cult_short_colonizedPOL| Shortly Colonized by Poland ]]
<li>[[cult_short_colonizedRUS| Shortly Colonized by Russia ]]
<li>[[cult_short_colonizedSWE| Shortly Colonized by Sweden ]]
<li>[[cult_short_colonizedTUR| Shortly Colonized by Turkey (Ottoman Empire) ]]
<li>[[cult_short_colonizedUSA| Shortly Colonized by the United States of America]]
<ul>

|description = Country has been shortly colonized by Spain. Sharing a common colonial history is considered to be a major driver of similarities in trade, migration, development aid etc. Furthermore, we suspect that major groups in states orientate politically, socially and/or culturally on the former colonial power, that could very well manifest itself as an outright rejection. Sharing a common past, i.e. being colonized by the same state, displays the possibility of common cultural features either by being formed in the colonization period or by a shared (understanding of the) past.
|codingrules =
We take the CEPII GeoDist dataset and create binary variables for several colonial powers depicting whether a country was colonized by this state. “Colonization is here a fairly general term that we use to describe a relationship between two countries, independently of their level of development, in which one has governed the other over a long period of time and contributed to the current state of its institutions” (Mayer T. and Zignago 2011, 12).
For this version we compute that as a time-invariant variable. Furthermore, we take the information from CEPII that distinguishes between being colonized and being shortly colonized.
To make an example we can look at Zambia, which was colonized by the UK and compare that to Afghanistan which was only shortly colonized by the UK. In this example Zambia and Afghanistan have no common cultural history because the exposure to the colonizer was of a very different length. We have a total of 21 different colonizers and 16 short colonizers.
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania).

For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.]

|citation =
|relatedpublications = NA (no information available)
|projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = * Mayer, Thierry, and Soledad Zignago. 2011. “Notes on CEPII’s distances measures: The GeoDist database.” CEPII Working Paper 2011-25.
}}

Children in employment, female

2021-11-17T14:52:45Z

Stecor:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|scale=Metric
|scale=Metric
|valuelabels=value labels
|techname=socstr_lm_ep_cepf
|category=Social structure
|label=Children in employment, female
|relatedindicators=<ul><li>[[Children in employment, total]]</li></ul>
<ul><li>[[Children in employment, male]]</li></ul>
|description=Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school. Only female children counted in this indicator.
|codingrules=Ratio = (girls in employment / all girls)
|citation=World Bank. 2016. Understanding Children's Work project based on data from ILO, UNICEF and the World Bank.
|relatedpublications=related publications
|projectmanagers=Andrea Schäfer, Karolin Meyer
|datarelease=<ul><li>Version 0.001: Initial release</li></ul>
|revisions=No revisions yet
|sources=sources
}}


{{#default_form:IndicatorForm}}

Atkinson Coefficient (epsilon=0.5)

2021-11-17T14:51:58Z

Stecor:

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|scale=Metric
|scale=Metric
|valuelabels=<ul><li>0 = complete unequal distribution of income </li></ul>
<ul><li>1 = complete equal distribution of income </li></ul>
The Atkinson Coefficient ranges between these two values.
|techname=socstr_atkincoef_eps05
|category=Social structure
|label=Atkinson Coefficient (epsilon=0.5)
|relatedindicators=<ul><li>[[Atkinson Coefficient (epsilon=1)]]</li></ul>
|description=The Atkinson Coefficient is measuring income inequality around the world. This Coefficient not only shows the ratio itself but takes different parameters like society's aversion of income inequality into account.
|codingrules=The epsilon is measuring the aversion of the society towards income inequality. The greater the epsilon the more tends the Coefficient towards 1.
|citation=LIS Cross-National Data Center in Luxembourg. 2019. Luxembourg income study database
and the luxembourg wealth study database. Retrieved from http://www.lisdatacenter.org/
data-access/key-figures/download-key-figures/
|relatedpublications=
|projectmanagers=Jean-Yves Gerlitz, Karolin Meyer
|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, version Jan19. University of Gothenburg: The Quality of Government Institute. http://www.qog.pol.gu.se doi:10.18157/qogstdjan19 </li>
</ul>
}}

Introduction (implementation) long-term care system (type A)

2021-11-17T14:49:30Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul>
<li>0 = "no"</li>
<li>1 = "yes"</li>
</ul>

|techname = health_LTC_system_intro_imple_LTCsystem_typeA
|category= [[Health and long-term care|Health and long-term care]]
|label = Introduction (implementation) long-term care system (type A)
|relatedindicators =
|description = The indicator specifies the year of formal implementation of a long-term care system (type A) in a country in the period 1940-2020. The year in which the law establishing the long-term care system enters formally (de jure) into force is coded ‘1’, all others ‘0’.
A long-term care system is introduced when (i) country-wide legislation is adopted which (ii) establishes entitlements to LTC benefits, and (iii) one/several specific institution(s) are determined responsible for manageing the LTC system (integration) (cf. De Carvalho & Fischer 2020; Fischer et al. 2021).
|codingrules = The year in which the law establishing the long-term care system enters formally (de jure) into force is coded ‘1’, all others ‘0’.
Multiple data sources were used for identifying the year/date of system introduction: laws and regulations, academic publications and reports and grey literature, national online newspaper articles and (official) websites as well as primary data collected through the project’s Expert Survey on Long-Term Care in 2020/21. For more information on the sources used per country see Fischer & Sternkopf (2021).
|citation = Fischer, J., & Sternkopf, M. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series.
|relatedpublications = <ul><li>Fischer, Johanna, Alexander Polte, and Meika Sternkopf. 2021. “The introduction of long-term care systems: The nascent diffusion of an emergent field of social policy.” In Global Dynamics of Social Policy. Networks and geographies of global social policy diffusion: Culture, economy and colonial legacies, eds. Michael Windzio, Ivo Mossig, Fabian Besche-Truthe and Helen Seitzer. Basingstoke: Palgrave Macmillan.
</li><li>Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series.</li> </ul>

|projectmanagers = A04 project, main responsible person Johanna Fischer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> For a detailed overview of all sources per country used please see:
Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series. </li></ul>
}}

Introduction (implementation) long-term care system (type A)

2021-11-17T14:49:20Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul>
<li>0 = "no"</li>
<li>1 = "yes"</li>
</ul>

|techname = health_LTC_system_intro_imple_LTCsystem_typeA
|category= [[Health and long-term care|Health and long-term care]]
|label = Introduction (implementation) long-term care system (type A)
|relatedindicators = NA (no information available)
|description = The indicator specifies the year of formal implementation of a long-term care system (type A) in a country in the period 1940-2020. The year in which the law establishing the long-term care system enters formally (de jure) into force is coded ‘1’, all others ‘0’.
A long-term care system is introduced when (i) country-wide legislation is adopted which (ii) establishes entitlements to LTC benefits, and (iii) one/several specific institution(s) are determined responsible for manageing the LTC system (integration) (cf. De Carvalho & Fischer 2020; Fischer et al. 2021).
|codingrules = The year in which the law establishing the long-term care system enters formally (de jure) into force is coded ‘1’, all others ‘0’.
Multiple data sources were used for identifying the year/date of system introduction: laws and regulations, academic publications and reports and grey literature, national online newspaper articles and (official) websites as well as primary data collected through the project’s Expert Survey on Long-Term Care in 2020/21. For more information on the sources used per country see Fischer & Sternkopf (2021).
|citation = Fischer, J., & Sternkopf, M. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series.
|relatedpublications = <ul><li>Fischer, Johanna, Alexander Polte, and Meika Sternkopf. 2021. “The introduction of long-term care systems: The nascent diffusion of an emergent field of social policy.” In Global Dynamics of Social Policy. Networks and geographies of global social policy diffusion: Culture, economy and colonial legacies, eds. Michael Windzio, Ivo Mossig, Fabian Besche-Truthe and Helen Seitzer. Basingstoke: Palgrave Macmillan.
</li><li>Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series.</li> </ul>

|projectmanagers = A04 project, main responsible person Johanna Fischer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet
|sources = <ul><li> For a detailed overview of all sources per country used please see:
Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series. </li></ul>
}}

Introduction (adoption) long-term care system (type A)

2021-11-17T14:46:31Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul>
<li>0 = "no"</li>
<li>1 = "yes"</li>
</ul>

|techname = health_LTC_system_intro_adoption_rud_LTCsystem
|category= [[Health and long-term care|Health and long-term care]]
|label = Introduction (adoption) long-term care system (type A)
|relatedindicators =
|description = The indicator specifies the year of formal adoption of a long-term care system (type A) in a country in the period 1940-2020. The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
A long-term care system is introduced when (i) country-wide legislation is adopted which (ii) establishes entitlements to LTC benefits, and (iii) one/several specific institution(s) are determined responsible for manageing the LTC system (integration) (cf. De Carvalho & Fischer 2020; Fischer et al. 2021).
|codingrules = The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
Multiple data sources were used for identifying the year/date of system introduction: laws and regulations, academic publications and reports and grey literature, national online newspaper articles and (official) websites as well as primary data collected through the project’s Expert Survey on Long-Term Care in 2020/21. For more information on the sources used per country see Fischer & Sternkopf (2021).
|citation = Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series.
|relatedpublications = <ul><li>Fischer, Johanna, Alexander Polte, and Meika Sternkopf. 2021. “The introduction of long-term care systems: The nascent diffusion of an emergent field of social policy.” In Global Dynamics of Social Policy. Networks and geographies of global social policy diffusion: Culture, economy and colonial legacies, eds. Michael Windzio, Ivo Mossig, Fabian Besche-Truthe and Helen Seitzer. Basingstoke: Palgrave Macmillan.</li>
</li>Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series.</li></ul>
|projectmanagers = A04 project, main responsible person Johanna Fischer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> For a detailed overview of all sources per country used please see:
Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series. </li></ul>
}}

Introduction (adoption) long-term care system (type A)

2021-11-17T14:46:11Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = = <ul>
<li>0 = "no"</li>
<li>1 = "yes"</li>
</ul>

|techname = health_LTC_system_intro_adoption_rud_LTCsystem
|category= [[Health and long-term care|Health and long-term care]]
|label = Introduction (adoption) long-term care system (type A)
|relatedindicators =
|description = The indicator specifies the year of formal adoption of a long-term care system (type A) in a country in the period 1940-2020. The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
A long-term care system is introduced when (i) country-wide legislation is adopted which (ii) establishes entitlements to LTC benefits, and (iii) one/several specific institution(s) are determined responsible for manageing the LTC system (integration) (cf. De Carvalho & Fischer 2020; Fischer et al. 2021).
|codingrules = The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
Multiple data sources were used for identifying the year/date of system introduction: laws and regulations, academic publications and reports and grey literature, national online newspaper articles and (official) websites as well as primary data collected through the project’s Expert Survey on Long-Term Care in 2020/21. For more information on the sources used per country see Fischer & Sternkopf (2021).
|citation = Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series.
|relatedpublications = <ul><li>Fischer, Johanna, Alexander Polte, and Meika Sternkopf. 2021. “The introduction of long-term care systems: The nascent diffusion of an emergent field of social policy.” In Global Dynamics of Social Policy. Networks and geographies of global social policy diffusion: Culture, economy and colonial legacies, eds. Michael Windzio, Ivo Mossig, Fabian Besche-Truthe and Helen Seitzer. Basingstoke: Palgrave Macmillan. </li>
</li>Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series.</li></ul>
|projectmanagers = A04 project, main responsible person Johanna Fischer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> For a detailed overview of all sources per country used please see:
Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series. </li></ul>
}}

Introduction (adoption) long-term care system (type A)

2021-11-17T14:45:30Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = 0 = "no"; 1 = "yes"
|techname = health_LTC_system_intro_adoption_rud_LTCsystem
|category= [[Health and long-term care|Health and long-term care]]
|label = Introduction (adoption) long-term care system (type A)
|relatedindicators =
|description = The indicator specifies the year of formal adoption of a long-term care system (type A) in a country in the period 1940-2020. The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
A long-term care system is introduced when (i) country-wide legislation is adopted which (ii) establishes entitlements to LTC benefits, and (iii) one/several specific institution(s) are determined responsible for manageing the LTC system (integration) (cf. De Carvalho & Fischer 2020; Fischer et al. 2021).
|codingrules = The year of adoption of the law establishing the long-term care system is coded ‘1’, all others ‘0’.
Multiple data sources were used for identifying the year/date of system introduction: laws and regulations, academic publications and reports and grey literature, national online newspaper articles and (official) websites as well as primary data collected through the project’s Expert Survey on Long-Term Care in 2020/21. For more information on the sources used per country see Fischer & Sternkopf (2021).
|citation = Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series.
|relatedpublications = <ul><li>Fischer, Johanna, Alexander Polte, and Meika Sternkopf. 2021. “The introduction of long-term care systems: The nascent diffusion of an emergent field of social policy.” In Global Dynamics of Social Policy. Networks and geographies of global social policy diffusion: Culture, economy and colonial legacies, eds. Michael Windzio, Ivo Mossig, Fabian Besche-Truthe and Helen Seitzer. Basingstoke: Palgrave Macmillan. </li>
Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series.</li></ul>
|projectmanagers = A04 project, main responsible person Johanna Fischer
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> For a detailed overview of all sources per country used please see:
Fischer, Johanna, and Maika Sternkopf. (forthcoming). The Historical Long-Term Care Systems Dataset (HLTCS): Data Collection and Codebook. CRC 1342 Technical Paper Series. </li></ul>
}}

Healthcare system existence

2021-11-17T14:40:37Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_Existence
|category= [[Health and long-term care|Health and long-term care]]
|label = Healthcare system existence
|relatedindicators =
|description = This variable indicates that the respective country has established a healthcare system unter public responsibility.
|codingrules = This indicator takes tha value 1 = "Yes" for the year of introduction of a healthcare system under public responsibility and each year after this date. For all other years, the indicator is set to 0 = "No". A healthcare system under public responsibility is introduced with (a) the first nation-wide legislation, (b) enactment of entitlements to healthcare benefits, and (c) the integration of elements of the healthcare system (e.g. through the definition of institutional responsibilities for financing, service provision and regulation).
|citation = de Carvalho, Gabriela, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series. Bremen: SOCIUM, SFB 1342.

de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications = NA (no information available)
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Year of healthcare system introduction

2021-11-17T14:39:33Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroBinary
|category= [[Health and long-term care|Health and long-term care]]
|label = Year of healthcare system introduction
|relatedindicators =
|description = This variable indicates that the respective country has introduced a healthcare system unter public responsibility.
|codingrules = This indicator takes tha value 1 = "Yes" for the year of introduction of a healthcare system under public responsibility. For all other years, the indicator is set to 0 = "No". A healthcare system under public responsibility is introduced with (a) the first nation-wide legislation, (b) enactment of entitlements to healthcare benefits, and (c) the integration of elements of the healthcare system (e.g. through the definition of institutional responsibilities for financing, service provision and regulation).
|citation = de Carvalho, Gabriela, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series. Bremen: SOCIUM, SFB 1342.
de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Date of healthcare system introduction

2021-11-17T14:38:18Z

Stecor:

{{Indicator
|datatype = Date
|scale = Date
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroDateJure
|category= [[Health and long-term care|Health and long-term care]]
|label = Date of healthcare system introduction
|relatedindicators =
|description = This variable indicates the date of the adoption of the legislative act introducing a healthcare system under public responsibility.
|codingrules = This variable indicates the date of the adoption of the legislative act introducing a healthcare system under public responsibility. As a rule, the date is provided as YYYY.MM.DD. If the exact date is unknown DD is set to the last date of the month. If the exact month is unknown, the month is set to 12.
|citation = de Carvalho, Gabriela, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series. Bremen: SOCIUM, SFB 1342.

de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications = NA (no information available)
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Name of the legal act introducing the healthcare system

2021-11-17T14:37:21Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroLaw
|category= [[Health and long-term care|Health and long-term care]]
|label = Name of the legal act introducing the healthcare system
|relatedindicators =
|description = This variable specifies the name and where available and applicable the number of the legal act introducing a healthcare system under public responsibility.
|codingrules = Name and number of the law.
|citation = de Carvalho, Gabriela, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series. Bremen: SOCIUM, SFB 1342.

de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources =<ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Content of the legislation introducing the healthcare system

2021-11-17T14:34:33Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroLawContent
|category= [[Health and long-term care|Health and long-term care]]
|label = Content of the legislation introducing the healthcare system
|relatedindicators =
|description = Brief description of the contents of legislation introducing a healthcare system under public responsibility. Includes e.g., major policy goals, extent of coverage in terms of population and services, or entitlement criteria.
|codingrules = Description of content.
|citation = Carvalho, Gabriela de, and Johanna Fischer. 2020. Healthcare and Long-Term Care Systems and Reforms - Concepts and Operationalisations for Global and Historical Comparative Research. Vol. 3 of SFB 1342 Technical Paper Series. Bremen: SOCIUM, SFB 1342.

de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Healthcare system existence before Independence

2021-11-17T14:31:30Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_BeforeIndependence
|category= [[Health and long-term care|Health and long-term care]]
|label = Healthcare system existence before Independence
|relatedindicators =
|description = This variable refers to former colonies. Generally, the introduction of a healthcare system under public responsibility is examined for independent countries. This variable describes healthcare systems introduced under colonial rule which sustained after independence.
|codingrules = Date of introduction and description of the healthcare system under public responsibility implemented under colonial rule.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Actors responsible for regulation at introduction

2021-11-17T14:29:56Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroActorsDescrip
|category= [[Health and long-term care|Health and long-term care]]
|label = Actors responsible for regulation at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at introduction. It includes a list of actors responsible for the regulation and organization of the healthcare system and their main responsibilities.
|codingrules = Regulation refers to the day-to-day organization of relations between healthcare providers, (potential) patients/ beneficiaries, and financing institutions. In particular, this includes the competence to define the level of prepayments, the allocation of funds and provider remuneration, and access to healthcare.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Eligibility rules for entitled groups of population at introduction

2021-11-17T14:28:30Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroEligibility
|category= [[Health and long-term care|Health and long-term care]]
|label = Eligibility rules for entitled groups of population at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable specifies eligilbility rules for groups of people entitled to healthcare benefits.
|codingrules = Description of eligibility conditions for groups entitled to healthcare benefits.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources =<ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Healthcare system type at introduction

2021-11-17T14:27:31Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_systemtype
|category= [[Health and long-term care|Health and long-term care]]
|label = Healthcare system type at introduction
|relatedindicators = [[Main actor type responsible for regulation at introduction|Main actor type responsible for regulation at introduction]]
[[Main actor type responsible for financing at introduction|Main actor type responsible for financing at introduction]]

[[Main actor type providing services at introduction|Main actor type providing services at introduction]]
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates the dominant actor type in the regulation, financing, and service provision dimension of the healthcare system.
|codingrules = Description of dominant actor types. For coding rules see related indicators.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Main actor type responsible for regulation at introduction

2021-11-17T14:26:07Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Multinomial
|valuelabels = <ul><li>1 = State actor</li><li>2 = Societal actor</li><li>3 = Private actor</li><li>4 = Gobal actor </li><li>0 = No dominant actor identified</li></ul>
|techname = health_HC_regulation_IntroActorCoded
|category= [[Health and long-term care|Health and long-term care]]
|label = Main actor type responsible for regulation at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates the dominant actor type in the regulation dimension of the healthcare system. It is based on the actor types as defined in respective legislations and descriptions of the healthcare system. Actor types are state, societal, private or global.
|codingrules = The variable is coded according to the dominant actor type responsible for healthcare regulation as it was organized at the introduction of a healthcare system under public responsibility. Regulation refers to the day-to-day organization of relations between healthcare providers, (potential) patients/ beneficiaries, and financing institutions. In particular, this includes the competence to define the level of prepayments, the allocation of funds and provider remuneration, and access to healthcare. Actor types are: state, societal, private, and global actors. State actors refer to government branches and public authorities at different territorial levels (value label = 1). Societal actors refer to self-regulation by non-governmental or private non-profit organizations (2). Private actors refer to the interactions of private for-profit organizations and/or individuals in the healthcare market (3). Global actors refer to non-resident institutions, international organizations, international NGOs or foreign states (4). The dominant actor type is defined by expert assessment.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Population groups entitled to healthcare at introduction

2021-11-17T14:22:57Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_regulation_IntroEntitlement
|category= [[Health and long-term care|Health and long-term care]]
|label = Population groups entitled to healthcare at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable lists the group of people entitled to healthcare benefits.
|codingrules = List of groups entitled to healthcare benefits.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on citizenship

2021-11-17T14:22:13Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleCitizen
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on citizenship
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on their rights as citizens.
|codingrules = This indicator takes the value 1 = "Yes", if all citizens are entitled to healthcare goods and services. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on occupation

2021-11-17T14:21:27Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleOccupation
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on occupation
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on their occupation.
|codingrules = This indicator takes the value 1 = "Yes", if entitlements to healthcare goods and services are conditional upon occupation. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on means-testing

2021-11-17T14:20:35Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleMT
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on means-testing
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on a means-test.
|codingrules = This indicator takes the value 1 = "Yes", if entitlements to healthcare goods and services are conditional upon a means-test and entitlements are targeted at the poor. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources =<ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on ethnicity

2021-11-17T14:19:46Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleEthnicity
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on ethnicity
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on their ethnic affiliation.
|codingrules = This indicator takes the value 1 = "Yes", if entitlements to healthcare goods and services are based on ethnic affiliation. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on residence

2021-11-17T14:18:33Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleResidence
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on residence
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on their rights as residents.
|codingrules = This indicator takes the value 1 = "Yes", if all residents are entitled to healthcare goods and services. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Entitlement at introduction based on other criteria

2021-11-17T14:17:14Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_regulation_IntroEntitleOther
|category= [[Health and long-term care|Health and long-term care]]
|label = Entitlement at introduction based on other criteria
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates that people are entitled to healthcare goods and services based on other characteristics than citizenship, ethnic affiliation, means-test, residence or occupation.
|codingrules = This indicator takes the value 1 = "Yes", if entitlements to healthcare goods and services are based on other characteristics than citizenship, ethnic affiliation, means-test, residence or occupation. Otherwise, the indicator takes the value 0 = "No".
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
|relatedpublications =
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Actors responsible for financing at introduction

2021-11-17T14:15:40Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_financing_IntroActorsDescrip
|category= [[Health and long-term care|Health and long-term care]]
|label = Actors responsible for financing at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. It specifies the actors responsible for the financing of the healthcare system in the respective country. Responsibility for financing includes the collection, pooling, and allocation of funds.
|codingrules = List of actors responsible for financing (collection, pooling, and allocation of funds).
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.
|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Main actor type responsible for financing at introduction

2021-11-17T14:14:27Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Multinomial
|valuelabels = <ul><li>1 = State actor</li><li>2 = Societal actor</li><li>3 = Private collective actor</li><li>4 = Private individual actor</li><li>5 = Gobal actor </li><li>0 = No dominant actor identified</li></ul>
|techname = health_HC_financing_IntroActorCode
|category= [[Health and long-term care|Health and long-term care]]
|label = Main actor type responsible for financing at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. The variable indicates the dominant actor type in the financing dimension of the healthcare system. It is based on the actor types and system of financing as defined in respective legislations and, where available, the size of financing shares attributed to the respective actor type.
|codingrules = The variable is coded according to the dominant actor type responsible for healthcare financing of the healthcare system under public responsibility at introduction. Dominance is either defined by the definition of major financing actors in the legislation or, if available, the relative majority of healthcare financing attributable to the respective actor type. Actor types are: state, societal, private collective, private individual, and global actors. State actors refer to government agencies and the general government budget at different territorial levels ( value label = 1). Societal actors refer to mandatory financing schemes organized by non-governmental organizations with autonomous budgets (2). Private collective actors refer to voluntary financing schemes organized by private actors (3). Private individual actors refer to healthcare financing by individuals and households (4). Global actors refer to healthcare financing by non-resident institutions, international organizations, international NGOs or foreign states (5).
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Financing of the healthcare system at introduction

2021-11-17T14:12:58Z

Stecor:

{{Indicator
|datatype = String
|scale = String
|valuelabels = Not applicable
|techname = health_HC_financing_IntroDescription
|category= [[Health and long-term care|Health and long-term care]]
|label = Financing of the healthcare system at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. It describes the system of financing as defined in the respective legislation.
|codingrules = Description of the the system of financing.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Healthcare system financed through taxation at introduction

2021-11-17T14:11:30Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>1 = Yes</li><li>0 = No</li></ul>
|techname = health_HC_financing_IntroTax
|category= [[Health and long-term care|Health and long-term care]]
|label = Healthcare system financed through taxation at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. It indicates taxes and the general government budget as a major financing source of the healthcare system.
|codingrules = Taxes or general government budget are characterized as revenue that does not establish enitlements to healthcare. It may include health spending at federal, regional or local level of government. The indicator takes the value 1 = "Yes" if the respective legislation and system description define taxes or the general government budget as a substantial financing source of healthcare services.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}

Healthcare system financed through contribution at introduction

2021-11-17T14:10:18Z

Stecor:

{{Indicator
|datatype = Numeric
|scale = Binary
|valuelabels = <ul><li>0 = No</li><li>1 = Yes</li></ul>
|techname = health_HC_financing_IntroCont
|category= [[Health and long-term care|Health and long-term care]]
|label = Healthcare system financed through contribution at introduction
|relatedindicators =
|description = This variable describes the features of the healthcare system at the point of introduction. It indicates insurance contributions as a major financing source of the healthcare system.
|codingrules = Contributions are characterized as prepayments to social health insurance establishing entitlements to healthcare goods and services. The indicator takes the value 1 = "Yes" if the respective legislation and system description define contributions as a substantial financing source of healthcare services.
|citation = de Carvalho, Gabriela, Alexander Polte, Katharina Scherf, Mai Mohamed Abdou Mahmoud , Lorraine Frisina Doetter. 2021. Trends in time: Identifying health care system introductions worldwide. SFB1342 Working Paper 16. Bremen: SOCIUM, SFB 1342.
Achim Schmid, Gabriela de Carvalho, Antonio Basilicata, and Heinz Rothgang. 2021. Classifying healthcare systems at introduction: Types of healthcare systems under public responsibility. SFB 1342 Working Paper 13. Bremen: SOCIUM, SFB 1342.

|relatedpublications = <ul><li> Frisina Doetter, Lorraine, Achim Schmid, Gabriela de Carvalho, and Heinz Rothgang. 2021. “Comparing apples to oranges? Minimizing typological biases to better classify healthcare systems globally.” Health Policy OPEN 2 (1): 1-8. doi:https://doi.org/10.1016/j.hpopen.2021.100035. </li></ul>
|projectmanagers = Gabriela de Carvalho; Achim Schmid
|datarelease = <ul><li>Version 0.001: Initial release</li></ul>
|revisions = No revisions yet

|sources = <ul><li> National legislation, expert survey, secondary literature, see bibliographic info </li></ul>
}}