Skills | LinkedIn Data

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Data that captures the evolution of skill requirements over time across the workforce based on updates to LinkedIn member profiles.

This dataset is part of the LinkedIn - World Bank Group partnership, which helps governments and researchers understand rapidly evolving labor markets with detailed and dynamic data. It allows leaders to benchmark and compare labor markets across the world; analyze skills, occupations, migration, and industries; and leverage real-time data to make policy changes.

Visualizations for many of these data are available at linkedindata.worldbank.org. The data cover 2015-2019, are refreshed on an annual basis, and are available for 140+ countries.

Type: 
Time Series
Languages Supported: 
English
Custom License Information: 

Aggregated datasets and visuals are available under the Creative Commons Attribution 4.0 IGO license with attribution to both LinkedIn Corporation and the World Bank. The World Bank Group and LinkedIn Corporation (including its affiliates) do not take responsibility and are not liable for any damage caused through use of data and insights through this website, including any indirect, special, incidental or consequential damages.

Topics: 
Economic Growth
Education
Information and Communication Technologies
Jobs
Private Sector Development
Public-Private Partnerships
Social Protection and Labor
Geographical Coverage: 
World
East Asia & Pacific
Australia
Cambodia
China
Fiji
Guam
Hong Kong SAR, China
Indonesia
Japan
Kiribati
Korea, Dem. People's Rep.
Korea, Rep.
Lao PDR
Macao SAR, China
Malaysia
Marshall Islands
Mongolia
Myanmar
Nauru
New Caledonia
New Zealand
Papua New Guinea
Philippines
Samoa
Singapore
Taiwan, China
Thailand
Timor-Leste
Tonga
Tuvalu
Vanuatu
Vietnam
Europe & Central Asia
Albania
Andorra
Armenia
Austria
Azerbaijan
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Georgia
Germany
Gibraltar
Greece
Greenland
Hungary
Iceland
Ireland
Italy
Kazakhstan
Kosovo
Latvia
Liechtenstein
Lithuania
Luxembourg
Moldova
Monaco
Montenegro
Netherlands
North Macedonia
Norway
Poland
Portugal
Romania
Serbia
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Tajikistan
Turkey
Turkmenistan
Ukraine
United Kingdom
Uzbekistan
Latin America & Caribbean
Anguilla
Argentina
Belize
Bolivia
Brazil
Chile
Costa Rica
Colombia
Cuba
Dominica
Dominican Republic
Ecuador
El Salvador
Grenada
Guatemala
Guyana
Haiti
Honduras
Jamaica
Martinique
Mexico
Montserrat
Nicaragua
Panama
Paraguay
Peru
Netherlands Antilles
Puerto Rico
Sint Maarten (Dutch part)
St. Barthélemy
St. Martin (French part)
Trinidad and Tobago
Uruguay
Venezuela, RB
Middle East & North Africa
Algeria
Bahrain
Egypt, Arab Rep.
Iraq
Iran, Islamic Rep.
Israel
Jordan
Kuwait
Lebanon
Libya
Malta
Morocco
Oman
Qatar
Saudi Arabia
Syrian Arab Republic
West Bank and Gaza
United Arab Emirates
Tunisia
Yemen, Rep.
North America
Canada
United States
South Asia
Afghanistan
Bangladesh
Bhutan
India
Pakistan
Nepal
Sri Lanka
Sub-Saharan Africa
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Congo, Dem. Rep.
Congo, Rep.
Côte d'Ivoire
Ethiopia
Eritrea
Gabon
Gambia, The
Ghana
Guinea
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Reunion
Rwanda
Seychelles
Senegal
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Eswatini
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Economy Coverage: 
High Income
Low Income
Lower Middle Income
Upper Middle Income
Number of Economies: 
199
Periodicity: 
Annual
Temporal Coverage: 
2015 - 2019

Update Frequency

Update Frequency: 
Annually
Statistical Concept and Methodology: 
Skill penetration: Measures the adoption of a skill based on the number of times it appears in the top 30 skills listed on the typical LinkedIn member profile for that occupation. For example, if 3 of 30 skills for Data Scientists in the Information Services industry fall into the Artificial Intelligence skill group, Artificial Intelligence has a 10% penetration for Data Scientists in Information Services. Industry Skills Needs: This metric captures which skills are most likely to be added to a member's profile in one industry compared to other industries. It's calculated using an adapted version of a text mining technique called Term Frequency - Inverse Document Frequency (TF-IDF). This method gives more weight to a skill for an industry if more members in the industry list the skill on their profiles and the skill is more unique to the industry. The skills considered are those added while a member holds a particular occupation. See this paper for more details on these methods: http://documents.worldbank.org/curated/en/827991542143093021/Data-Insights-Jobs-Skills-and-Migration-Trends-Methodology-and-Validation-Results
Unit of measure: 

Skill penetration

Organization: 
LinkedIn
Time Periods: 
August, 2020

No Visualizations Available.

"Skills Data" by World Bank Group & LinkedIn Corporation, licensed under CC BY 4.0

Data that captures the evolution of skill requirements over time across the workforce based on updates to LinkedIn member profiles.

This dataset is part of the LinkedIn - World Bank Group partnership, which helps governments and researchers understand rapidly evolving labor markets with detailed and dynamic data. It allows leaders to benchmark and compare labor markets across the world; analyze skills, occupations, migration, and industries; and leverage real-time data to make policy changes.

Visualizations for many of these data are available at linkedindata.worldbank.org. The data cover 2015-2019, are refreshed on an annual basis, and are available for 140+ countries.

FieldValue
Modified Date
2020-09-22
Release Date
Periodicity
Annual
Identifier
750afa0b-b015-4a84-a006-fe5e70284501
Temporal Coverage

2015 - 2019

License
License Not Specified
Contact Email
Public Access Level
Public
Rating: 
0
No votes yet
Type: 
Languages Supported: 
Time Periods: 
August, 2020
Number of Economies: 
199
Update Frequency: 
Custom License Information: 

Aggregated datasets and visuals are available under the Creative Commons Attribution 4.0 IGO license with attribution to both LinkedIn Corporation and the World Bank. The World Bank Group and LinkedIn Corporation (including its affiliates) do not take responsibility and are not liable for any damage caused through use of data and insights through this website, including any indirect, special, incidental or consequential damages.

Geographical Coverage: 
Data Classification of a Dataset: 
Start Date: 
Thursday, January 1, 2015
End Date: 
Tuesday, December 31, 2019
DEC
Programatic Region: 
Unit of measure: 
Skill penetration
Organization: 
LinkedIn
Statistical Concept and Methodology: 
Skill penetration: Measures the adoption of a skill based on the number of times it appears in the top 30 skills listed on the typical LinkedIn member profile for that occupation. For example, if 3 of 30 skills for Data Scientists in the Information Services industry fall into the Artificial Intelligence skill group, Artificial Intelligence has a 10% penetration for Data Scientists in Information Services. Industry Skills Needs: This metric captures which skills are most likely to be added to a member's profile in one industry compared to other industries. It's calculated using an adapted version of a text mining technique called Term Frequency - Inverse Document Frequency (TF-IDF). This method gives more weight to a skill for an industry if more members in the industry list the skill on their profiles and the skill is more unique to the industry. The skills considered are those added while a member holds a particular occupation. See this paper for more details on these methods: http://documents.worldbank.org/curated/en/827991542143093021/Data-Insights-Jobs-Skills-and-Migration-Trends-Methodology-and-Validation-Results
Citation Text: 
"Skills Data" by World Bank Group & LinkedIn Corporation, licensed under CC BY 4.0
Modified date: 
99
License: 
Primary Dataset: 
Yes

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