This project is created by visadb.io - First digital marketplace to hire immigration, relocation and tax experts around the world. Visadb.io is funded by European Smart growth funds, Government of Chile and Poland innovation grants and MIT Enterprise forum acceleration. Learn more at visadb.io/about

All the data is a public data available from multiple sources. Talent migration data is sourced and offered by Worldbank and LinkedIn partnership for digital development campaign. Immigration data is sourced from United Nations. All the data is processed from its original state to make meaningful visualizations.

LinkedIn members self-report their work experience and skills on their profiles, using more than 50,000 distinct, standardized skills classified by LinkedIn. These have been coded and classified by taxonomists at LinkedIn into 249 skill groups that are presented in this dataset; each is described in our methodology paper, together with methodology to extract, clean and validate the data against official sources where available. The data in this first phase of collaboration include countries that had at least 100,000 LinkedIn members at the end of 2017 to maximize confidence in our samples and data quality. All the data is processed from its original state to make meaningful visualizations.

The project team has conducted data quality checks for all the countries included in the dataset and validated the data against 23 external data sources before publishing to the dashboard. Our assessments of the data’s representativeness are based on comparisons to the latest available data from ILOStat, a consolidated dataset of national labor surveys covering over 100 countries. Compared to workers overall, LinkedIn’s members are more likely to be young, technology-savvy, female, and from the ranks of business professionals. How members use the LinkedIn platform can vary based on professional, social, and regional culture, as well as overall LinkedIn website availability and accessibility in individual countries. Information on data standardization, validation and adjustments to reflect cultural and membership variances are detailed in the data’s methodology paper. All data shared under this collaboration is aggregated data to protect LinkedIn member privacy: each data point contains aggregated information of at least 50 members.

Our initial comparisons to government surveys show that WBG-LinkedIn metrics have strongest representation in knowledge-intensive and tradable sectors, including: Financial Services, Professional Services, Information & Communication Technology (ICT), the Arts & Creative Industries, Manufacturing, and Mining/Quarrying. Tech-savvy, business professionals, youth, and women are also more likely to be on LinkedIn than the average worker. Our assessments of representativeness are based on comparisons to the latest available data from ILOStat, a consolidated dataset of national labor surveys covering over 100 countries. In general, the metrics represent the world as seen through the lens of LinkedIn data, which is influenced by how LinkedIn members choose to use the platform. This can vary based on professional, social, and regional culture, as well as overall site availability and accessibility in individual countries. These variances cannot be fully accounted for in the analysis and is detailed in the methodology paper here.

The data only show countries that had at least 100,000 LinkedIn members at the end of 2017 to maximize confidence in our samples and data quality. The team will add more countries as they cross this threshold in the next data refresh.

Yes, though this is subject to end-user demand and feedback. The project team will monitor downloads and citations of this dataset to assess demand.

Yes, email us at danish@visadb.io