• This research was conducted in partnership with the Inter-American Development Bank (IDB) for presentation at the Employment Working Group meeting of the G20 on April 11th, 2018
• LinkedIn data was analyzed to understand:
  1. What are the most emerging and declining roles?
  2. What are the skills most associated with these roles?
  3. For declining skills, how transferable are they to other areas of the labor market?
  4. For emerging occupations, how is talent migrating across the world?
• This document is meant to be an interactive complement to the work presented to the G20

• Included hiring data based on member profiles from 2008 - 2017
• For each position on a member's profile, we looked at the starting year of that role. That was considered the year of hire.
• Only members who have logged into LinkedIn from 2017 onwards were included in the data to ensure that profile information was up to date
• Internships and volunteer positions were removed from the data
• Countries included: Argentina, Australia, Brazil, Chile, France, India, Mexico, South Africa, United Kingdom, United States
• Occupations included reflect 275 occupations from LinkedIn's taxonomy
• When analyzing labor market insights, we only use anonymized, aggregated data. As a members-first organization, the privacy of our members is LinkedIn's first priority.

1. For each country and year, hiring for an occupation was calculated as a proportion of total hiring for that country-year, giving us a time-series dataset of hiring proportions for each occupation
2. Occupations that are emerging and declining are defined as those with the greatest increase or decrease (respectively) in hiring proportions over the observed period
3. To smooth out noise and variance from year to year, a linear regression was fit to each country+occupation combination
4. Identification of emerging and declining occupations are therefore based on the slope of each regression line

1. The top skills associated with each emerging and declining occupation, within each country, were identified by analyzing the skills listed in member profiles of those who are currently working in that occupation
2. For each country-occupation, the top skills were identified by indexing how important a skill was for that occupation vs. an average occupation

• Transferable skills are better able to withstand labor market disruptions in a dynamic, digital economy
• By analyzing how distributed a skill is across multiple industries, we can understand how transferable a skills is across different areas and develop a "Transferability Index"
• The higher the index, the more transferable the skill

• To understand migration of emerging occupations, an "emerging occupation migration index" was calculated
• This index represents amount of migration entering country for emerging occupations, relative to all occupations
• The higher the index, the more emerging occupations are coming from that country

• The Economic Graph, as a digital representation of the global economy, can provide insights that complement traditional sources of data available to policymakers
• We look forward to sharing these ideas with the G20 and working with IDB and other partners to develop them further
• Our vision is that our data could be used to help policymakers design education and workforce policies that create economic opportunity for every member of the global workforce

The work we are doing to support the G20 exploration of Future of Work has benefited greatly from our collaboration with the Labor Markets Division at the Inter-American Development Bank under the direction of Carmen Pages-Serra. We would also like to recognize the support of Beatriz Nofal and Alejandra Kern, Esteban Eseverri and the Argentine Ministry of Labor for providing direction.