The first round of the Economic Graph Challenge included more than 200 submissions, each of which had something interesting to say. We ended up working with 10 teams for about a year. These engagements produced some strong results, generated exciting lines of research and generated intriguing new ideas that can help us better understand the economy.
Below is a quick summary of some of the ongoing research from these teams.
Gender bias in self-promotion by MBAs
One team analyzed whether male and female graduates of top-ten MBA programs from 2011-2016 promoted themselves equally on their LinkedIn profiles. The team discovered that women and men were comparable in the number of skills and honors they included on their profiles, but that women were less likely than men to include job descriptions or summaries. We don’t know why women in top MBA programs are less inclined to include these more descriptive parts of their LinkedIn profiles, but this could be a fertile avenue for future research.
Read the paper here.
This team dug into the costs and benefits of restricting worker mobility using non-compete clauses. Such clauses can be a double-edged sword: while they potentially limit workers’ ability to seek out better employee-job matches in the short term, they may also encourage capital investment at existing firms by helping firms retain talent. Using data on employment history and court decisions on the enforceability of non-competes, the researchers concluded that both of these effects indeed occurred. Non-competes simultaneously reduce worker departures to entrepreneurship opportunities (and thereby reduce the creation of new firms), but do also lead employers to invest more.
Read more about the team’s work here.
Collaborations with Indiana University and MIT
Additionally, two EGC teams unveiled such promising research that we decided to form longer partnerships with their institutions: Indiana University and MIT.
The team from Indiana University designed an algorithm that identifies fine-grained geo-industrial clusters called “microindustries” (e.g., electric vehicle manufacturers in northern California, or Milanese fashion houses) based on workers’ firm-to-firm transitions. Using Bloomberg S&P 500 data, the team showed that talent flows into and out of microindustries correlate with changes in market cap. This demonstrated that our dataset captures real-world economic phenomena at a level of detail and scale that rivals best-of-breed methodologies from agencies like the Bureau of Labor Statistics.
The MIT collaboration has a research agenda of measuring the value of human capital and the distribution of that value between firms, employees and consumers. In the first stage of that research, the team is using LinkedIn data as well financial data to determine the value of human capital embodied in firms. In future stages of the work, they plan to use LinkedIn salary data to measure how much of the value of human capital accrues to the workers themselves and test different theories of investment in human capital.
Read more about our published Economic Graph Research.