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Economic Graph Challenge: LinkedIn
In October 2014, LinkedIn launched the Economic Graph Challenge, inviting proposals to utilize their economic data for addressing significant labor market issues. Eleven teams were selected for their innovative solutions which included personalized career guidance, assessing gender disparities in self-promotion, and identifying skill gaps in the workforce. The overarching objective of these projects is to enhance job market efficiency, improve workforce mobility, and guide policy interventions through data-driven insights.
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Economic Graph Challenge: LinkedIn
- 2. Introducing the EconomicGraph Challenge In October 2014, LinkedIn put out an open call for proposals asking researchers, academics, and data-driven thinkers how they would use data from the LinkedIn Economic Graph to solve some of the challenging economic problems of our times. Out of hundreds of submissions, these are the eleven teams whose proposals met our challenge…
- 3. 2015 Winning Proposals •Text Mining on Dynamic Graphs • Your Next Big Move: Personalized Data-Driven Career Making • Connecting with Coworkers: The Value of Within-Firm Networks *Listed in no particular order • Effects of Social Structure on Labor Market Dynamics • Linking Women to Opportunity: Evaluating Gender Differences in Self-Promotion • Identifying Skill Gaps: Determining Trends in Supply and Demand for Skills
- 4. 2015 Winning Proposals •Find and Change Your Position in a Virtual Professional World • Forecasting Large-Scale Industrial Evolution • Urban Professional Genome Measuring City Performance *Listed in no particular order • Inequality of Access to Productive Labor Markets: How big is it and How Can it be Fixed? • Bridging the Skills Gap by Transforming Education
- 5. Katherine Heller Assistant Professor,Statistical Science Duke University David Banks Professor, Statistical Science Duke University Sayan Patra PhD Student, Statistical Science Duke University Text mining on dynamic graphs
- 6. We propose developingnew text models that analyze member profiles and job listings, utilizing network structure to discover relevant content. The new models use cutting-edge machine learning methods to predict changes to both text content and the network dynamics. Our goal is to invent new information technology that improves how LinkedIn members are matched with job openings and to advise companies on which skill sets best match their needs.
- 7. Abhinav Maurya Data ScienceResearcher Carnegie Mellon University Rahul Telang Professor Carnegie Mellon University Your next big move: Personalized data-driven career making
- 8. We propose buildingan engine that can recommend the skills most useful for a LinkedIn member to learn, based on the member’s existing skillset. Our goal is to help workers realize their true potential by acquiring skills for the job that they want, thus making them more competitive in the job market.
- 9. Jessica Jeffers PhD Candidate WhartonSchool, University of Pennsylvania Michael Lee PhD Candidate Wharton School, University of Pennsylvania Connecting with coworkers: The value of within-firm networks
- 10. We propose studyingwithin-firm connectivity, e.g. connections between managers and employees, to determine how network characteristics affect the social and economic value of a firm. By quantifying the importance of within-firm connectivity, we can encourage and empower companies to build their internal LinkedIn networks.
- 11. Alexander Volfovsky NSF MathematicalSciences Postdoctoral Research Fellow Statistics, Harvard University Edoardo Airoldi Associate Professor Statistics, Harvard University Effects of social structure on labor market dynamics Panos Toulis PhD Student, Google Fellow Statistics, Harvard University
- 12. Our research aimsto quantify causal mechanisms through which social structure and interactions can affect workforce mobility, and labor market dynamics more generally. We wish to help policy makers understand the dynamics of economic mobility in the United States. Our results will enable accurate predictions and can help inform policy interventions.
- 13. Rajlakshmi De Senior ResearchAnalyst Federal Reserve Bank of New York Linking women to opportunity: Evaluating gender differences in self-promotion Kaylyn Frazier Research Program Manager Google Kristen M. Altenburger Statistics Graduate Student Harvard University
- 14. We will usematching techniques to analyze comparable LinkedIn profiles between men and women and examine differences in self-promotion. We will then evaluate whether individuals with higher degrees of self-promotion receive greater job opportunities. Our goal is to help women maximize career success through LinkedIn.
- 15. Identifying skill gaps:Determining trends in supply and demand for skills Frank MacCrory Postdoctoral Associate MIT Sloan Initiative on the Digital Economy George Westerman Research Scientist MIT Sloan Initiative on the Digital Economy Parul Batra MBA Candidate MIT Sloan School of Management Noel Sequeira MBA Candidate MIT Sloan School of Management
- 16. Although unemployment isdropping, a skills gap exists: employers face skill shortages and many workers are underemployed. We propose to develop tools that show skill gaps and workforce mobility issues in different segments of the economy. Our goal is to help job seekers, employers, educators and policy makers understand, in exceptional detail, skill gaps and other challenges and opportunities in the labor market.
- 17. David Dunson Arts andSciences Distinguished Professor Dept. of Statistical Science Duke University Joseph Futoma PhD Student Dept. of Statistical Science Duke University Yan Shang PhD Student Fuqua School of Business Duke University Find and change your position in a virtual professional world
- 18. Our goal isto use relational information from the LinkedIn network to increase transparency and efficiency of both job searching and recruiting. We propose determining the relative positions of LinkedIn members in a virtual professional world. Each LinkedIn member is represented by a point in space. Closeness between members measures professional similarity. An institute/company/job can be represented by a data cluster of individual members, capturing complexity and heterogeneity.
- 19. Azadeh Nematzadeh PhD Student IndianaUniversity Bloomington Jaehyuk Park PhD Student Indiana University Bloomington Forecasting large-scale industrial evolution Ian Wood PhD Student Indiana University Bloomington Yizhi Jing PhD Student Indiana University Bloomington Yong-Yeol Ahn Assistant Professor School of Informatics and Computing Indiana University Bloomington
- 20. In order tohelp professionals adapt to an ever-changing economic landscape, we want to understand the macro-evolution of industries. We will analyze the flow of professionals between companies to identify emerging industries and associated skills. Our goal is to predict large-scale evolutions of industries and emerging skills, allowing us to forecast economic trends and guide professionals towards promising future career paths.
- 21. Stanislav Sobolevsky Research Scientist MIT AnthonyVanky PhD Candidate MIT Iva Bojic Postdoctoral Fellow MIT Urban professional genome measuring city performance Lyndsey Rolheiser PhD Candidate MIT Hongmou Zhang Research Fellow MIT
- 22. We propose creatingan “economic genome” of cities, companies, and individuals that aggregates various associated characteristics from the Economic Graph. The urban genome will provide a measure of a city’s economic health, as well as lend insight into the migration patterns of individuals and firms. The goal of this analysis is to predict city-level economic trends and to gain an understanding of what contributes to a city’s economic competitiveness.
- 23. Bobak Moallemi PhD Student StanfordGraduate School of Business Ryan Shyu PhD Student Stanford Graduate School of Business Inequality of access to productive labor markets: How big is it and how can it be fixed?
- 24. We will focuson job-to-job movements and recruiting activity to study flows of jobs and workers across geography and industries in the United States, ultimately aiming to quantify the importance of the job-worker match for economic growth and dynamism. Our goal is to allow the evaluation of the effect of various public and private sector programs on labor market fluidity and opportunity. Examples include tax incentives, social insurance, and career boards.
- 25. Bridging the skillsgap by transforming education Ozan Candogan Assistant Professor Fuqua School of Business Kostas Bimpikis Assistant Professor Stanford Graduate School of Business Kimon Drakopoulos PhD Candidate MIT
- 26. We propose ametric that measures the “distance” between skills, characterizes the mismatch between the supply and demand for skills in today’s workforce, and enables us to provide concrete and cost-effective ways to bridge the skills gap and identify economic opportunities for both employers and prospective employees. Our goal is to prescribe cost-effective ways to bridge skills gaps through efficient matching as well as through recommendations to community colleges and online course offerings.
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