Building Economic Data Products for Global Policymakers

Chris Glynn is Head of Data Science at the Indeed Hiring Lab

Economic policy organizations across the globe – including central banks, ministries of treasury, housing, commerce, labor, and more – must navigate rapidly changing economic conditions with incomplete information.  Policymakers have historically relied on economic indicators from government organizations to inform decisions, but these indicators are often stale, aggregated, and low-frequency.  For example, the Job Openings and Labor Turnover Survey (JOLTS) from the Bureau of Labor Statistics (BLS) in the United States is released monthly but with a two month lag.  Modern technology companies offer an alternative.  Platforms like Indeed (labor) and Zillow (housing) offer data that is (i) near real-time; (ii) geographically disaggregated; and (iii) released with minimal delay.  In contrast to JOLTS, the Indeed Job Postings Index (JPI) is a daily measure of labor market activity that is released weekly – significantly closing the information gap for policymakers.  Building trusted economic data products from technology platforms requires integration of a robust tech stack, economic expertise, statistical modeling, and product management.  In this talk, I will discuss how we’ve built our Data Science team at the Indeed Hiring Lab, the role of statistics, and our approach to building data products that create social and economic value from Indeed’s data.  

Speaker Bio: 

Chris Glynn is Head of Data Science at the Indeed Hiring Lab. Prior to joining Indeed, Chris was Senior Managing Economist at Zillow, where he led teams in economic research, data products, forecasting, and risk management. He was previously an assistant professor at the University of New Hampshire, where his research focused on statistical models and computational methods applied to dynamic markets. His research has been published in journals such as the Annals of Applied Statistics, Nature Communications, and Bayesian Analysis. Chris earned his Ph.D. in Statistical Science from Duke University, an M.S. in Mathematical Finance from Boston University, and a B.S. in Electrical Engineering from the University of New Hampshire.