Visualizing the opinions of the world's leading economists

Several times a year, Chicago Booth's IGM Economic Experts Panel surveys dozens of leading economists on major public policy issues, ranging from the effects of a $15 minimum wage to those from the Brexit. The panel publishes summary statistics for each survey, such as how many economists agreed or disagreed with the survey question and their confidence in their responses. However I was interested in the other ways the data could be explored: for example, do economists from different research institutions vote differently? Which economists respond to the survey most and least often? Which economists have the most verbose comments?

This project represents the results of a series on data science in name, theory and practice. I chose this dataset because analyzing it required every step along the data pipeline, from scraping to cleaning to analyzing and visualizing data. Though I could have included a couple more graphs (which are in the code), I decided instead to show only the most interesting ones. First, some summary statistics about the dataset - there are:

  • 132 survey topics between Sept. 2011 and today
  • 195 survey questions (ie. some topics include more than one question)
  • 51 surveyed economists (40 male, 11 female), between 6 and 9 from each university

And now for some charts...

Which economists responded to the survey most and least often?

We can see the majority of economists responded over 80% of the time. Quite a few economists responded to every survey question. There seems to be no pattern between which economists responded and their university affiliation.

Which economists commented most and least frequently?

I expected an inverse relationship between an economist's number of comments and their comment length - as they comment more often (bar height), the length of their comments (bar color) would diminish. However, there seems to be no pattern here - for example Anil Kashyap comments the most often yet is also in the highest quintile in terms of average comment length. Additionally, there is greater variability in comment frequency as compared to survey response rate, likely because including a comment takes more time and effort.

What was the relationship between an economist's average confidence in their responses and the variability of their confidence?

I wasn't sure what I would see here and I'm still not sure too much can be interpreted from this graph. Excluding (arbitrarily) the outliers on the left and the right, there appears to be an inverse relationship between mean confidence and variability of confidence. For example, as one becomes more confident in her responses, she will generally use a more narrow range to report her confidence.

And finally, is there a difference between responses of economists at freshwater (Chicago) and saltwater (Harvard) universities?

Here, I only sampled the 30 most recent survey topics to not overcrowd the visualization. From this graph (and others, such as plotting all universities), it looks like the survey questions are fairly uncontroversial. Most often, economists from different universities agree with each other, and on topics where they disagreed, it was often due to small sample size (eg. one economist disagreeing with another). At least from visual inspection, it doesn't look like there is much disagreement between the economic schools of thought on major public policy issues.


If you found these graphs interesting, feel free to explore the data on your own! The Python pickle file is included along with the code to the graphs. In Part 3 of the series on data science, I walk through the data pipeline by example, from extraction to visualization.

The accompanying code and data are on GitHub.