About The Project

The economic literature on inequality extends almost back to foundation of the academy. In a lot of respects the question of distribution, and by extension inequality, remains a central question of economics. Additionally, the conversation of income and wealth concentration has been foregrounded in the broader public discussion. This research sought to compare the most contemporary estimates of wealth and income concentration, by the leading economic scholars in that field.

This work was undertaken by three separate policy researchers: Fatima Omar, Ishan Nagpal, and Mark Sheppard, from the University of Chicago, Harris School of Public Policy, under the supervision of Professor Jeffrey A. Levy .

The modern empirical literature on inequality somewhat begins with Atkinson , Stiglitz , and Galbraith . However, seminal research and more recent work by Piketty & Saez , and by extension Piketty, Saez, & Zucman have expanded upon distributional implications, by offering more precise estimates at the percentile level of upper incomes, which has only previously been studied in deciles. That research rose prominence following the Great Recession, however since that time additional estimates have been offered by Auten & Splinter, and more recently, Smith, Yagan, Zidar & Zwick.

Generally the work of Piketty, Saez, & Zucman is often critiqued with the work of Auten & Splinter, or with Smith, Yagan, Zidar & Zwick. That being recognized this project attempted to unpack how different these estimates were from one another.

Visualizations

Project Detail

This research used several statistical methods and programming languages to compare the underlying research:

• The research primary utilized open source software, particularly Python.
• Leveraging various programming packages to source and illustrate data.
• Cleaning and identifying overlapping estimates across different researchers.
• Visualizing that data in an interactive format.
• Selecting authors in the key will highlight the respective data.

Conclusion

In order to visualize this research, this project actively scrapes, cleans and merges data from multiple sources, then uses a live Python file and Jupyter Notebook to diplay a Bokeh plot within a Github Pages enviroment. These acadmic debates surrounding point estimations have important downstream implications for policy, however those disagreements are often hard to visualize for the broader public. By emphasing both the statistics and the visualization this work hopes to illuminate the public disussion on this topic.