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Synopsis

Dr. Elise Gould, senior economist at the Economic Policy Institute, joins Rachel and Dan to pull back the curtain on how economic data is collected, revised, and communicated — and how that process is vulnerable to political manipulation. We talk about the federal statistical agencies that produce employment and wage data, the role of transparency and revision in maintaining trust, and the pressure those institutions are under today. Rachel and Dan close with two lenses: Human vs. System Integrity and Maybe.

Interview

• Dr. Elise Gould — senior economist, Economic Policy Institute
• Economic Policy Institute — nonpartisan think tank focused on employment, wages, and inequality
• EPI Microdata — EPI's publicly accessible economic microdata site
• State of Working America Data Library — EPI's public database of wages, employment, and inequality data
• EPI Data Accountability Dashboard — EPI's tool for tracking parallel measures and monitoring changes to federal data
• Bureau of Labor Statistics — federal agency that produces jobs and unemployment data
• Philosophy Minis — Jonny Thomson's Instagram account featuring short philosophical vignettes (referenced by Dan in the lenses segment)

Lenses

Lens 1: Human vs. System Integrity

Information systems rely on some combination of built-in mechanisms and individual actors — whistleblowers, researchers, editors, external stakeholders — to maintain the integrity of their data. This lens asks where that responsibility actually lives.

• What mechanisms are built into the system for detecting anomalies or integrity issues?
• How much does the system rely on humans to address failures of integrity or reliability?
• When there are gaps, inconsistencies, or suspicious patterns in the data, whose job is it to surface them?
• What would it take for an integrity failure to go unnoticed — and how much of that risk has the system actually designed against?

Lens 2: Maybe

Drawn from the parable of the lost horse, this lens challenges the impulse to frame information as inherently good or bad news. Data systems — like dashboards and reports — routinely signal conclusions for users, even with incomplete context.

• When presenting data as either good or bad, does the system also present the sufficient context to explain why?
• How does the system signal urgency or alarm — and how are users empowered to specify the rules of urgency?
• What tools does the system offer users to reach their own conclusions without pushing them to a predetermined frame?

Edited by Jared Landis (https://www.landispodcastediting.com/)

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Personnel

• Dan Brown, Host
• Rachel Price, Host

Music

• Turtle Up Fool, by Elliot

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Unchecked is a production of Curious Squid

Curious Squid is a digital design consulting firm specializing in information architecture, user experience, and product design