Project description

Unemployment in the United States is near the lowest level since the 1960s. But how do you know whether you’re benefiting from today’s strong labor market? This interactive tool asks readers to input their own ages, occupations, educational backgrounds, location types and annual wages, then generates graphics and text that tell them how their wages compare with typical wages of similar workers.

What makes this project innovative?

Most economics stories focused on wages tend to give broad measures of losses and gains by citing national medians. This tool provides a way to explore occupation-specific data, cross-cut with data on age and location type. There are multiple entry points into the charts. For example, a user need not fill out every form input to get results; she could instead enter only her occupation and discover national medians for just her occupation. Or she could enter nothing at all and still find national statistics.

What was the impact of your project? How did you measure it?

A top metric in our newsroom is how many people signed up for a subscription after viewing an article. This project drove a high number of subscription orders — in the 99th percentile of wsj.com articles, according to our analytics team. Leadership called the project an “addictive interactive tool, which readers are loving.” A commenter wrote, “It's pretty interesting as a 24 year old engineer, I am behind in all categories except wages compared to my age group. Just shows that separating a question like this into these groups can create some interesting conclusions.“

Source and methodology

We explored different ways of getting cross-sections of data, including previously published Labor Department tables and IPUMS USA’s online data analysis system, and finally decided to run a custom analysis using the most recent three years of American Community Survey microdata via IPUMS USA. We downloaded the microdata and isolated demographic factors by occupation to determine what would be the most valuable information for a reader. Some occupations had too few survey responses to provide a meaningful assessment once cut up by age group, education or location, so we declared a minimum threshold of respondents per category. Results were discussed with our in-house demographics expert. Another colleague, familiar with Python, reviewed and replicated the analysis.

Technologies Used

Data analysis: Jupyter Python notebook using the pandas and weightedcalcs libraries Online presentation: D3 and SelectWoo (accessible, customizable select boxes)

Project members

By Soo Oh Contribution Joel Eastwood, Paul Overberg, Tyler Paige and Hanna Sender

Link

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