Andrew's Contributions

Andrew's Contributions

Publication Date: 04/09/2015



Best individual portfolio. 1) Beyond the headline number: Which jobs are gaining ground, and which are falling behind? My contribution to the WSJ's jobs market tracker doubled as my first project in D3.js. The U.S. federal government releases a wealth of jobs data that's swamped by the headline number, and this was my attempt to dig deeper into the numbers and surface detailed data in a delightful, friendly manner. 2) Moving targets: How the Fed's interest-rate target shifted as their economic predictions changed The Federal reserve releases their data in the least friendly, most inconsistent set of formats possible, and thus nobody had ever taken the time to compile their entire prediction history in one place. Taken all together, it paints a striking portrait of the Federal Open Market Committee's tendencies, and how it may impact any future plans to raise the interest-rate target. 3) The path to growth: How GDP came to grow at a 4.0% pace Buried within each month's GDP release is a detailed contribution table, which breaks the economy down into dozens of sectors and assess how they added or subtracted to the economy in a given month. After re-sorting the sectors into coherent units, I sketched until I found a chartform that would adequately illustrate the push and pull of all the components of the American economy. 4) On second, third or fourth thought: Revisions to GDP New GDP data always makes headlines, but reporters never stop to remember that they're just looking at a preliminary estimate. Here, we looked to bring some accountability and context to the revision process by cataloguing official revisions over the past few years. Many of them bear little relation to the final number. 5) Rural job blues: How Wabash, Indiana illustrates the nation's non-metro malaise The simplicity of that first line chart belies the magnitude of the data assembly required. To get the headline number showing the decline in rural employment, I had to sift through hundreds upon hundreds of megabytes of county-level data, match it with accepted classifications, and re-aggregate everything in a meaningful yet credible fashion. 6) Shaking up Sectors: Changes in the job market and what it means for wages Wage data is incredibly seductive, but also incredibly finicky. Here, I dealt with a challenging dataset by breaking it into ordered sectors and placing it within the broader context of the economy. Are lower-wage jobs really replacing high-wage ones? What high-wage jobs still have room for growth? Questions that would typically take an hours-long data dive can be answered at a glance.

Technologies used for this project:

For design, I primarily used Adobe Illustrator and the d3.js Javascript library. Many of my static charts were also built in d3, and then exported as SVGs and styled in Illustrator. For data analysis, I used a mix of SQL, Open Refine and good old MS Excel.
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