Nobel Prize for research in global labor markets, using historical data

Claudia Goldin, an economist at Harvard, has won the Nobel Prize in Economics. A big part of her studies are rooted in the collection and analysis of centuries-old data:

Women are vastly underrepresented in the global labour market and, when they work, they earn less than men. Claudia Goldin has trawled the archives and collected over 200 years of data from the US, allowing her to demonstrate how and why gender differences in earnings and employment rates have changed over time.

Goldin showed that female participation in the labour market did not have an upward trend over this entire period, but instead forms a U-shaped curve. The participation of married women decreased with the transition from an agrarian to an industrial society in the early nineteenth century, but then started to increase with the growth of the service sector in the early twentieth century. Goldin explained this pattern as the result of structural change and evolving social norms regarding women’s responsibilities for home and family.

Amazing.

The illustrations by Johan Jarnestad that accompany the announcement are also really useful.

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Outsourced work and generative AI

For Rest of World, Andrew Deck turned the AI focus on outsourced workers, whose jobs have been directly affected as of late and will probably shift much more. Deck profiled and commissioned four workers to make things without AI and with:

For more than seven years, Santiago Bautista González worked full time selling his cartoon-style illustrations, using the freelance gig marketplace Fiverr. His income, around $1,500 in a good month, dropped by a third this past January. February was equally disappointing.

In search of an explanation, Bautista, 31, read about the growing popularity of visual generative AI software. He found that Fiverr had added a section for AI artists. “And I say, ‘Well, maybe it’s because of this,'” he told Rest of World.

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More hiring, because more quitting

This is a good example of things are not quite what they seem until you look at more data. Andrew Van Dam, for Washington Post’s Department of Data, looks into why it appears red states hire more than blue states.

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How teenagers’ job ambitions have changed

The Programme for International Student Assessment (PISA), run by the OECD since 2000, surveys teenage students to estimate the quality of education around the world. One of the questions asked: “What kind of job do you expect to have when you are about 30 years old?” For Vox, Alvin Chang walks through how the responses changed over the past two decades, which appears to suggest that students are less certain about what the future holds.

There are some tricky spots in explaining misalignment between ambition and preparation, but Chang does a good job of moving along step-by-step.

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How much AI will affect your job

Research by Edward W. Felten, Manav Raj, and Robert Seamans provides estimates for how occupations will be impacted by artificial intelligence, such as ChatGPT and Midjourney, based on AI exposure and demographics. Yan Wu and Sergio Peçanha, for The Washington Post, provide a rundown and searchable charts for the work so that you can check your own occupation.

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Declining vacation time from work

Despite available vacation days, it appears that American workers are taking less and less vacation. Andrew Van Dam, for The Washington Post’s Department of Data, has the demographic breakdowns:

It does not seem to be a matter of vacation-day supply. It is true that the United States is the only advanced economy without guaranteed paid vacation. However, BLS data on employee benefits suggests that more than 90 percent of full-time, private-industry workers have access to paid vacation time, a figure that has remained relatively steady for decades. And the number of paid vacation days offered by the typical employer has ticked up in recent years.

So we looked instead at vacation-day demand: Who uses the most? Has that changed?

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Data Visualization State of the Industry, 2022

The 2022 results from the State of the Industry survey, run by the Data Visualization Society, are out. Among 1,218 respondents, see the roles, the salaries, and the responsibilities:

The overall median of the annual compensation graphs is at $60,000 to $79,999 per year, with very few respondents reporting over $159,000 per year, and a small but notable increase in the number of respondents reporting annual compensation in the $240,000 per year or more category.

I’d have to look at the actual data, which you can get for this year and previous, but my hunch that the split distribution in salary is between non-tech and tech workers.

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Tom Brady of other jobs

Tom Brady, the quarterback for the Tampa Bay Buccaneers, is 45 years old, which makes him the oldest player in the National Football League. Francesca Paris, for NYT’s The Upshot, places Brady’s age under the perspective of other occupations. For example, Lilian Thomas Burwell, who is an artist at 95 years old, is well in the upper percentile for those in her field (and the general population).

See also: the distributions of age and occupation.

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Tracked while reading about being tracked at work

While reading this NYT article, by Jodi Kantor and Arya Sundaram, on the drawbacks of activity and time tracking for work, the article itself tracks your reading behavior. You see counters for the time you spend reading and scrolling, clicks, keystrokes, idle time, and active time. It comes complete with snippy comments and a final grade — and a bitter taste for productivity tracking.

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Time splits from a visualization freelancer

Eli Holder shows how he split his freelance time across various projects and categories. With visualization work, a lot of your time is spent doing non-visualization things:

As expected, at 16 percent, data wrangling and analysis takes a significant chunk of total time. This includes data prep, which I’ve categorized as fairly mindless data engineering or spreadsheet maneuvering (nine percent) or data pulls (three percent). More interesting data work was more fragmented: ~two percent of the time was exploratory analysis (e.g., for storytelling), ~one percent of the time was spent designing metrics (e.g., exploring different calculations that might best tell a given story) and another one percent was creating mock datasets (e.g., to compensate for data security constraints or clients who are slow to provide real data).

I don’t track my time with FlowingData, but if I were to guess, I spend at least half my time on analysis and wrangling. If you consider the many potential visualization projects that I scrapped because nothing panned out in analysis, that analysis/wrangling percentage goes up a lot more.

Sometimes you gotta dig a lot before you find anything worth showing.

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