Race and Occupation

About 22 percent of physicians in the United States are Asian, but Asian people only make up about 6 percent of the full working population. Compare the former to the latter, and you could say that Asian people are about 3.5 times more likely to be physicians.

Are there other jobs that jump out? What’s it like for other races and ethnicity?

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Commonness of Races in Different Occupations

Some jobs are worked commonly by people of a certain race or ethnicity more than others. Farm managers are almost all white, postal service processors are half black, manicurists are 65% Asian, and drywall installers are 75% Hispanic. This chart shows the percentage of employed persons 16 years and older who are a given race or ethnicity for each job. It’s based on 2023 data from the Bureau of Labor Statistics.

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Fields of Study and the Jobs of Young People with Higher Incomes

Income tends to increase with age, because more work experience and education tends to lead to higher paying jobs. However, young people can also earn higher incomes. Using data from the most recent 2022 American Community Survey, let’s see what those people studied and what they do for a living.

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Jobs with Higher Income and Fewer Hours

About 15% of working Americans make at least $100,000 of income per year as of the 2021 American Community Survey. As you’d expect, many who fall in that 15% spent more years in school and spend more hours at work.

But you can also earn a six-figure income without working all the time. What about those people? What do they do?

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Most Common Jobs, By Income Group

What jobs typically pay over $200,000 in annual salary? What about jobs that pay at least six-figure incomes? These are income ranges for the ten most common jobs at different income levels.

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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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