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People in highly digitized jobs earn more, report finds

The more your job is digitizing, the more you are earning and the greater the chances you will get a raise, according to a new report. In a counter-intuitive finding, there is also slightly greater protection from being automated out of a job.

Why it matters: The report released today by the Brookings Institution dramatizes the financial gulf between those working jobs that have undergone heavy computerization, and those that have not, and adds nuance to the story of income stagnation.

Data: Brookings "Digitalization and the American workforce" report; Interactive: Lazaro Gamio / Axios

The chart: Each arrow tracks workers in an occupation and what they earned on average between 2002 and 2016. Big arrows represent occupations where there are a lot of workers (fast food workers, nurses, accountants) and small arrows show more specialized occupations (tour guides, embalmers, astronomers). The colors reflect how digitized the occupation was in 2016. If an arrow is pointing up and to the right (general and operations managers), that means the number of workers in the occupation grew and wages rose. For some low-digital occupations, the arrows are pointing up and to the left. That means more people are working those jobs and earning less over the year.

The background: Brookings' Mark Muro, Sifan Liu, Jacob Whiton and Siddharth Kulkarni looked at 545 occupations that comprise 90% of the U.S. work force, relying on data from the Bureau of Labor Statistics. Here are the mean wages last year:

  • $72,896 for workers in highly digital occupations, such as financial managers and software developers;
  • $48,274 in middle-level digital jobs, such as nurses and mechanics; and
  • $30,393 in low-digital positions, such as personal care aides and construction workers.

And the gulf is growing: In 2002, a 1-point increase in a job's digitalization score, as measured by Brookings, predicted a $166.20 rise in annual income in 2016 dollars. By last year, the number had almost doubled to $292.80.

Read these factoids: The incomes of people in highly digitized occupations grew more than 0.8% a year from 2010 to 2016. Middle-digitization meant 0.3% higher pay. But the income of people in low-digitized jobs actually shrunk by 0.2% a year.

Worse news for lesser-digitized jobholders: Nearly 60% of the tasks in such jobs appear to be susceptible to automation, compared to only around 30% in high-digitized occupations, Brookings said.

  • Digitalization scores have significant and positive effects on real annual wages even when controlling for education level. And the effect is growing. In 2002, a one-point increase in digitalization score predicted a $166.20 (in 2016 dollars) increase in real annual average wages for occupations with the same education requirements. By 2016 this wage premium had almost doubled to $292.80.

Another view: In another new survey, Deloitte found that as time goes on, artificial intelligence will cost more jobs.

Ina Fried Mar 15
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DHL taps crowdsourcing for faster local deliveries

DHL delivery vans in Malaysia
Photo: DHL

Global shipper DHL announced plans for Parcel Metro, a new service to help speed local deliveries, in part through the use of crowdsourced shipping options.

Why it matters: It comes as e-commerce shipments continue to grow, but online retailers face increased pressure from Amazon and others to offer same-day delivery.

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Yejin Choi: Trying to give AI some common sense

A photo of Yejin Choi from the University of Washington and the Allen Institute for Artificial Intelligence.
Photo illustration: Axios Visuals

Artificial intelligence researchers have tried unsuccessfully for decades to give machines the common sense needed to converse with humans and seamlessly navigate our always-changing world. Last month, Paul Allen announced he is investing another $125 million into his Allen Institute for Artificial Intelligence (AI2) in a renewed effort to solve one of the field's grand challenges.

Axios spoke with Yejin Choi, an AI researcher from the University of Washington and AI2 who studies how machines process and generate language. She talked about how they're defining common sense, their approach to the problem and how it's connected to bias.