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<!DOCTYPE html>
<html lang="en">
<head>
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<meta charset="utf-8" />
<title>Who's Susceptable?</title>
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<body>
<!-- Title of the page -->
<section class="wrapper style1 inner block-map top-header">
<h1>Who's Susceptible To The Automation Boom?</h1>
<h4>By Lucas Kitzmueller & Bhavik Nagda | May, 2021</h4>
<h3>The last few decades have witnessed rapid advances in automation technology amidst growing anxiety about
the
labor market. As emerging technologies mature and gain commercial traction, concerns of jobs
displacement
will
only heighten. New technologies create winners and losers. They improve employment opportunities for
some
workers but lower the demand for others. Scroll down to learn more about the impacts of automation on
certain
economies, geographies, and demographics.
</h3>
</section>
<section class="wrapper style1 inner block-map linechart">
<h2>A Brief History of Automation Risk</h2>
<div id="linechart-box"></div>
<div id="linechart-year"></div>
</section>
<section class="linechart story" id="linechart-step2">
In the last 30 years, it was primarily workers in middle-skilled occupations in production and
administration
who found themselves on the wrong side of the demand curve. These jobs have in common that they require
challenging, yet at the end of the day, repetitive routine tasks that can be automated.
</section>
<section class="linechart story" id="linechart-step3">
For example, industrial robots reduced the need for factory workers and computers have reduced the
demand
for
secretaries.
</section>
<section class="linechart story" id="linechart-step4">
While employment in middle-skill occupations was declining, high-skill occupations such as managers and
professionals were growing . These occupations rely on cognitive non-routine
tasks,
which
to date have been proven hard to automate.
</section>
<section class="linechart story" id="linechart-step5">
Similarly, low-skill occupations with a large share of manual routine tasks <a
href="https://workofthefuture.mit.edu/research-post/the-work-of-the-future-shaping-technology-and-institutions/">have
been growing. </a> This includes,
for example, workers in personal care services.
</section>
<section class="linechart story" id="linechart-step6">
</section>
<section class="text" id="text">
<h2>The Data</h2>
<br>
However, as the commercial adoption of AI is still nascent in many industries, it is still unclear what
workers
will be
affected by AI. While there exist many studies on the labor market impact of robot adoption, there are
no
comparable
studies for AI technologies.
Fortunately, the Stanford economist Michael Webb has <a href="https://www.michaelwebb.co/webb_ai.pdf"> developed
a clever way </a> to measure future exposure
to AI
technologies. His idea is the following: The text of AI-based patents contains information about what
technologies do,
and the text of job descriptions compiled by the Department of Labor contains information about the
tasks
people
do in
their jobs. Combining these two datasets, we can quantify what jobs AI may soon be capable of performing
at
the
workplace.
For example, one of the tasks of doctors is “Interpret tests to diagnose patient’s condition”. From
these
task
descriptions, Webb extracts verb-object pairs. In this case, the pairs would be (interpret, test) and
(diagnose,
condition). To then measure this task’s exposure to AI, Webb calculates the frequency of these
verb-object
pairs
in the
titles of all AI-based patents. With this method, he is able to rank 964 occupations in the database
according
to their
exposure to AI technology. To contrast the impact of AI with past technological shocks, Webb also
calculates
the
exposure to robots and software technologies.
<br>
<br>
<div class="image">
<img src="https://github.com/6859-sp21/final-project-impact-of-automation-on-labor-markets/blob/main/data/webb.png?raw=true"
alt="Michael Webb's Process">
</div>
</section>
<section class="wrapper style1 inner block-map">
<h2>Does Education Affect Automation Risk?</h2>
<select id="selectButton"></select>
</section>
<section class="ridgeline story start-story" id="ridgeline-step2">
For this analysis, we grouped occupations by their typical entry level education as determined by the
Bureau
of
Labor
Statistics. The graph shows distribution of Webb’s exposure estimates within each educational group.
Occupations
are
weighted by size (i.e., the number of workers employed in the occupation in 2019).
</section>
<section class="wrapper style1 inner ridgeline">
<!-- Create a div where the graph will take place -->
<div id="my_dataviz"></div>
</section>
<section class="ridgeline story" id="ridgeline-step3">
The data for robot exposure confirm the earlier finding: less-educated workers, typically employed in
jobs
with
a high
share of manual routine tasks, are more exposed to disruption from robotics than higher-educated
workers.
</section>
<section class="ridgeline story" id="ridgeline-step4">
The exposure estimates for AI, however, reveal an opposite pattern. Some lower-skill occupations, such
as
power
plant
operators and dispatchers, are still highly exposed. But many highly educated workers will be impacted
too,
such
as
clinical laboratory technicians, chemical engineers, and optometrists. In fact, workers with a
bachelor’s
degree
appear
to be exposed the most.
</section>
<section class="ridgeline story" id="ridgeline-step5">
</section>
<section class="wrapper style1 inner block-map map">
<h2>The Geographical Distribution of Automation Risk</h2>
<div class="toggle-data-buttons" id="toggle-data-buttons">
<button type="button" autocomplete="off" class="btn btn-default btn-primary" data-index="webb_pct_software"
id="software-map">Software
Automation Risk</button>
<button type="button" autocomplete="off" class="btn btn-default" data-index="webb_pct_robot"
id="robot-map">Robot
Automation Risk</button>
<button type="button" autocomplete="off" class="btn btn-default" data-index="webb_pct_ai" id="ai-map">AI
Automation
Risk</button>
</div>
<div id="custom-tooltip">
<div id="custom-tooltip-legend" class="legend legend-horizontal legend-scale"></div>
<h3 id="title-map">Risk Percentile for Robot Exposure</h3>
</div>
<div id="custom-tooltip-toolbox" class="squaire-toolbox"></div>
</section>
<section class="map story" id="map-step2">
Worker displacement represents a challenge for policymakers, especially if it is concentrated in few
labor
markets. The
map shows the exposure of states to different technologies, based on the occupational composition of
their
workforce as
calculated from the <a href="https://www.census.gov/programs-surveys/cps.html">Current Population Survey.</a>
</section>
<section class="map story" id="map-step3">
As expected, states involved in manufacturing, such as Wisconsin or Kentucky, are heavily impacted by
robotics.
</section>
<section class="map story" id="map-step4">
However, as AI is increasingly linked to production, the states involved in manufacturing appear to be
also
disproportionately exposed to AI. At the same time, states with a large high-tech sector and managerial
workforce, e.g.,
Washington DC, Boston, and Washington state, also have high exposure to AI technologies.
</section>
<section class="map story" id="map-step5">
These findings suggest that this time will be different: while past automation shocks primarily affected
lower
and
middle-skilled workers, AI is set to displace higher-skilled workers. And while increases in robots
primarily
impact
regions intensive in manufacturing, the impact of AI will be felt more widely across the US.
</section>
<section class="map story" id="map-step6">
</section>
<section class="wrapper style1 inner block-map" id="bottomSources">
<h5>Sources: Data from <a href="https://www.michaelwebb.co/webb_ai.pdf">Michael Webb's Paper </a> and the <a
href="https://fredblog.stlouisfed.org/2016/04/job-polarization/"> St. Louis Fed </a> </h5> <br>
<h5>Thank you to the 6.859 course staff for a wonderful semester!</h5>
</section>
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