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Mapping the Lowest & Highest Automation Potential of White-Collar Employment in the U.S.

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As responses to the COVID-19 pandemic have accelerated digital adaptation by several years, the ongoing conversation on the complexities of job automation naturally returns to the fore. First, it is important to note that the automation discourse primarily refers to the automation of tasks and not the entirety of a job. Almost all existing occupations consist of a complex mix of interdependent tasks and interactions. As such, while it is possible to theoretically generalize that occupations consisting mainly of routine repetitive tasks that follow set procedures can easily be performed by sophisticated algorithms, it is essential to note that very few jobs consist entirely of such tasks.

The task we set out for this article was to contribute to the larger conversation by gauging the extent of automation compatibility for white-collar occupations within the broader landscape of U.S. employment. Specifically, by combining 2020 data released by the U.S. Bureau of Labor Statistics (BLS) with automation potential estimates calculated by WillRobotsTakeMyJob.com, our analysis offers a national overview, and also paints a broad picture of employment computerization potential in each state.

Key Takeaways

  • U.S. overview: 60% of white-collar jobs (86% of all U.S. jobs) are less than 50% compatible with automation.
  • The least-automatable white-collar U.S. jobs are in education, healthcare, counseling, arts, science and engineering occupations.
  • Washington D.C.; Massachusetts; and Connecticut are home to the highest shares of white-collar jobs, based on their total state employment.
  • The lowest concentrations of white-collar jobs are in Nevada, Louisiana, Mississippi and Wyoming.
  • The largest shares of above-average automatable jobs are in Puerto Rico, Arizona and South Dakota.
  • The smallest share of white-collar jobs with at least 50% automation compatibility are in Arkansas; Mississippi; Indiana; Wyoming; Washington, D.C.; and Louisiana.

Even with the current advanced state of technological innovation, the mix of tasks performed during almost any given job remains too complex to allow for an accurate division of automatable and non-automatable jobs. Moreover, for most existing occupations, it’s difficult to draw a clear line between how much of a job’s inherent tasks are easily automatable at this time — or even what “easily automatable” itself really means. To that end, any sufficiently sophisticated algorithm would need to have a comprehensive grasp of both practical and moral aspects that govern the decision-making process in any given situation. And, while research into such technology continues on an ever-expanding scale, that level of understanding remains beyond reach.

So far, automation has been mostly integrated with hard, manual labor occupations, which has spared many from grueling, physically taxing and, in some cases, exploitative work. And while there is no denying the economic hardships faced by displaced workers, it is equally important to note that such consequences would be offset by the existence of easily accessible continuous training programs that are designed and funded to keep the workforce flexible and adaptable to the ever-faster churning of skills in a rapidly progressing economy.

Our previously published analysis on the fastest-growing jobs in the U.S. found that business and healthcare occupations nearly doubled and tripled, respectively, during the previous decade. More precisely, jobs for personal care aides increased 251% during the 10 years leading up to 2019 — a trend that was only expected to continue as more of the country’s aging population retires. And, while bedside robots are unlikely to replace the human healthcare workforce any time soon, there are certain tasks that can be handled by non-human work participants. For instance, one recent example looks to provide increased efficiency of care, along with a greater degree of independence, via telecare and care robotics used for at-home safety and health monitoring. Clearly, COVID restrictions during lockdown have shown how crucial a system like telemedicine can be in ensuring proper and timely care to all who need it, especially when health workers are stretched too thin.

However, this process is not limited to what we broadly refer to as blue-collar jobs. As automation research and development continues, the repetitive and tedious tasks of office-using, white-collar occupations are first up for an increase in efficiency — which frees up the human workforce for more complex professional activities. As such, we focused our current analysis on office-using occupations and looked at: which white-collar jobs in the U.S. were most compatible with task automation; which states were home to the most-susceptible occupations at this time; and where the least-compatible jobs accounted for most of the total employment in the state.

See our methodology section for more details on the data we analyzed.

Less Than Half of U.S. White-Collar Jobs Are at Least 50% Compatible with Task Automation

Automating menial and tedious tasks frees up workers’ resources, which can then be better invested into more complex activities. In this way, automation can be a much-needed helping hand in any industry. According to BLS data, the white-collar occupations included in this analysis accounted for roughly 35% of the nearly 140 million jobs in the U.S. in 2020. Of that share, about 19.4 million jobs were estimated to be at least 50% compatible with automation. In other words, at least 50% of the tasks pertaining to each of these occupations could be automated to some extent. Per the most recent employment data, this represents 40% of white-collar jobs and just 14% of all U.S. jobs.

U.S. Occupations That Are Least Compatible with Task Automation

The lowest task-automation potential — when none (or close to none) of the aspects of the job are automatable — is mostly associated with occupations that are also essential to the functional fabric of society. In particular, high-complexity jobs in arts, education, healthcare, counseling, social work, and earth science and engineering are less than 5% compatible with task automation. Jobs in these occupations represent nearly 17% of white-collar employment and only 5.35% of all U.S. jobs.

At 0% task-automation potential, we found secondary school career/technical education teachers, along with postsecondary nursing instructors and teachers. The former accounted for 73,530 jobs last year, which translates into 0.17% of white-collar jobs and 0.05% of all U.S. employment. Similarly, there were about 61,100 jobs filled for nursing instructors and teachers in 2020, accounting for 0.14% of all white-collar employment and 0.04% of all jobs in the country.



Postsecondary art, drama and music teachers (1% automation compatibility) added up to 91,170 jobs in 2020, which accounted for 0.21% of white-collar jobs and 0.07% of total employment. There were also 31,790 lodging managers across the U.S. last year, making this the smallest cohort among the five occupations highlighted here. Also coming in at 1% task-automation compatibility, lodging managers accounted for 0.07% of white-collar jobs in the U.S. last year, which equated to 0.02% of all of the employment in the country.

Conversely, preschool teachers (except special education) made up the largest group of the five lowest-compatibility occupations. In 2020, there were 370,940 jobs filled for this occupation in the U.S., which represented 0.84% of white-collar employment and 0.27% of all U.S. jobs. According to the WillRobotsTakeMyJob score, roughly 2% of tasks associated with this occupation would be compatible with automation.

The other eight occupations to be estimated at 2% task-automation compatibility include clergy, conservation and soil & plant scientists, special education teachers, and counselors. These occupations account for a combined total of 1.91% of white-collar jobs and 0.6% of all U.S. employment.

Search the table below for similar data on all occupations included in our study, and read on for select highlights and occupation data for each state.

U.S. Occupations Most Compatible with Task Automation

On the opposite end of the spectrum lie the occupations in which more than 95% of associated tasks could be automated. Often, these are aspects such as clerical and administrative tasks, as well as sampling, measurements and logistics management. The five occupations that topped this list accounted for a combined total of roughly 0.24% of total U.S. employment: Procurement clerks; library technicians; tax preparers; cargo and freight agents; and agricultural and food science technicians each scored a nearly perfect 10 for automation compatibility. Roughly 97% of tasks associated with each of these five occupations could be computerized.



And, although the dispersion of these jobs across the U.S. will vary, what we can look at is how much each of them represents within the total employment. For instance, last year, roughly 96,510 cargo and freight agents were employed across the country, comprising 0.22% of white-collar jobs and 0.07% of the total U.S. employment. Second in line by number of jobs were library technicians: BLS data showed 89,070 jobs filled for this occupation in 2020, which amounted to 0.20% of white-collar employees and 0.06% of all U.S. workers. Next, tax preparers numbered around 62,600 last year, rounding out to 0.14% of U.S. white-collar jobs and 0.05% of total employment. At the same time, procurement clerks working in the U.S. accounted for 0.14% of white-collar employment and 0.04% of total jobs in the country. Finally, in 2020, there were 21,940 agricultural and food science technicians working across the U.S., which represented 0.05% of white-collar jobs and 0.02% of all jobs in the country.

Puerto Rico, Arizona & South Dakota: Highest Share of Jobs with Above-Average Automation Potential

The number of jobs for any one occupation can vary widely from one state to another. Plus, these totals often depend on factors like total population, predominant industries, and the overall health and complexity of the business environment.

On average, white-collar jobs account for roughly 34% of the total jobs in each state. Last year, the three highest concentrations were in Washington D.C., where 49% of all jobs were white-collar; Massachusetts with 42%; and Connecticut and New York, which shared the third spot with 39% each. Meanwhile, Nevada was home to the smallest share of white-collar jobs — just 27% of the total jobs in the state. It was followed by Louisiana, Mississippi and Wyoming, with 28% each, and Indiana with 29%.


However, when we zoom in on white-collar occupations with above-average automation potential, the more detailed picture features different book ends. In this scenario, the smallest share of white-collar jobs that are at least 50% compatible with task automation stood at 12%. Albeit with variation in the actual number of jobs, this was the case in Arkansas; Louisiana; Mississippi; Indiana; Washington, D.C.; and Wyoming.

Meanwhile, the highest percentage of above-average automation potential jobs among all the administrative divisions we analyzed was in Puerto Rico. Here, white-collar jobs accounted for 35% of total employment in 2020, 17% of which was in occupations that are at least 50% compatible with task automation. Not far behind, Arizona and South Dakota followed at 16% each.

Expand any item in the list below for highlights and full data sets on the automation compatibility of employment in each state and territory. Our analysis covered data for all states in the continental U.S., as well as Washington D.C., Hawaii, and Puerto Rico.

The five office-using occupations that were most compatible with task automation in Alabama in 2020 were library technician; cargo and freight agent; tax preparer; procurement clerk; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); soil and plant scientist (2%); and conservation scientist (2%).

The least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); clergy (2%); and middle school special education teacher (2%). Meanwhile, the five office-using occupations that were most compatible with task automation in Alaska in 2020 were cargo and freight agent (97%); library technician (97%); procurement clerk (97%); general office clerk (95%); and legal secretary and administrative assistant (95%).

The five office-using occupations that were least compatible with task automation in Arizona in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%). Meanwhile, the highest compatibility in the state was for procurement clerk; cargo and freight agent; tax preparer; library technician; and agricultural and food science technician, each with 97% compatibility.

The five office-using occupations that were least compatible with task automation in Arkansas in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%). Meanwhile, the most-compatible occupations in the state were cargo and freight agent; procurement clerk; library technician; tax preparer; and agricultural and food science technician, each with 97% compatibility.

The five office-using occupations that were most compatible with task automation in California in 2020 were library technician; cargo and freight agent; tax preparer; procurement clerk; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were least compatible with task automation in Colorado in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%). Meanwhile, the highest task-compatibility occupations in the state were library technician; cargo and freight agent; tax preparer; procurement clerk; and agricultural and food science technician, each with 97% compatibility.

The five office-using occupations that were most compatible with task automation in Connecticut in 2020 were library technician; procurement clerk; cargo and freight agent; tax preparer; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations most compatible with task automation in Delaware in 2020 were tax preparer; library technician; procurement clerk; cargo and freight agent; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); clergy (2%); and conservation scientist (2%).

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The five office-using occupations that were most compatible with task automation in Florida in 2020 were cargo and freight agent; tax preparer; library technician; procurement clerk; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Georgia in 2020 were cargo and freight agent; procurement clerk; library technician; tax preparer; and agricultural and food science technician, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were least compatible with task automation in Hawaii in 2020 were postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); soil and plant scientist (2%); and conservation scientist (2%). Meanwhile, the most-compatible occupations in the state were cargo and freight agent; procurement clerk; tax preparer; library technician; and agricultural and food science technician, each with 97% compatibility.

The five office-using occupations most compatible with task automation in Idaho in 2020 were tax preparer; library technician; procurement clerk; agricultural and food science technician; and cargo and freight agent, each with 97% compatibility. Meanwhile, the least-compatible occupations in the state were postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); middle school special education teacher (2%); and clergy (2%).

The five office-using occupations that were least compatible with task automation in Illinois in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%). Meanwhile, the highest task automation compatibility occupations in the state were cargo and freight agent; library technician; tax preparer; procurement clerk; and agricultural and food science technician, each with 97% compatibility.

The five office-using occupations that were most compatible with task automation in Indiana in 2020 were cargo and freight agent; library technician; tax preparer; procurement clerk; and agricultural and food science technician, with each occupation presenting 97% automation compatibility. On the other end of the spectrum, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Iowa in 2020 were library technician; agricultural and food science technician; cargo and freight agent; procurement clerk; and tax preparer, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); clergy (2%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Kansas in 2020 were library technician; procurement clerk; agricultural and food science technician; cargo and freight agent; and tax preparer, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Kentucky in 2020 were cargo and freight agent; tax preparer; library technician; procurement clerk; and agricultural and food science technician, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Louisiana in 2020 were library technician; procurement clerk; tax preparer; cargo and freight agent; and agricultural and food science technician, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Maine in 2020 were library technician; procurement clerk; tax preparer; cargo and freight agent; and agricultural and food science technician, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Maryland in 2020 were tax preparer; library technician; cargo and freight agent; procurement clerk; and agricultural and food science technician, each at 97% automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Massachusetts in 2020 were library technician; cargo and freight agent; procurement clerk; tax preparer; and agricultural and food science technician, each at 97% task automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); conservation scientist (2%); and soil and plant scientist (2%).

The five office-using occupations that were least compatible with task automation in Michigan in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%). Meanwhile, the most-compatible occupations in the state were cargo and freight agent; library technician; procurement clerk; tax preparer; and agricultural and food science technician, each at 97% task automation compatibility.

The five office-using occupations that were most compatible with task automation in Minnesota in 2020 were tax preparer; cargo and freight agent; library technician; procurement clerk; and agricultural and food science technician, each at 97% task automation compatibility. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Mississippi in 2020 were procurement clerk; library technician; tax preparer; cargo and freight agent; and agricultural and food science technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Missouri in 2020 were cargo and freight agent; procurement clerk; tax preparer; agricultural and food science technician; and library technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Montana in 2020 were library technician; cargo and freight agent; tax preparer; procurement clerk; and agricultural and food science technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Nebraska in 2020 were agricultural and food science technician; tax preparer; cargo and freight agent; procurement clerk; and library technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Nevada in 2020 were cargo and freight agent; tax preparer; library technician; procurement clerk; and agricultural and food science technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in New Hampshire in 2020 were library technician; cargo and freight agent; tax preparer; and procurement clerk, each at 97% compatibility with task automation, as well as general office clerk (95%). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in New Jersey in 2020 were procurement clerk; cargo and freight agent; library technician; tax preparer; and agricultural and food science technician, each at 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in New Mexico in 2020 were library technician; procurement clerk; tax preparer; cargo and freight agent; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and soil and plant scientist (2%).

The five office-using occupations that were least compatible with task automation in New York in 2020 were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%). Meanwhile, the most-compatible occupations in the state were library technician; cargo and freight agent; tax preparer; procurement clerk; and agricultural and food science technician, each with 97% compatibility with task automation.

The five office-using occupations that were most compatible with task automation in North Carolina in 2020 were cargo and freight agent; tax preparer; library technician; procurement clerk; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in North Dakota in 2020 were library technician; cargo and freight agent; tax preparer; agricultural and food science technician; and procurement clerk, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and clergy (2%).

The five office-using occupations that were most compatible with task automation in Ohio in 2020 were library technician; procurement clerk; cargo and freight agent; tax preparer; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Oklahoma in 2020 were procurement clerk; tax preparer; library technician; cargo and freight agent; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Oregon in 2020 were library technician; tax preparer; cargo and freight agent; agricultural and food science technician; and procurement clerk, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Pennsylvania in 2020 were procurement clerk; tax preparer; cargo and freight agent; library technician; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Puerto Rico in 2020 were procurement clerk; library technician; tax preparer; cargo and freight agent; and agricultural and food science technician, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and clergy (2%).

The five office-using occupations that were most compatible with task automation in Rhode Island in 2020 were procurement clerk; tax preparer; cargo and freight agent; and library technician, each with 97% compatibility with task automation, as well as general office clerk (95%). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and middle school special education teacher (2%).

The five office-using occupations that were most compatible with task automation in South Carolina in 2020 were cargo and freight agent; library technician; procurement clerk; tax preparer; and agricultural and food science technician (97% compatibility each). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in South Dakota in 2020 were library technician; agricultural and food science technician; tax preparer; cargo and freight agent; and procurement clerk (97% compatibility each). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and clergy (2%).

The five office-using occupations that were most compatible with task automation in Tennessee in 2020 were tax preparer; cargo and freight agent; procurement clerk; library technician; and agricultural and food science technician (97% compatibility each). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Texas in 2020 were cargo and freight agent; procurement clerk; tax preparer; library technician; and agricultural and food science technician, with 97% compatibility each. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Utah in 2020 were tax preparer; cargo and freight agent; procurement clerk; library technician; and agricultural and food science technician, with 97% compatibility each. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in Vermont in 2020 were library technician; agricultural and food science technician; tax preparer; procurement clerk; and cargo and freight agent, with 97% compatibility each. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and conservation scientist (2%).

The five office-using occupations that were most compatible with task automation in Virginia in 2020 were cargo and freight agent; tax preparer; library technician; procurement clerk; and agricultural and food science technician, with 97% compatibility each. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and soil and plant scientist (2%).

The five office-using occupations that were least compatible with task automation in Washington, D.C. in 2020 were postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); soil and plant scientist (2%); and clergy (2%). Meanwhile, the most-compatible occupations in the state were library technician, procurement clerk and tax preparer, each with 97% compatibility, as well as correspondence clerk (96%) and general office clerk (95%).

The five office-using occupations that were most compatible with task automation in Washington state in 2020 were library technician; cargo and freight agent; procurement clerk; tax preparer; and agricultural and food science technician, with 97% compatibility each. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); clergy (2%); and soil and plant scientist (2%).

The five office-using occupations that were most compatible with task automation in West Virginia in 2020 were procurement clerk; tax preparer; library technician; and cargo and freight agent, each with 97% compatibility with task automation, as well as general office clerk (95%). Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and clergy (2%).

The five office-using occupations that were most compatible with task automation in Wisconsin in 2020 were library technician; agricultural and food science technician; tax preparer; cargo and freight agent; and procurement clerk, each with 97% compatibility with task automation. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); lodging manager (1%); postsecondary art, drama and music teacher (1%); and clergy (2%).

The five office-using occupations that were most compatible with task automation in Wyoming in 2020 were library technician; procurement clerk; cargo and freight agent; general office clerk; and legal secretary and administrative assistant. Meanwhile, the least-compatible occupations in the state were secondary school career/technical education teacher (0%); postsecondary nursing instructor and teacher (0%); postsecondary art, drama and music teacher (1%); lodging manager (1%); and clergy (2%).

Methodology

In the context of such an ample and nuanced topic as work automation, we have done our best to conduct a balanced and appropriately contextualized analysis. However, there are several relevant aspects for which we could not account at this time. These include, but are not limited to: the accurate state of automation research and technology pertaining to specific tasks or individual occupations; an accurate correlation between job loss or gain and changes in state population or shifts in the economy of the region; or changes in official occupation nomenclature.

For percentage of task-automation compatibility, we turned to estimates available on WillRobotsTakeMyJob.com, which employs machine learning algorithms trained using human predictions, in combination with proprietary research to attribute scores for computerization susceptibility of U.S. employment. Then, to the best of our ability, we matched that with 2020 BLS data on each occupation of interest for all states in the U.S., as well as Washington, D.C., Hawaii, and Puerto Rico. Our analysis did not include territories with total employment of less than 200,000 jobs.

For the scope of this article, we deemed an occupation to be “white collar” if the characteristic activity happens at least partly in an office setting — be it virtual or in-person. We excluded occupations with a BLS suffix of “All Other,” as well as occupations for which the title or the OCC Code did not have a matching entry in the WillRobotsTakeMyJob.com database. Furthermore, we opted for a hypothetical 50% threshold of computerizable tasks, which would roughly correspond to a 5/10 score on WillRobotsTakeMyJob.com.

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