Predicting the Future of Work: Lay Beliefs about Job Automation

Predicting the Future of Work: Lay Beliefs about Job Automation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting the Future of Work: Lay Beliefs about Job Automation Almira Abilova, Mirjam Tuk, Stefano Puntoni, Alina Ferecatu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6464311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Advancements in automation technology, including AI and robotics, threaten many occupations. Prior work has shed light on experts’ views of automation risk and identified key determinants of the risk for different occupations. Yet research into lay beliefs about job automation risk is limited, a gap this research addresses. This is important because students and job seekers should consider automation risk when making educational and occupational choices. Understanding what drives those beliefs and how they differ from expert predictions could help mitigate the threats linked to future economic displacement (e.g., by informing effective communication strategies about jobs at high risk of automation). A comprehensive study involving 4,388 respondents assessing 542 occupations demonstrates both alignment and divergence between laypeople’s perceptions and expert opinions. Crucially, job prestige is a key but often misleading predictor of lay beliefs about job automation. These findings have significant implications for workers, educators, and policymakers. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour job automation lay beliefs occupational stereotype prestige Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Summary Research in economics has shown that technological advancements significantly impact labor markets. However, to effectively mitigate the effects of job automation on people's careers, it is essential to complement expert predictions with a deeper understanding of laypeople’s beliefs about job automation likelihood, as these influence career- and educational decisions. This study explores laypeople’s beliefs about the likelihood of job automation. Through a comprehensive study involving 4,388 respondents evaluating 542 occupations, using both ordinary least squares (OLS) and hierarchical linear model (HLM) regressions, we examine the relationships between job characteristics, job stereotypes, and the perceived likelihood of job automation. We find that laypeople's beliefs about job automation risk simultaneously align with and differ from expert predictions. Like experts, laypeople tend to believe that jobs requiring non-routine, manual, and social tasks are less vulnerable to automation. However, our findings also reveal that laypeople additionally rely on job stereotypes: They believe that jobs higher in prestige are less vulnerable to automation. When comparing laypeople’s beliefs to expert predictions, we find that there is little correspondence between laypeople’s prestige perceptions and expert predictions of the likelihood of job automation, especially when expert predictions account for recent AI advancements. Introduction Rapid technological advancements in artificial intelligence (AI) and robotics are transforming labor markets 1 – 3 . New applications of technology enter the workspace daily. For example, developments in generative AI (e.g., language models, image generation) are reshaping many occupations 4 – 7 , due to their capacity to automate (at least partially) various tasks. Generative AI alone can enable the (partial) automation of at least some tasks across most occupations, with around 80% of the workforce being impacted and more than one-third of organizations experimenting with recent technologies 8 , 9 . Not surprisingly, to ensure employability and job security for the future, experts try to predict what type of jobs will be most affected by automation. These insights can, in turn, inform policies and programs intended to improve the match between job supply and demand. Such analyses by experts often reflect judgments about the degree to which fast-developing technological capabilities can automate various tasks that comprise a job 2 , but to convince people to pursue more automation-proof jobs, we also need an understanding of laypeople's beliefs about job automation. Despite widespread concerns about the impact of automation on employment, understanding laypeople's views on this topic is lacking. This is significant because it is these individuals who ultimately make decisions about their education and careers. Emerging technologies with significant potential to automate jobs have attracted considerable attention and have increased fears of replacement. In particular, the increased presence of autonomous technologies in the workplace heightens job insecurity 10 and threatens people’s economic stability 11 . Half of the respondents to a recent large survey indicated that technology could replace their jobs, and 74% of them predicted that further developments would increase unemployment rates (American Staffing Association, 2023). Thus, people appear aware of the risks linked to job automation; consequently, in an effort to reduce their job insecurity, they might be inclined to avoid jobs that they perceive at high risk of automation. But how do laypeople assess the likelihood of job automation? We seek to identify lay people’s beliefs about the likelihood of job automation, and test the correspondence between these lay beliefs and expert predictions. To do so, we investigate which jobs people think are more (vs. less) likely to be automated, the antecedents of those beliefs, and the extent to which these beliefs correspond with experts’ predictions. Expert Predictions of Job Automation Substantial research details the various effects of technological advancements on the demand for labor. Experts in labor economics and computer science agree that technology affects different jobs differently. Notably, Autor, Levy, and Murnane 12 proposed a foundation for understanding what causes certain jobs to be affected by automation more than other jobs, using a framework of task characteristics that machines are best suited to accomplish. The framework emphasizes the types of tasks for which computers or machines can substitute or complement human activity, and it categorizes tasks within a job as routine or nonroutine. Routine tasks adhere to explicit rules and involve executing a limited array of scripted actions, whereas nonroutine tasks are more complex and require problem-solving. According to the proposed framework, routine tasks are easier to substitute with existing technology. However, for nonroutine tasks, the degrees of substitution and complementarity depend on whether the task is analytic or manual. Analytic tasks demand the assimilation and processing of information, often in the form of knowledge-based work, whereas manual tasks are physically oriented and require the manipulation of objects, animals, or people. Arguably, nonroutine analytic tasks can be complemented by technology, but nonroutine manual tasks are difficult to substitute or complement. These authors also mention interactive or social tasks as difficult to automate. This foundation has informed much subsequent research into the impact of automation on jobs 13 – 15 , including efforts to quantify the potential for automation by examining the tasks and activities that are characteristic of different occupations. These various studies compare patent data, detail the tasks workers perform 16 , or apply AI capabilities to the demands of different occupations 9 , 17 . Other studies rely on machine learning to categorize the automation potential of different jobs, synthesizing task-level data to draw broader occupational insights 13 , or gathering expert predictions about how easily jobs can be taken over by machines 18 , 19 . Although these research findings vary in terms of their conclusions regarding the overall impact of job automation on the economy, they consistently emphasize that the nature of the specific tasks performed as part of a job is crucial in determining how susceptible that job is to automation. Lay Beliefs of Job Automation Importantly, these existing insights into the drivers of future job automation mainly come from experts. But what do laypeople believe, and do these beliefs differ from experts' findings? Insights into laypeople’s predictions can be crucial for designing effective policies, incentives, and communication that might channel them toward jobs that involve lower risks of automation. Prior literature confirms that beliefs about occupations influence both behaviors and occupational decisions 20 , 21 . In addition, education and career choices tend to be forward-looking 22 , such that young people’s aspirations reflect their perceptions of future career opportunities 23 , and people worry about their long-term economic well-being if they perceive that their jobs might be replaced 11 . Finally, research on job insecurity shows that perceived risk of job loss severely impacts health 24 and life satisfaction 25 . Considering the intensity and frequency of societal conversations about the threats posed by AI, robots, and other automation technology 26 , 27 , it seems both timely and relevant to deepen our understanding of lay beliefs about job automation. We expect lay theories about job automation to simultaneously align with and diverge from expert predictions. In terms of alignment, previous research suggests that lay people perceive tasks demanding elaboration or that are nonroutine as more suitably executed by humans rather than machines 28 – 30 . For example, people express lower preferences for AI (vs. human) agents in medicine and other consumer domains because they believe AI agents cannot capture their unique personal characteristics 31 , 32 . Laypeople also believe that humans can perform high-level cognitive (i.e., analytic) tasks better than machines 33 , 34 . In social interaction tasks, they negatively react to non-human (e.g., robots, AI) service providers and prefer interacting with humans. This combined evidence implies a general preference for human workers to perform tasks that are nonrepetitive in nature, require complex cognitive skills, or involve human interaction. In this sense, lay beliefs about which jobs can (or cannot) be replaced by automation appear to align with expert predictions about which jobs are more or less likely to be automated. Yet when laypeople assess a job, they often evaluate the job as a whole, based on so-called occupational stereotypes 20 , 35 – 37 . Occupational stereotypes are "preconceived attitudes about a particular occupation, the people in it, or one's suitability for it" 35 . Two commonly applied occupational stereotypes involve prestige and gender 36 , 37 . Gender stereotypes mainly influence judgments about personal aptitude for a job, so we do not expect them to affect beliefs about automation likelihood. In contrast, we expect that the occupational prestige stereotype does relate to lay beliefs about job automation. As a broad construct, occupational prestige entails many referents, including status, socioeconomic ranking, level of training, occupational level, and levels of difficulty and responsibility 21 . Historically, automation has largely impacted and replaced blue-collar jobs 38 , and observing such effects of automation, whether firsthand or among close others, and their further ripple effects on the economy seems likely to have shaped lay beliefs about job automation. Blue-collar work is often linked with low occupational prestige, so we predict that historical trends of automation in low-prestige roles might have fostered a lay theory that links (low) prestige to (high) automation likelihood 36 , 37 . Prestige and power are also closely intertwined, and power implies control over valued resources 39 and dominance in decisions 40 . If automation seems top-down, laypeople might believe that powerful, high-prestige figures influence which jobs get automated 20 . However, using prestige stereotypes to inform job choices can have deeply detrimental effects, as demonstrated by BRIDGE, a European Commission–funded project designed to aid students from low-income areas to build better careers. Project officers noticed that many students pursued post-secondary vocational training in economics and accounting, even though tasks in basic administration and bookkeeping are quickly being automated 15 . Through interviews with BRIDGE project associates, we learned that many low-income students continued to pursue vocational studies associated with jobs they consider more prestigious (e.g., entry-level, white-collar office jobs), even though those roles offer worse job prospects than available (blue-collar) alternatives (see SI appendix ). Indeed, the link between occupational prestige with factors that shield jobs from the risk of automation seems to be dissolving, especially as advances in generative AI enable the automation of knowledge work. As Brynjolfsson et al. 14 demonstrate, machine learning capabilities extend across wages and education levels. Moreover, generative AI currently has achieved greater exposure in occupations marked by higher income and educational levels 4 , 9 . Hence, we will investigate whether laypeople hold beliefs regarding the relationship between task characteristics (routine, analytic, manual, social) and the likelihood of automation that match those indicated by experts, as well as whether laypeople also rely on occupational stereotypes (i.e., job prestige) to make inferences about automation likelihood. We examine these questions in two stages. First, we measure laypeople’s perceptions of occupations from the Occupational Information Network (O*NET). Second, we assess the correspondence between our data and expert findings documented in previous research. Our findings demonstrate that, while laypeople rely on relevant (as defined by experts) task characteristics, such as routine, manual, and social aspects, to predict the likelihood of job automation, job prestige is also a crucial antecedent. We further demonstrate that lay beliefs exhibit low correspondence with expert predictions of job automation, particularly for occupations that are substantially exposed to AI, a finding that can be attributed to high reliance on prestige stereotypes. For illustrative purposes, all our data can be found here ( https://job-automation-project.com ). This app provides insights into all data and relationships at the aggregate level but also allows users to zoom in on any single occupation. Results We conducted a comprehensive analysis of lay beliefs about job automation across 542 occupations. We used O*NET to compile a list of 821 distinct occupational titles, which we narrowed down on the basis of a pretest (N = 888), in which we sought specifically to identify which job titles laypeople could understand. Then, in the main study (N = 3500), we asked each participant to rate a subset of 50 occupations, using scales pertaining to the perceived likelihood of job automation (dependent variable) and its antecedents, including the extent to which the job involves routine, manual, analytic, and social tasks, as well as prestige and gender stereotypes (independent variables). Using both ordinary least squares (OLS) and hierarchical linear model (HLM) regressions, we examine the relationships between job characteristics, job stereotypes, and the perceived likelihood of job automation. First, we tested our predictions using the job as the unit of analysis, such that we averaged the scores for each job. The regression results revealed significant associations between most ratings of (expert-based) job characteristics and the perceived likelihood of job automation. Specifically, we uncover a positive and significant relationship between perceptions of the extent to which a job contains routine tasks and this perceived automation likelihood b = 9.53, P < 0.001, 95% confidence intervals (CI) = (7.62, 11.44). Further, we find negative and significant relationships between perceptions of the extent to which a job contains manual b = -5.52, P < 0.001, 95% CI = (-6.56, -4.47) and social b = -4.32, P < 0.001, 95% CI = (-5.43, -3.20) tasks and the perceived likelihood of job automation (Table 1 ). Hence, regarding task characteristics, laypeople’s beliefs align with expert opinions: They perceive occupations involving physical labor, substantial interpersonal interaction, or a high degree of nonroutine tasks as less susceptible to automation. However, we do not find a significant relationship between perceptions of the extent to which a job contains analytic tasks and automation likelihood ( b = 1.23, P = 0.14). Furthermore, occupational prestige is significantly associated with the perceived likelihood of job automation b = -8.78, P < 0.001, 95% CI = (-11.14, -6.43) (Fig. 1 ), even if we control for the other relevant antecedents. People believe that less prestigious jobs are more likely to be automated than are more prestigious jobs. As expected, gender stereotypes do not significantly affect the perceived likelihood of job automation (gender: b = 0.29, P = 0.70; Table 1 , Fig. 2 ). Table 1 OLS Linear Regression of Lay Beliefs of Likelihood of Job Automation as a Function of Task Characteristics and Job Stereotypes Estimate s.e. t value Pr(>|t|) (Intercept) 61.540 6.990 8.804 < 0.0001 Analytic 1.230 0.836 1.471 0.1418 Manual -5.518 0.532 -10.37 < 0.0001 Routine 9.531 0.972 9.803 < 0.0001 Social -4.315 0.566 -7.627 < 0.0001 Gender 0.292 0.625 0.468 0.6399 Prestige -8.784 1.201 -7.316 < 0.0001 Notes: N = 542 jobs, Model fit: F (6,531) = 89.52, P < 0.001, Residual SE = 8.431, R 2 = 0.50, Adjusted R 2 = 0.49. Second, we assessed the robustness of these results by using individual ratings as the unit of analysis (N = 19,784). In an HLM regression with varying intercepts, we specify different baseline levels of the likelihood of automation across jobs and thereby test for the relationship between perceived job automation and all the antecedents, while also accounting for within-job variation in automation likelihood. Figure 3 plots the predicted mean scores for the likelihood of automation for all occupations, ranked from lowest to highest, and their 95% Bayesian credible intervals (BCI). At the observation level, we also account for significant variation in the baseline likelihood of automation across occupations, which ranges from 20–70% on average. The results in Table 2 further show that the HLM replicates the results of our OLS regression at the job level: We find a negative relationship between the likelihood of automation and prestige b Prestige = -0.810, 95% BCI = (-1.224, -0.403), such that when prestige is lower, the likelihood of automation is rated as higher. Furthermore, we note a significant positive effect of the routine task score on the likelihood of automation b Routine = 0.659, 95% BCI = (0.263, 1.062) and significant negative effects of both manual b Manual = -0.431, 95% BCI = (-0.797, -0.062) and social b Social = -0.671, 95% BCI = (-1.079, -0.256) tasks. Again, gender and analytical tasks are not significant predictors b Gender = 0.142, 95% BCI = (-0.347, 0.650), b Analytic = -0.317, 95% BCI = (-0.701, 0.070). Table 2 Parameter Estimates of the HLM Predicting the Likelihood of Automation as a Function of Task Characteristics and Job Stereotypes. Mean 0.5% CI 2.5% CI 97.5% CI 99.5% CI (Intercept) 44.965 40.313 41.545 48.346 49.381 Manual -0.431 -0.889 -0.797 -0.062 0.060 Routine 0.659 0.156 0.263 1.062 1.181 Gender 0.142 -0.514 -0.347 0.650 0.798 Social -0.671 -1.226 -1.079 -0.256 -0.137 Analytic -0.317 -0.807 -0.701 0.070 0.174 Prestige -0.810 -1.360 -1.224 -0.403 -0.265 Notes: N = 19,784 observations, Log – posterior = -96423.62, CI = credible interval. The plot of model fit across 542 occupations demonstrates the negative relationship between the perceived likelihood of job automation and prestige, even at an observation level. Figure 4 shows, in gray, the base chances of automation for different jobs, each of which has a different starting point. Additionally, the shiny app ( https://job-automation-project.com ) offers the opportunity to view individual plots for each job. The consistent trend in Fig. 4 illustrates how job prestige affects the perceived chances of the job being automated. The aggregate-level model, in blue, represents the relationship between automation likelihood and prestige for the average occupation. Individual occupations with higher or lower baseline job automation scores vary around this average. Using the results in Table 2 , we can reproduce the effects observed in the job-level analysis. Task characteristics (routine, manual, and social) still predict the perceived likelihood of job automation significantly, and the coefficients are significant based on the 95% BCI. Prestige also remains a significant predictor, with a 99% BCI, highlighting its robust influence on perceived job automation potential. With a preregistered, controlled experiment, we also investigated the causal relationship between prestige and the perceived job automation likelihood (see SI Appendix ). We assessed whether the same jobs (e.g., auditor) are considered less likely to be automated when they are in high-prestige industries (e.g., corporate law firms) rather than in low-prestige industries (e.g., correctional facilities). Controlling for job attractiveness, we found that job prestige affects perceived automation likelihood, such that high-prestige jobs are perceived as less likely to be automated. We confirm this effect across various jobs and industry types. Thus, these results show that prestige stereotype exerts a causal impact on perceptions of automation risk. Correspondence of Lay Beliefs with Expert Predictions In the preceding section, we established that laypeople use expert-identified task characteristics as well as occupational prestige stereotypes to predict job automation likelihood. Next, we aim to identify the extent to which these lay beliefs correspond with expert predictions. To facilitate this comparison, we sought out studies that quantify job automation effects for each occupation, which may manifest as a probability score, exposure to technology, or risk of substitution 13 , 14 , 17 . Experts have quantified automation's potential by examining the tasks and activities characteristic of different occupations 12 , 16 , 38 , 41 , 42 . Because we are interested in perceived differences in risk of automation across jobs, we focused on expert estimates of the impact of automation for specific occupations. To that end, we identified two relevant sources of expert estimates for our comparative analysis: Frey and Osborne 13 , who were among the first to calculate the risk of automation at the occupational level 18 , 42 , and the recent study by Felten, Raj, and Seamans 17 , which includes considerations of recent developments in AI. The susceptibility to automation score proposed by Frey and Osborne 13 offers probabilistic estimates of job replacement due to computerization, based on a task framework that includes routine versus nonroutine and cognitive versus manual tasks 12 . Their model provides estimates of the potential for automation across various occupations. For this analysis, we include only 490 of the 542 occupations in our previous analysis, which reflects necessary exclusions of occupations that had not appeared in the expert studies or that the pretest results indicated were difficult to understand. Such discrepancies arise even though both data sets draw from the same O*NET database. When we compare Frey and Osborne’s probability estimates with our lay beliefs data set, we uncover a significant positive association, r(490) = 0.47, p < 0.001, 95% CI = (0.40, 0.54) (Fig. 5 ), suggesting general agreement about the likelihood of job automation between Frey and Osborne’s predictions and the lay beliefs of our respondents. However, Frey and Osborne’s work has prompted some criticism 9 , 16 . The data are approximately a decade old, and in the intervening years, AI has made huge leaps forward, with massive potential to impact work. Just a few months after the broad market introduction of language models, for example, 50% of surveyed managers reported that they had already tried out AI applications 6 . Furthermore, workers who use AI applications demonstrate increased productivity and quality of work, especially knowledge workers performing tasks like writing and coding 7 , 43 . With such rapid adoption and impressive effects, it becomes imperative to detail how lay beliefs about job automation correspond with the effects of AI applications and conduct updated analyses, using more recent expert ratings. Accordingly, we turn to a score, 'AI exposure on occupation,' of an occupation's exposure to AI applications provided by Felten, Raj, and Seamans 17 . To obtain this forward-looking measure, these authors associated 10 AI applications with 52 O*NET occupational abilities and then calculated the required ability level and occupational-level exposure. In this case, we find that lay beliefs about job automation do not significantly correlate with expert estimates of exposure to AI, r (436) = -0.04, P = 0.39 (Fig. 5 ). These two comparisons suggest that lay beliefs correspond to some extent to expert predictions, but this correspondence weakens when the expert scores include more recent AI applications. The relatively low correspondence with the occupational exposure to AI score might be attributed, at least in part, to a reliance on prestige when laypeople make predictions about the likelihood of job automation, whereas, in reality, prestige is becoming less relevant as a predictor of whether a job is likely to be automated 41 . To further examine this, we tested the relationship between prestige perceptions rated by our respondents and occupation computerization probability and AI exposure based on Frey and Osborne and Felten, Raj and Seamans, respectively. These analyses reveal opposing results. Relating prestige perceptions to occupation computerization probability from Frey and Osborne, we find a significant negative relationship r (490) = -0.61, P < 0.001, 95% CI = (-0.66, -0.55) (Fig. 6 ), in line with the relationship between prestige and lay beliefs about job automation. Contrary, if we relate the prestige perceptions to AI exposure as captured by Felten, Raj and Seamans, we find a positive association, r (436) = 0.55, P < 0.001, 95% CI = (0.48, 0.61): The jobs considered more prestigious are more exposed to AI (Fig. 6 ). It should be noted that the latter finding aligns closely with the observations shared by the BRIDGE associates in our interviews, who pointed to (low-income) students making career choices based on prestige and opting for occupations with high automation likelihood. Discussion This research is the first to explore laypeople’s beliefs about the antecedents of job automation likelihood, which is important given rapidly advancing technological developments and the relevance of such lay beliefs for people’s choice of study and careers. We identify key antecedents of lay beliefs about job automation and compare them with expert predictions. Although laypeople draw on factors directly related to job automation, such as the routine nature of tasks or the extent of manual and social interactions involved, they also rely on occupational prestige, which, in reality, has limited relevance. Our analysis of 542 occupations shows a negative association between the perceived prestige and the perceived likelihood of automation. By complementing our OLS analysis with Bayesian HLM analysis, we provide robust evidence for the relationship between the dependent and predictive variables. Our follow-up experiment further strengthens our findings, suggesting a causal link between prestige and job automation perceptions. Further analyses examining the alignment between laypeople’s predictions and expert measures are inconclusive. When experts operationalize the risk of automation as computerization probability, we observe a positive correlation between both. However, when experts operationalize the risk of automation as occupational exposure to AI, we find no correlation between lay beliefs and experts’ predictions and even a positive correlation between prestige perceptions and occupational exposure to AI. Given that the adoption of AI by firms is increasing rapidly, this lack of correspondence between lay beliefs and expert predictions is alarming and underscores the importance of studying lay beliefs. Our research is the first to focus on the layperson's perspective and thus adds to the job automation literature, most of which has sought to identify job or task characteristics that increase susceptibility to automation as well as its broader implications for labor markets 2 . Further adding to the literature on occupational beliefs and stereotypes 21 , 35 , 37 , our findings highlight the significant influence of occupational stereotypes, particularly job prestige, on laypeople’s perceptions of job automation likelihood. We uncover that job prestige relates negatively to perceived job automatability; more prestigious jobs are assumed to be less susceptible to automation. Yet contrary to this lay belief, expert predictions about the effect of AI applications on jobs suggest a positive association between occupational prestige perceived by our respondents and exposure to AI based on expert predictions. This positive relationship resonates with the findings of our interviews in the BRIDGE case, which included observations that more prestigious, white-collar jobs are both more popular and more at risk of automation. This large gap underscores the importance of considering and addressing laypeople’s (potentially erroneous) beliefs. In particular, the association between occupational prestige and perceived automation likelihood suggests the need for targeted interventions to reshape laypeople’s perceptions about job prospects and improve their understanding of job automation determinants. The findings also emphasize the relevance of realigning skill development and training programs to reflect the evolving job market. The importance of studying lay beliefs regarding job automation pertains not only to those seeking jobs or choosing a future career but also those giving career advice (career mentors, parents), whose understanding and recommendations can strongly influence others’ career choices 22 , 44 . Therefore, our work suggests that a collaborative effort by policymakers, educators, and industry stakeholders is needed to address the discrepancies between lay beliefs and expert predictions, ensuring that skill and knowledge development matches future demands and fosters a resilient labor market. Continued research might test specific interventions designed to enhance predictive accuracy about job automation likelihood, to help laypeople develop expectations that align more closely with expert assessments. Our work provides an important initial step to facilitate such efforts, by pointing to the important but potentially misguided belief that more prestigious jobs are less susceptible to automation. Materials and Methods List of Occupations . To compile a comprehensive list of occupational titles, we relied on the O*NET database. The initial list consisted of 821 distinct occupational titles. To ensure these titles were easily understood and unambiguous, we conducted a pretest with 888 participants (M age = 39.88 years, female = 41%), who we assigned randomly to review 50 occupations and rate the clarity of each occupational title on a scale, ranging from 1 (completely unclear) to 6 (completely clear). Only occupations with mean clarity scores above the midpoint (exceeding 3.5) were retained, resulting in a final list of 542 occupations. Data Collection . Before the data collection, respondents provided informed consent to participate in the study. The survey and data collection procedure received ethics approval from the IRB of our institution. The data collection procedure involved recruiting participants from MTurk’s Panel Workers through the Cloud Research platform. Each participant was randomly assigned to rate 50 occupations on one of the following scales: perceived likelihood of job automation, analytic, gender, manual, prestige, routine, or social. Participants rated 542 jobs, and the sample consisted of 3500 participants with a mean age of 38.75 years, 46% of whom identified as female. Before rating each occupation, they received the definitions of each concept; all definitions were adopted from Merriam-Webster's (2020) dictionary and paraphrased for ease of comprehension. Participants in all conditions then had to answer a straightforward question based on the definition. Participants did not proceed unless they answered the question correctly. Scales and Measurements . We measured perceptions related to job automation likelihood, job characteristics, gender stereotypes, and occupational prestige, as outlined subsequently. Antecedents Based on the Expert Predictions. Considering predictors derived from expert findings that the extent to which jobs involve routine, manual, analytic tasks and social interactions is related to automatability, we measured laypeople’s perceptions of these four job characteristics. Each characteristic was measured on a 5-point Likert scale, such that higher scores indicate a stronger presence of the characteristic in the occupation. Participants read the following descriptions of each antecedent. Analytic: An occupation is analytic or cognitive to the extent that it consists of cognitive or knowledge work, such as information processing, analysis, planning, controlling, or problem-solving. Manual: An occupation is manual if it involves physical labor, such as handling, moving, or manipulating any person, animal, or thing. Routine: An occupation is routine if it follows precise, well-understood, repetitive, and standard procedures. Social: An occupation is social if it involves communicating, relationship building, presenting, negotiating, advocating, or caring for others. Please pick out the statement that best gives your personal opinion of the amount of social that such a job has. Antecedents Based on Job Stereotypes Gender: The occupation will be considered masculine if it requires skills and characteristics typically associated with men, such as muscular, rugged, competent, and confident. The occupation will be considered feminine if it requires skills and characteristics typically associated with women, such as fine features, warm, good-natured, friendly. Prestige: Prestige is a rank granted to those who are recognized and respected for their skills, success, or knowledge. Someone worthy to look up to. Dependent Variable . Perceived Likelihood of Job Automation: Labor automation is the practice of substituting human labor with technology to perform specific tasks or jobs. Data Preparation for Hierarchical Linear Modeling (HLM). Not all occupations had an equal number of responses for all variables because each variable's response was collected from a different participant to avoid potential halo effects. For example, the Audiologist occupation had 30 complete observations, but the Librarian had 34 complete observations, spanning all measures, so the response counts for each occupation differ. This variance reflects the random assignment of 50 occupations to each participant from a full list of 542 occupations and different measures. Therefore, the final sample consisted of 138,488 observations, considering all the variables. Each respondent rated only 50 occupations on one characteristic. We restructured the data to create a detailed data set that logs each occupation's ratings on all task characteristics, job stereotypes, and the perceived likelihood of job automation. In turn, we could randomly pair participant responses about various characteristics to generate occupation-specific profiles. For each observation, the rating for each characteristic of an occupation came from a different respondent. The minimum number of observations per occupation is 23, and the maximum is 80. Respondents were asked to rate 10 of the 542 occupations twice, because the occupations were replicated in the questionnaire due to a coding error. The final data set consists of 19,784 observations. Model Specifications. We specified a random-effects regression with errors clustered at the occupation level. The model specification is: \(\:L{A}_{ij}={\alpha\:}_{j}+{\beta\:}_{k}{C}_{ijk}+{ϵ}_{ij}\:\) , and (1.1) \(\:{\alpha\:}_{j}={\alpha\:}_{0}+{\nu\:}_{j}\) , (1.2) where j is the occupation indicator, i represents the observation, LA ij indicates the likelihood of automation score of each observation i for each occupation j , and C ijk represents the scores of each job characteristic k for observation i for each occupation j . The intercept \(\:{\alpha\:}_{j}\) is occupation-specific, so the likelihood of automation randomly varies across occupations, distributed as \(\:N\left({\alpha\:}_{0},\:\tau\:\right)\) . Then \(\:{ϵ}_{ij}\:\) reflects within-occupation variability, thereby indicating the strength of the relationship between an occupation-specific likelihood of automation and its job characteristics, distributed as \(\:N\left(0,\:\sigma\:\right)\) . The parameters of interest are \(\:{\beta\:}_{k}\) , or the impact of each antecedent on the likelihood of automation, which are specified at the aggregate level, such that the impact of all antecedents on the likelihood of automation is assumed to be similar across occupations. Some occupations have a higher likelihood of automation than others, but the impact of all the antecedents is the same across occupations. Shiny app . We have created a shiny app ( https://job-automation-project.com ) for illustrative purposes, visualizing the results demonstrated in this paper across three pages. On Page 1, "Main Results," users can select a predictor from task characteristics to job stereotypes and view the plotted relationship with the lay beliefs of job automation. By hovering over each dot on the plot, users can see which job the dot represents along with its mean values. On Page 2, "Beliefs per Occupation," users can select an occupation and either a task characteristic or job stereotype of interest to see the plotted relationship at the observational level for each job. To make navigation easier, users can first select a cluster of interest and then choose a job within that cluster. On Page 3, "Expert Predictions," users can choose between expert predictions of job automation, either "Occupation Computerization Probability" by Frey and Osborne (2017) or "Occupational Exposure to AI" by Felten, Raj, and Seamans. Similar to Page 1, users can hover over the dots to see which occupation each dot represents, along with the mean scores. Declarations Data Availability Statement Data files, analysis codes, and study instructions can be found on the Open Science Framework (https://osf.io/buhkc/?view_only=a3f541c9b17c40d3b6d63ed4a0e6b36d). Author Contributions: A.A., M.A.T., and S.P. designed research; A.A. performed research; A.A. and A.M.F. analyzed data; A.A., M.A.T., S.P., and A.M.F. wrote the paper. Competing Interest Statement: The authors declare no competing interest. Classification: Psychological and Cognitive Sciences. Acknowledgments We thank the Erasmus Research Institute of Management for their financial support for the data collection and the Research Software Engineering and Consulting team at Rotterdam School of Management for developing a Shiny app. We are also grateful to the associates of the BRIDGE program for sharing their insights. References Acemoglu, D. & Lensman, T. Regulating Transformative Technologies. NBER Working Paper Series (2023) doi:10.3386/w31461. Frank, M. R. et al. Toward understanding the impact of artificial intelligence on labor. Proc Natl Acad Sci U S A 116 , 6531–6539 (2019). Brynjolfssonn, E. & Mitchell, T. What can machine learning do? Workforce implications. Science (1979) 358 , 1530–1534 (2017). Felten, E. & Raj, M. Occupational Heterogeneity in Exposure to Generative AI. SSRN (2023) doi:10.2139/ssrn.4414065. Brynjolfsson, E. et al. Generative AI at Work. NBER Working Paper Series (2023) doi:10.3386/w31161. Korst, J. & Puntoni, S. 5 Ways Marketing and Sales Leaders Can Embrace GenAI. Harvard Business Review (2023). Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science (1979) 381 , 187–192 (2023). Chui, M. et al. The economic potential of generative AI: The next productivity frontier. McKinsey & Company (2023). Eloundou, T., Manning, S., Mishkin, P. & Rock, D. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. ArXiv (2023) doi:10.48550/arXiv.2303.10130. Yam, K. C., Tang, P. M., Jackson, J. C., Su, R. & Gray, K. The Rise of Robots Increases Job Insecurity and Maladaptive Workplace Behaviors: Multimethod Evidence. Journal of Applied Psychology (2023) doi:10.1037/apl0001045. Granulo, A., Fuchs, C. & Puntoni, S. Psychological reactions to human versus robotic job replacement. Nat Hum Behav 3 , 1062–1069 (2019). Autor, D. H., Levy, F. & Murnane, R. J. The Skill Content of Recent Technological Change: and Empirical Exploration. Q J Econ 118 , 1279–1333 (2003). Frey, C. B. & Osborne, M. A. The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Change 114 , 254–280 (2017). Brynjolfsson, E., Mitchell, T. & Rock, D. What Can Machines Learn and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings 108 , 43–47 (2018). Das, S. et al. Learning Occupational Task-Shares Dynamics for the Future of Work. in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 36–42 (2020). doi:10.1145/3375627.3375826. Webb, M. The Impact of Artificial Intelligence on the Labor Market. SSRN (2020) doi:http://dx.doi.org/10.2139/ssrn.3482150. Felten, E., Raj, M. & Seamans, R. Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal 42 , 2195–2217 (2021). Nedelkoska, L. & Quintini, G. Automation, skills use and training. OECD Social, Employment and Migration Working Papers No.202 (2018) doi:10.1787/2e2f4eea-en. Arntz, M., Gregory, T. & Zierahn, U. The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. (2016) doi:10.1787/5jlz9h56dvq7-en. Anteby, M., Curtis, K. C. & DiBenigno, J. Three Lenses on Occupations and Professions in Organizations: Becoming, Doing, and Relating. Acad Manag Ann 10 , 183–244 (2016). Gottfredson, L. S. Circumscription and Compromise: A Developmental Theory of Occupational Aspirations. Journal of Counseling Psychology Monograph 28 , 545–579 (1981). Fouad, N. A. Work and vocational psychology: Theory, research, and applications. Annu Rev Psychol 58 , 543–564 (2007). Wall, J., Covell, K. & Macintyre, P. D. Implications of Social Supports for Adolescents’ Education and Career Aspirations. Canadian Journal of Behavioral Science 31 , 63–71 (1999). Reichert, A. R. & Tauchmann, H. Workforce reduction, subjective job insecurity, and mental health. J Econ Behav Organ 133 , 187–212 (2017). Geishecker, I. Simultaneity bias in the analysis of perceived job insecurity and subjective well-being. Econ Lett 116 , 319–321 (2012). Verma, P. & De Vynck, G. ChatGPT took their jobs. Now they walk dogs and fix air conditioners. The Washington Post (2023). Leonhardt, M. Some workers are worried that ChatGPT will replace their jobs. They might be right. Fortune (2023). Langer, M. & Landers, R. N. The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Comput Human Behav 123 , (2021). Mahmud, H., Islam, A. K. M. N., Ahmed, S. I. & Smolander, K. What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technol Forecast Soc Change 175 , (2022). Castelo, N., Bos, M. W. & Lehmann, D. R. Task-Dependent Algorithm Aversion. Journal of Marketing Research 56 , 809–825 (2019). Longoni, C., Bonezzi, A. & Morewedge, C. K. Resistance to Medical Artificial Intelligence. Journal of Consumer Research 46 , 629–650 (2019). Granulo, A., Fuchs, C. & Puntoni, S. Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts. Journal of Consumer Psychology 31 , 72–80 (2021). Waytz, A. & Norton, M. I. Botsourcing and Outsourcing: Robot, British, Chinese, and German Workers Are for Thinking - Not Feeling - Jobs. Emotion 14 , 434–444 (2014). Lee, M. K. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data Soc 5 , (2018). He, J. C., Kang, S. K., Tse, K. & Toh, S. M. Stereotypes at work: Occupational stereotypes predict race and gender segregation in the workforce. J Vocat Behav 115 , (2019). Oswald, P. A. Sex-typing and prestige ratings of occupations as indices of occupational stereotypes. Percept Mot Skills 97 , 953–959 (2003). Glick, P., Wilk, K. & Perreault, M. Images of Occupations: Components of Gender and Status in Occupational Stereotypes. Sex Roles 32 , 565–582 (1995). Acemoglu, D. & Restrepo, P. Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives 33 , 3–30 (2019). Smith, P. K. & Galinsky, A. D. The Nonconscious Nature of Power: Cues and Consequences. Soc Personal Psychol Compass 4 , 918–938 (2010). Kteily, N., Saguy, T., Sidanius, J. & Taylor, D. M. Negotiating power: Agenda ordering and the willingness to negotiate in asymmetric intergroup conflicts. J Pers Soc Psychol 105 , 978–995 (2013). Brynjolfsson, E., Mitchell, T. & Rock, D. What Can Machines Learn and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings 108 , 43–47 (2018). Arntz, M., Gregory, T. & Zierahn, U. Revisiting the risk of automation. Econ Lett 159 , 157–160 (2017). Peng, S., Kalliamvakou, E., Cihon, P. & Demirer, M. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. ArXiv (2023) doi:https://doi.org/10.48550/arXiv.2302.06590. Siy, J. O. et al. Does the Follow-Your-Passions Ideology Cause Greater Academic and Occupational Gender Disparities Than Other Cultural Ideologies? J Pers Soc Psychol 125 , 548–570 (2023). Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis. (Chapman & Hall/CRC, 2013). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTALLayBeiefsaboutJobAutomation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6464311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450650881,"identity":"284fb88a-0590-49c6-90aa-f9d2dc9c1405","order_by":0,"name":"Almira Abilova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYHACNoaEAiDF3gDmyRCpxQBI8RwA83iI08IA0iKRQKQW/gbmZw8eGNgk9s98+/jTDYY7hLVIHGAzN0gwSEuccTvdTDqH4RkRDjvAYCaRYHDYmOF2GhtzDsNhwlrkD7B/A2uRv3mM+TNRWgwO8IBtkTO4wcYgTZQWw8M85SC/yBmeSWOTzjEgQovc8fZtD39U2PDIHQc5rOKwHEEtDMyo7iSsYRSMglEwCkYBEQAAtPg0Fdi9TKYAAAAASUVORK5CYII=","orcid":"","institution":"Rotterdam School of Management, Erasmus University","correspondingAuthor":true,"prefix":"","firstName":"Almira","middleName":"","lastName":"Abilova","suffix":""},{"id":450650882,"identity":"8f6ad6ec-2e0c-4406-ab37-0ceebf775c69","order_by":1,"name":"Mirjam Tuk","email":"","orcid":"","institution":"Rotterdam School of Management, Erasmus University","correspondingAuthor":false,"prefix":"","firstName":"Mirjam","middleName":"","lastName":"Tuk","suffix":""},{"id":450650883,"identity":"a48f53f8-2a4b-462b-b3cf-e4aea020958d","order_by":2,"name":"Stefano Puntoni","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Puntoni","suffix":""},{"id":450650884,"identity":"f7e48297-9ae7-4010-9480-8cfdce252cf5","order_by":3,"name":"Alina Ferecatu","email":"","orcid":"","institution":"Rotterdam School of Management, Erasmus University","correspondingAuthor":false,"prefix":"","firstName":"Alina","middleName":"","lastName":"Ferecatu","suffix":""}],"badges":[],"createdAt":"2025-04-16 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6464311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6464311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82001029,"identity":"97c0d6e4-1108-4a72-a64e-17cc805f726d","added_by":"auto","created_at":"2025-05-05 19:40:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":594633,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of prestige and perceived job automation likelihood. The job-level average scores of lay beliefs about the likelihood of job automation and prestige reflect ratings by the study participants. The OLS regression, associating the likelihood of job automation with prestige, highlights their negative correlation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/9a3b418c243d0a70ac0a5405.png"},{"id":82000558,"identity":"e0e54fec-205b-4102-b6bc-f6ce424bda6e","added_by":"auto","created_at":"2025-05-05 19:32:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81055,"visible":true,"origin":"","legend":"\u003cp\u003eOLS model estimates and 95% confidence intervals for the effects of task characteristics and job stereotypes on perceived likelihood of job automation. This figure highlights the negative significant effect of prestige, manual tasks, and social tasks; the positive significant effect of routine tasks on lay beliefs about job automation; and the nonsignificant effect of analytic tasks and gender.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/c98b788aa320c5a069dbe304.png"},{"id":82000557,"identity":"ebce1e8d-8741-40ff-a4c7-66b10eea2f0c","added_by":"auto","created_at":"2025-05-05 19:32:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":303450,"visible":true,"origin":"","legend":"\u003cp\u003eAverage observed likelihood of automation per occupation (in red) vs. predicted mean scores within 95% Bayesian credible intervals (in black). This figure highlights the goodness-of-fit of the model, which can accommodate observations with both high and low baseline levels of automation likelihood.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/162468f91d5950cb53754c9d.png"},{"id":82001030,"identity":"29bfea0c-b3c3-4258-b8a2-b5d755b7bbd5","added_by":"auto","created_at":"2025-05-05 19:40:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196424,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior median models for 542 occupations, calculated by a hierarchical random intercepts model (in gray), with the posterior median global model (in blue). This plot reveals the differences in the baseline likelihood of automation; the lowest ranges slightly above 20%, and the highest reaches close to 80%, captured by the random intercept. The plot also depicts the negative relationship between the likelihood of automation and prestige, which is similar across all occupations.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/587a3686a23c508f25376b78.png"},{"id":82000561,"identity":"1fd3c850-c1ea-4e67-833c-6752b41ea790","added_by":"auto","created_at":"2025-05-05 19:32:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":404223,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between lay beliefs and expert predictions about automation likelihood across occupations. We overlay the graph with the OLS regression associating the lay beliefs and expert predictions, which highlights the positive correlation according to Frey and Osborne (2017) but no correlation according to Felten, Raj, and Seamans’s (2021) score of occupational exposure to AI.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/4499b939eb09f68083536f92.png"},{"id":82001032,"identity":"8d4a0c5a-3917-4eab-9f2e-2c8a918158d8","added_by":"auto","created_at":"2025-05-05 19:40:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":395907,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between the occupational prestige stereotype and expert predictions about the likelihood of job automation across occupations. The OLS regression associating prestige and expert predictions about job automation likelihood appears overlaid, which highlights the negative correlation on the left graph with Frey and Osborne (2017) and the positive correlation on the right graph with Felten, Raj, and Seamans’s (2021) score on occupational exposure to AI.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/4e051b09357cda41f3592ce2.png"},{"id":82001661,"identity":"4e57021c-50c0-4ff2-9564-be739268303b","added_by":"auto","created_at":"2025-05-05 19:56:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2873840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/5c365b85-f970-4bf3-a934-e7277686f01e.pdf"},{"id":82001298,"identity":"119dd45f-c43e-4cc9-89a4-fdba731582c6","added_by":"auto","created_at":"2025-05-05 19:48:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":172399,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALLayBeiefsaboutJobAutomation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6464311/v1/455aef31ebca192f8462ae10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the Future of Work: Lay Beliefs about Job Automation","fulltext":[{"header":"Summary","content":"\u003cp\u003eResearch in economics has shown that technological advancements significantly impact labor markets. However, to effectively mitigate the effects of job automation on people\u0026apos;s careers, it is essential to complement expert predictions with a deeper understanding of laypeople\u0026rsquo;s beliefs about job automation likelihood, as these influence career- and educational decisions. This study explores laypeople\u0026rsquo;s beliefs about the likelihood of job automation. Through a comprehensive study involving 4,388 respondents evaluating 542 occupations, using both ordinary least squares (OLS) and hierarchical linear model (HLM) regressions, we examine the relationships between job characteristics, job stereotypes, and the perceived likelihood of job automation. We find that laypeople\u0026apos;s beliefs about job automation risk simultaneously align with and differ from expert predictions. Like experts, laypeople tend to believe that jobs requiring non-routine, manual, and social tasks are less vulnerable to automation. However, our findings also reveal that laypeople additionally rely on job stereotypes: They believe that jobs higher in prestige are less vulnerable to automation. When comparing laypeople\u0026rsquo;s beliefs to expert predictions, we find that there is little correspondence between laypeople\u0026rsquo;s prestige perceptions and expert predictions of the likelihood of job automation, especially when expert predictions account for recent AI advancements.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eRapid technological advancements in artificial intelligence (AI) and robotics are transforming labor markets\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. New applications of technology enter the workspace daily. For example, developments in generative AI (e.g., language models, image generation) are reshaping many occupations \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, due to their capacity to automate (at least partially) various tasks. Generative AI alone can enable the (partial) automation of at least some tasks across most occupations, with around 80% of the workforce being impacted and more than one-third of organizations experimenting with recent technologies \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Not surprisingly, to ensure employability and job security for the future, experts try to predict what type of jobs will be most affected by automation. These insights can, in turn, inform policies and programs intended to improve the match between job supply and demand. Such analyses by experts often reflect judgments about the degree to which fast-developing technological capabilities can automate various tasks that comprise a job\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, but to convince people to pursue more automation-proof jobs, we also need an understanding of laypeople's beliefs about job automation.\u003c/p\u003e \u003cp\u003eDespite widespread concerns about the impact of automation on employment, understanding laypeople's views on this topic is lacking. This is significant because it is these individuals who ultimately make decisions about their education and careers. Emerging technologies with significant potential to automate jobs have attracted considerable attention and have increased fears of replacement. In particular, the increased presence of autonomous technologies in the workplace heightens job insecurity \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and threatens people\u0026rsquo;s economic stability \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Half of the respondents to a recent large survey indicated that technology could replace their jobs, and 74% of them predicted that further developments would increase unemployment rates (American Staffing Association, 2023). Thus, people appear aware of the risks linked to job automation; consequently, in an effort to reduce their job insecurity, they might be inclined to avoid jobs that they perceive at high risk of automation. But \u003cem\u003ehow\u003c/em\u003e do laypeople assess the likelihood of job automation? We seek to identify lay people\u0026rsquo;s beliefs about the likelihood of job automation, and test the correspondence between these lay beliefs and expert predictions. To do so, we investigate which jobs people think are more (vs. less) likely to be automated, the antecedents of those beliefs, and the extent to which these beliefs correspond with experts\u0026rsquo; predictions.\u003c/p\u003e\n\u003ch3\u003eExpert Predictions of Job Automation\u003c/h3\u003e\n\u003cp\u003eSubstantial research details the various effects of technological advancements on the demand for labor. Experts in labor economics and computer science agree that technology affects different jobs differently. Notably, Autor, Levy, and Murnane\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e proposed a foundation for understanding what causes certain jobs to be affected by automation more than other jobs, using a framework of task characteristics that machines are best suited to accomplish. The framework emphasizes the types of tasks for which computers or machines can substitute or complement human activity, and it categorizes tasks within a job as routine or nonroutine. \u003cem\u003eRoutine\u003c/em\u003e tasks adhere to explicit rules and involve executing a limited array of scripted actions, whereas \u003cem\u003enonroutine\u003c/em\u003e tasks are more complex and require problem-solving. According to the proposed framework, routine tasks are easier to \u003cem\u003esubstitute\u003c/em\u003e with existing technology. However, for nonroutine tasks, the degrees of substitution and complementarity depend on whether the task is analytic or manual. \u003cem\u003eAnalytic\u003c/em\u003e tasks demand the assimilation and processing of information, often in the form of knowledge-based work, whereas \u003cem\u003emanual\u003c/em\u003e tasks are physically oriented and require the manipulation of objects, animals, or people. Arguably, nonroutine analytic tasks can be complemented by technology, but nonroutine manual tasks are difficult to substitute or complement. These authors also mention interactive or social tasks as difficult to automate.\u003c/p\u003e \u003cp\u003eThis foundation has informed much subsequent research into the impact of automation on jobs\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, including efforts to quantify the potential for automation by examining the tasks and activities that are characteristic of different occupations. These various studies compare patent data, detail the tasks workers perform\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, or apply AI capabilities to the demands of different occupations\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Other studies rely on machine learning to categorize the automation potential of different jobs, synthesizing task-level data to draw broader occupational insights\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, or gathering expert predictions about how easily jobs can be taken over by machines\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Although these research findings vary in terms of their conclusions regarding the overall impact of job automation on the economy, they consistently emphasize that the nature of the specific tasks performed as part of a job is crucial in determining how susceptible that job is to automation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLay Beliefs of Job Automation\u003c/h2\u003e \u003cp\u003eImportantly, these existing insights into the drivers of future job automation mainly come from experts. But what do laypeople believe, and do these beliefs differ from experts' findings? Insights into laypeople\u0026rsquo;s predictions can be crucial for designing effective policies, incentives, and communication that might channel them toward jobs that involve lower risks of automation. Prior literature confirms that beliefs about occupations influence both behaviors and occupational decisions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, education and career choices tend to be forward-looking\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, such that young people\u0026rsquo;s aspirations reflect their perceptions of future career opportunities\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and people worry about their long-term economic well-being if they perceive that their jobs might be replaced\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Finally, research on job insecurity shows that perceived risk of job loss severely impacts health \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and life satisfaction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Considering the intensity and frequency of societal conversations about the threats posed by AI, robots, and other automation technology\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, it seems both timely and relevant to deepen our understanding of lay beliefs about job automation.\u003c/p\u003e \u003cp\u003eWe expect lay theories about job automation to simultaneously align with and diverge from expert predictions. In terms of alignment, previous research suggests that lay people perceive tasks demanding elaboration or that are nonroutine as more suitably executed by humans rather than machines\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For example, people express lower preferences for AI (vs. human) agents in medicine and other consumer domains because they believe AI agents cannot capture their unique personal characteristics\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Laypeople also believe that humans can perform high-level cognitive (i.e., analytic) tasks better than machines\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In social interaction tasks, they negatively react to non-human (e.g., robots, AI) service providers and prefer interacting with humans. This combined evidence implies a general preference for human workers to perform tasks that are nonrepetitive in nature, require complex cognitive skills, or involve human interaction. In this sense, lay beliefs about which jobs can (or cannot) be replaced by automation appear to align with expert predictions about which jobs are more or less likely to be automated.\u003c/p\u003e \u003cp\u003eYet when laypeople assess a job, they often evaluate the job as a whole, based on so-called occupational stereotypes \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Occupational stereotypes are \"preconceived attitudes about a particular occupation, the people in it, or one's suitability for it\"\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Two commonly applied occupational stereotypes involve prestige and gender\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Gender stereotypes mainly influence judgments about personal aptitude for a job, so we do not expect them to affect beliefs about automation likelihood. In contrast, we expect that the occupational prestige stereotype does relate to lay beliefs about job automation. As a broad construct, occupational prestige entails many referents, including status, socioeconomic ranking, level of training, occupational level, and levels of difficulty and responsibility\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Historically, automation has largely impacted and replaced blue-collar jobs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and observing such effects of automation, whether firsthand or among close others, and their further ripple effects on the economy seems likely to have shaped lay beliefs about job automation. Blue-collar work is often linked with low occupational prestige, so we predict that historical trends of automation in low-prestige roles might have fostered a lay theory that links (low) prestige to (high) automation likelihood\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Prestige and power are also closely intertwined, and power implies control over valued resources\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and dominance in decisions\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. If automation seems top-down, laypeople might believe that powerful, high-prestige figures influence which jobs get automated\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, using prestige stereotypes to inform job choices can have deeply detrimental effects, as demonstrated by BRIDGE, a European Commission\u0026ndash;funded project designed to aid students from low-income areas to build better careers. Project officers noticed that many students pursued post-secondary vocational training in economics and accounting, even though tasks in basic administration and bookkeeping are quickly being automated\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Through interviews with BRIDGE project associates, we learned that many low-income students continued to pursue vocational studies associated with jobs they consider more prestigious (e.g., entry-level, white-collar office jobs), even though those roles offer worse job prospects than available (blue-collar) alternatives (see \u003cem\u003eSI appendix\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eIndeed, the link between occupational prestige with factors that shield jobs from the risk of automation seems to be dissolving, especially as advances in generative AI enable the automation of knowledge work. As Brynjolfsson et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e demonstrate, machine learning capabilities extend across wages and education levels. Moreover, generative AI currently has achieved greater exposure in occupations marked by higher income and educational levels\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHence, we will investigate whether laypeople hold beliefs regarding the relationship between task characteristics (routine, analytic, manual, social) and the likelihood of automation that match those indicated by experts, as well as whether laypeople also rely on occupational stereotypes (i.e., job prestige) to make inferences about automation likelihood. We examine these questions in two stages. First, we measure laypeople\u0026rsquo;s perceptions of occupations from the Occupational Information Network (O*NET). Second, we assess the correspondence between our data and expert findings documented in previous research. Our findings demonstrate that, while laypeople rely on relevant (as defined by experts) task characteristics, such as routine, manual, and social aspects, to predict the likelihood of job automation, job prestige is also a crucial antecedent. We further demonstrate that lay beliefs exhibit low correspondence with expert predictions of job automation, particularly for occupations that are substantially exposed to AI, a finding that can be attributed to high reliance on prestige stereotypes. For illustrative purposes, all our data can be found here (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://job-automation-project.com\u003c/span\u003e\u003cspan address=\"https://job-automation-project.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This app provides insights into all data and relationships at the aggregate level but also allows users to zoom in on any single occupation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe conducted a comprehensive analysis of lay beliefs about job automation across 542 occupations. We used O*NET to compile a list of 821 distinct occupational titles, which we narrowed down on the basis of a pretest (N\u0026thinsp;=\u0026thinsp;888), in which we sought specifically to identify which job titles laypeople could understand. Then, in the main study (N\u0026thinsp;=\u0026thinsp;3500), we asked each participant to rate a subset of 50 occupations, using scales pertaining to the perceived likelihood of job automation (dependent variable) and its antecedents, including the extent to which the job involves routine, manual, analytic, and social tasks, as well as prestige and gender stereotypes (independent variables). Using both ordinary least squares (OLS) and hierarchical linear model (HLM) regressions, we examine the relationships between job characteristics, job stereotypes, and the perceived likelihood of job automation.\u003c/p\u003e \u003cp\u003eFirst, we tested our predictions using the job as the unit of analysis, such that we averaged the scores for each job. The regression results revealed significant associations between most ratings of (expert-based) job characteristics and the perceived likelihood of job automation. Specifically, we uncover a positive and significant relationship between perceptions of the extent to which a job contains routine tasks and this perceived automation likelihood \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% confidence intervals (CI) = (7.62, 11.44). Further, we find negative and significant relationships between perceptions of the extent to which a job contains manual \u003cem\u003eb\u003c/em\u003e = -5.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = (-6.56, -4.47) and social \u003cem\u003eb\u003c/em\u003e = -4.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = (-5.43, -3.20) tasks and the perceived likelihood of job automation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Hence, regarding task characteristics, laypeople\u0026rsquo;s beliefs align with expert opinions: They perceive occupations involving physical labor, substantial interpersonal interaction, or a high degree of nonroutine tasks as less susceptible to automation. However, we do not find a significant relationship between perceptions of the extent to which a job contains analytic tasks and automation likelihood (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14). Furthermore, occupational prestige is significantly associated with the perceived likelihood of job automation \u003cem\u003eb\u003c/em\u003e = -8.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = (-11.14, -6.43) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), even if we control for the other relevant antecedents. People believe that less prestigious jobs are more likely to be automated than are more prestigious jobs. As expected, gender stereotypes do not significantly affect the perceived likelihood of job automation (gender: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOLS Linear Regression of Lay Beliefs of Likelihood of Job Automation as a Function of Task Characteristics and Job Stereotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003es.e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoutine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrestige\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: N\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;542 jobs, Model fit: \u003cem\u003eF\u003c/em\u003e (6,531)\u0026thinsp;=\u0026thinsp;89.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Residual SE\u0026thinsp;=\u0026thinsp;8.431, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e =\u003c/sup\u003e 0.50, Adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e =\u003c/sup\u003e 0.49.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSecond, we assessed the robustness of these results by using individual ratings as the unit of analysis (N\u0026thinsp;=\u0026thinsp;19,784). In an HLM regression with varying intercepts, we specify different baseline levels of the likelihood of automation across jobs and thereby test for the relationship between perceived job automation and all the antecedents, while also accounting for within-job variation in automation likelihood. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e plots the predicted mean scores for the likelihood of automation for all occupations, ranked from lowest to highest, and their 95% Bayesian credible intervals (BCI). At the observation level, we also account for significant variation in the baseline likelihood of automation across occupations, which ranges from 20\u0026ndash;70% on average.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e further show that the HLM replicates the results of our OLS regression at the job level: We find a negative relationship between the likelihood of automation and prestige \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ePrestige\u003c/em\u003e\u003c/sub\u003e = -0.810, 95% BCI = (-1.224, -0.403), such that when prestige is lower, the likelihood of automation is rated as higher. Furthermore, we note a significant positive effect of the routine task score on the likelihood of automation \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eRoutine\u003c/em\u003e\u003c/sub\u003e = 0.659, 95% BCI = (0.263, 1.062) and significant negative effects of both manual \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eManual\u003c/em\u003e\u003c/sub\u003e = -0.431, 95% BCI = (-0.797, -0.062) and social \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eSocial\u003c/em\u003e\u003c/sub\u003e = -0.671, 95% BCI = (-1.079, -0.256) tasks. Again, gender and analytical tasks are not significant predictors \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/sub\u003e = 0.142, 95% BCI = (-0.347, 0.650), \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eAnalytic\u003c/em\u003e\u003c/sub\u003e = -0.317, 95% BCI = (-0.701, 0.070).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter Estimates of the HLM Predicting the Likelihood of Automation as a Function of Task Characteristics and Job Stereotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.5% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.5% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e97.5% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e99.5% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e(Intercept)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eManual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRoutine\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocial\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnalytic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrestige\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: N\u0026thinsp;=\u0026thinsp;19,784 observations, Log \u0026ndash; posterior = -96423.62, CI\u0026thinsp;=\u0026thinsp;credible interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe plot of model fit across 542 occupations demonstrates the negative relationship between the perceived likelihood of job automation and prestige, even at an observation level. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows, in gray, the base chances of automation for different jobs, each of which has a different starting point. Additionally, the shiny app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://job-automation-project.com\u003c/span\u003e\u003cspan address=\"https://job-automation-project.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) offers the opportunity to view individual plots for each job. The consistent trend in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates how job prestige affects the perceived chances of the job being automated. The aggregate-level model, in blue, represents the relationship between automation likelihood and prestige for the average occupation. Individual occupations with higher or lower baseline job automation scores vary around this average. Using the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we can reproduce the effects observed in the job-level analysis. Task characteristics (routine, manual, and social) still predict the perceived likelihood of job automation significantly, and the coefficients are significant based on the 95% BCI. Prestige also remains a significant predictor, with a 99% BCI, highlighting its robust influence on perceived job automation potential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith a preregistered, controlled experiment, we also investigated the causal relationship between prestige and the perceived job automation likelihood (see \u003cem\u003eSI Appendix\u003c/em\u003e). We assessed whether the same jobs (e.g., auditor) are considered less likely to be automated when they are in high-prestige industries (e.g., corporate law firms) rather than in low-prestige industries (e.g., correctional facilities). Controlling for job attractiveness, we found that job prestige affects perceived automation likelihood, such that high-prestige jobs are perceived as less likely to be automated. We confirm this effect across various jobs and industry types. Thus, these results show that prestige stereotype exerts a causal impact on perceptions of automation risk.\u003c/p\u003e\n\u003ch3\u003eCorrespondence of Lay Beliefs with Expert Predictions\u003c/h3\u003e\n\u003cp\u003eIn the preceding section, we established that laypeople use expert-identified task characteristics as well as occupational prestige stereotypes to predict job automation likelihood. Next, we aim to identify the extent to which these lay beliefs correspond with expert predictions. To facilitate this comparison, we sought out studies that quantify job automation effects for each occupation, which may manifest as a probability score, exposure to technology, or risk of substitution\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Experts have quantified automation's potential by examining the tasks and activities characteristic of different occupations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Because we are interested in perceived differences in risk of automation across jobs, we focused on expert estimates of the impact of automation for specific occupations. To that end, we identified two relevant sources of expert estimates for our comparative analysis: Frey and Osborne\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, who were among the first to calculate the risk of automation at the occupational level\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, and the recent study by Felten, Raj, and Seamans\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which includes considerations of recent developments in AI.\u003c/p\u003e \u003cp\u003eThe susceptibility to automation score proposed by Frey and Osborne\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e offers probabilistic estimates of job replacement due to computerization, based on a task framework that includes routine versus nonroutine and cognitive versus manual tasks\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Their model provides estimates of the potential for automation across various occupations. For this analysis, we include only 490 of the 542 occupations in our previous analysis, which reflects necessary exclusions of occupations that had not appeared in the expert studies or that the pretest results indicated were difficult to understand. Such discrepancies arise even though both data sets draw from the same O*NET database. When we compare Frey and Osborne\u0026rsquo;s probability estimates with our lay beliefs data set, we uncover a significant positive association, r(490)\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = (0.40, 0.54) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting general agreement about the likelihood of job automation between Frey and Osborne\u0026rsquo;s predictions and the lay beliefs of our respondents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, Frey and Osborne\u0026rsquo;s work has prompted some criticism\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The data are approximately a decade old, and in the intervening years, AI has made huge leaps forward, with massive potential to impact work. Just a few months after the broad market introduction of language models, for example, 50% of surveyed managers reported that they had already tried out AI applications\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Furthermore, workers who use AI applications demonstrate increased productivity and quality of work, especially knowledge workers performing tasks like writing and coding\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. With such rapid adoption and impressive effects, it becomes imperative to detail how lay beliefs about job automation correspond with the effects of AI applications and conduct updated analyses, using more recent expert ratings.\u003c/p\u003e \u003cp\u003eAccordingly, we turn to a score, 'AI exposure on occupation,' of an occupation's exposure to AI applications provided by Felten, Raj, and Seamans\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To obtain this forward-looking measure, these authors associated 10 AI applications with 52 O*NET occupational abilities and then calculated the required ability level and occupational-level exposure. In this case, we find that lay beliefs about job automation do not significantly correlate with expert estimates of exposure to AI, \u003cem\u003er\u003c/em\u003e(436) = -0.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese two comparisons suggest that lay beliefs correspond to some extent to expert predictions, but this correspondence weakens when the expert scores include more recent AI applications. The relatively low correspondence with the occupational exposure to AI score might be attributed, at least in part, to a reliance on prestige when laypeople make predictions about the likelihood of job automation, whereas, in reality, prestige is becoming less relevant as a predictor of whether a job is likely to be automated\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. To further examine this, we tested the relationship between prestige perceptions rated by our respondents and occupation computerization probability and AI exposure based on Frey and Osborne and Felten, Raj and Seamans, respectively. These analyses reveal opposing results. Relating prestige perceptions to occupation computerization probability from Frey and Osborne, we find a significant negative relationship \u003cem\u003er\u003c/em\u003e(490) = -0.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = (-0.66, -0.55) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), in line with the relationship between prestige and lay beliefs about job automation. Contrary, if we relate the prestige perceptions to AI exposure as captured by Felten, Raj and Seamans, we find a positive association, \u003cem\u003er\u003c/em\u003e(436)\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI \u003cem\u003e=\u003c/em\u003e (0.48, 0.61): The jobs considered more prestigious are more exposed to AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It should be noted that the latter finding aligns closely with the observations shared by the BRIDGE associates in our interviews, who pointed to (low-income) students making career choices based on prestige and opting for occupations with high automation likelihood.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research is the first to explore laypeople’s beliefs about the antecedents of job automation likelihood, which is important given rapidly advancing technological developments and the relevance of such lay beliefs for people’s choice of study and careers. We identify key antecedents of lay beliefs about job automation and compare them with expert predictions. Although laypeople draw on factors directly related to job automation, such as the routine nature of tasks or the extent of manual and social interactions involved, they also rely on occupational prestige, which, in reality, has limited relevance. Our analysis of 542 occupations shows a negative association between the perceived prestige and the perceived likelihood of automation. By complementing our OLS analysis with Bayesian HLM analysis, we provide robust evidence for the relationship between the dependent and predictive variables. Our follow-up experiment further strengthens our findings, suggesting a causal link between prestige and job automation perceptions. Further analyses examining the alignment between laypeople’s predictions and expert measures are inconclusive. When experts operationalize the risk of automation as computerization probability, we observe a positive correlation between both. However, when experts operationalize the risk of automation as occupational exposure to AI, we find no correlation between lay beliefs and experts’ predictions and even a positive correlation between prestige perceptions and occupational exposure to AI. Given that the adoption of AI by firms is increasing rapidly, this lack of correspondence between lay beliefs and expert predictions is alarming and underscores the importance of studying lay beliefs.\u003c/p\u003e \u003cp\u003eOur research is the first to focus on the layperson's perspective and thus adds to the job automation literature, most of which has sought to identify job or task characteristics that increase susceptibility to automation as well as its broader implications for labor markets\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Further adding to the literature on occupational beliefs and stereotypes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, our findings highlight the significant influence of occupational stereotypes, particularly job prestige, on laypeople’s perceptions of job automation likelihood. We uncover that job prestige relates negatively to perceived job automatability; more prestigious jobs are assumed to be less susceptible to automation. Yet contrary to this lay belief, expert predictions about the effect of AI applications on jobs suggest a positive association between occupational prestige perceived by our respondents and exposure to AI based on expert predictions. This positive relationship resonates with the findings of our interviews in the BRIDGE case, which included observations that more prestigious, white-collar jobs are both more popular and more at risk of automation. This large gap underscores the importance of considering and addressing laypeople’s (potentially erroneous) beliefs.\u003c/p\u003e \u003cp\u003eIn particular, the association between occupational prestige and perceived automation likelihood suggests the need for targeted interventions to reshape laypeople’s perceptions about job prospects and improve their understanding of job automation determinants. The findings also emphasize the relevance of realigning skill development and training programs to reflect the evolving job market. The importance of studying lay beliefs regarding job automation pertains not only to those seeking jobs or choosing a future career but also those giving career advice (career mentors, parents), whose understanding and recommendations can strongly influence others’ career choices\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Therefore, our work suggests that a collaborative effort by policymakers, educators, and industry stakeholders is needed to address the discrepancies between lay beliefs and expert predictions, ensuring that skill and knowledge development matches future demands and fosters a resilient labor market. Continued research might test specific interventions designed to enhance predictive accuracy about job automation likelihood, to help laypeople develop expectations that align more closely with expert assessments. Our work provides an important initial step to facilitate such efforts, by pointing to the important but potentially misguided belief that more prestigious jobs are less susceptible to automation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eList of Occupations\u003c/strong\u003e. To compile a comprehensive list of occupational titles, we relied on the O*NET database. The initial list consisted of 821 distinct occupational titles. To ensure these titles were easily understood and unambiguous, we conducted a pretest with 888 participants (M\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 39.88 years, female\u0026thinsp;=\u0026thinsp;41%), who we assigned randomly to review 50 occupations and rate the clarity of each occupational title on a scale, ranging from 1 (completely unclear) to 6 (completely clear). Only occupations with mean clarity scores above the midpoint (exceeding 3.5) were retained, resulting in a final list of 542 occupations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e. Before the data collection, respondents provided informed consent to participate in the study. The survey and data collection procedure received ethics approval from the IRB of our institution. The data collection procedure involved recruiting participants from MTurk\u0026rsquo;s Panel Workers through the Cloud Research platform. Each participant was randomly assigned to rate 50 occupations on one of the following scales: perceived likelihood of job automation, analytic, gender, manual, prestige, routine, or social. Participants rated 542 jobs, and the sample consisted of 3500 participants with a mean age of 38.75 years, 46% of whom identified as female. Before rating each occupation, they received the definitions of each concept; all definitions were adopted from Merriam-Webster\u0026apos;s (2020) dictionary and paraphrased for ease of comprehension. Participants in all conditions then had to answer a straightforward question based on the definition. Participants did not proceed unless they answered the question correctly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScales and Measurements\u003c/strong\u003e. We measured perceptions related to job automation likelihood, job characteristics, gender stereotypes, and occupational prestige, as outlined subsequently.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntecedents Based on the Expert Predictions.\u003c/strong\u003e Considering predictors derived from expert findings that the extent to which jobs involve routine, manual, analytic tasks and social interactions is related to automatability, we measured laypeople\u0026rsquo;s perceptions of these four job characteristics. Each characteristic was measured on a 5-point Likert scale, such that higher scores indicate a stronger presence of the characteristic in the occupation. Participants read the following descriptions of each antecedent.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAnalytic: An occupation is analytic or cognitive to the extent that it consists of cognitive or knowledge work, such as information processing, analysis, planning, controlling, or problem-solving.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eManual: An occupation is manual if it involves physical labor, such as handling, moving, or manipulating any person, animal, or thing.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eRoutine: An occupation is routine if it follows precise, well-understood, repetitive, and standard procedures.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSocial: An occupation is social if it involves communicating, relationship building, presenting, negotiating, advocating, or caring for others. Please pick out the statement that best gives your personal opinion of the amount of social that such a job has.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eAntecedents Based on Job Stereotypes\u003c/h2\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eGender: The occupation will be considered masculine if it requires skills and characteristics typically associated with men, such as muscular, rugged, competent, and confident. The occupation will be considered feminine if it requires skills and characteristics typically associated with women, such as fine features, warm, good-natured, friendly.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePrestige: Prestige is a rank granted to those who are recognized and respected for their skills, success, or knowledge. Someone worthy to look up to.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variable\u003c/strong\u003e. Perceived Likelihood of Job Automation: Labor automation is the practice of substituting human labor with technology to perform specific tasks or jobs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Preparation for Hierarchical Linear Modeling (HLM).\u003c/strong\u003e Not all occupations had an equal number of responses for all variables because each variable\u0026apos;s response was collected from a different participant to avoid potential halo effects. For example, the Audiologist occupation had 30 complete observations, but the Librarian had 34 complete observations, spanning all measures, so the response counts for each occupation differ. This variance reflects the random assignment of 50 occupations to each participant from a full list of 542 occupations and different measures. Therefore, the final sample consisted of 138,488 observations, considering all the variables.\u003c/p\u003e\n\u003cp\u003eEach respondent rated only 50 occupations on one characteristic. We restructured the data to create a detailed data set that logs each occupation\u0026apos;s ratings on all task characteristics, job stereotypes, and the perceived likelihood of job automation. In turn, we could randomly pair participant responses about various characteristics to generate occupation-specific profiles. For each observation, the rating for each characteristic of an occupation came from a different respondent. The minimum number of observations per occupation is 23, and the maximum is 80. Respondents were asked to rate 10 of the 542 occupations twice, because the occupations were replicated in the questionnaire due to a coding error. The final data set consists of 19,784 observations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Specifications.\u003c/strong\u003e We specified a random-effects regression with errors clustered at the occupation level. The model specification is:\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:L{A}_{ij}={\\alpha\\:}_{j}+{\\beta\\:}_{k}{C}_{ijk}+{ϵ}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003e, and\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1.1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{j}={\\alpha\\:}_{0}+{\\nu\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003ej\u003c/em\u003e is the occupation indicator, \u003cem\u003ei\u003c/em\u003e represents the observation, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e indicates the likelihood of automation score of each observation \u003cem\u003ei\u003c/em\u003e for each occupation \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e represents the scores of each job characteristic \u003cem\u003ek\u003c/em\u003e for observation \u003cem\u003ei\u003c/em\u003e for each occupation \u003cem\u003ej\u003c/em\u003e. The intercept \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e is occupation-specific, so the likelihood of automation randomly varies across occupations, distributed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\left({\\alpha\\:}_{0},\\:\\tau\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e. Then \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003ereflects within-occupation variability, thereby indicating the strength of the relationship between an occupation-specific likelihood of automation and its job characteristics, distributed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\left(0,\\:\\sigma\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e. The parameters of interest are \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003e, or the impact of each antecedent on the likelihood of automation, which are specified at the aggregate level, such that the impact of all antecedents on the likelihood of automation is assumed to be similar across occupations. Some occupations have a higher likelihood of automation than others, but the impact of all the antecedents is the same across occupations.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShiny app\u003c/strong\u003e. We have created a shiny app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://job-automation-project.com\u003c/span\u003e\u003c/span\u003e) for illustrative purposes, visualizing the results demonstrated in this paper across three pages. On Page 1, \u0026quot;Main Results,\u0026quot; users can select a predictor from task characteristics to job stereotypes and view the plotted relationship with the lay beliefs of job automation. By hovering over each dot on the plot, users can see which job the dot represents along with its mean values. On Page 2, \u0026quot;Beliefs per Occupation,\u0026quot; users can select an occupation and either a task characteristic or job stereotype of interest to see the plotted relationship at the observational level for each job. To make navigation easier, users can first select a cluster of interest and then choose a job within that cluster. On Page 3, \u0026quot;Expert Predictions,\u0026quot; users can choose between expert predictions of job automation, either \u0026quot;Occupation Computerization Probability\u0026quot; by Frey and Osborne (2017) or \u0026quot;Occupational Exposure to AI\u0026quot; by Felten, Raj, and Seamans. Similar to Page 1, users can hover over the dots to see which occupation each dot represents, along with the mean scores.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData files, analysis codes, and study instructions can be found on the Open Science Framework (https://osf.io/buhkc/?view_only=a3f541c9b17c40d3b6d63ed4a0e6b36d).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eA.A., M.A.T., and S.P. designed research; A.A. performed research; A.A. and A.M.F. analyzed data; A.A., M.A.T., S.P., and A.M.F. wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification:\u0026nbsp;\u003c/strong\u003ePsychological and Cognitive Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eWe thank the Erasmus Research Institute of Management for their financial support for the data collection and the Research Software Engineering and Consulting team at Rotterdam School of Management for developing a Shiny app. We are also grateful to the associates of the BRIDGE program for sharing their insights.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D. \u0026amp; Lensman, T. Regulating Transformative Technologies. \u003cem\u003eNBER Working Paper Series\u003c/em\u003e (2023) doi:10.3386/w31461.\u003c/li\u003e\n\u003cli\u003eFrank, M. R. \u003cem\u003eet al.\u003c/em\u003e Toward understanding the impact of artificial intelligence on labor. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 6531\u0026ndash;6539 (2019).\u003c/li\u003e\n\u003cli\u003eBrynjolfssonn, E. \u0026amp; Mitchell, T. What can machine learning do? Workforce implications. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e358\u003c/strong\u003e, 1530\u0026ndash;1534 (2017).\u003c/li\u003e\n\u003cli\u003eFelten, E. \u0026amp; Raj, M. Occupational Heterogeneity in Exposure to Generative AI. \u003cem\u003eSSRN \u003c/em\u003e(2023) doi:10.2139/ssrn.4414065.\u003c/li\u003e\n\u003cli\u003eBrynjolfsson, E. \u003cem\u003eet al.\u003c/em\u003e Generative AI at Work. \u003cem\u003eNBER Working Paper Series\u003c/em\u003e (2023) doi:10.3386/w31161.\u003c/li\u003e\n\u003cli\u003eKorst, J. \u0026amp; Puntoni, S. 5 Ways Marketing and Sales Leaders Can Embrace GenAI. \u003cem\u003eHarvard Business Review\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eNoy, S. \u0026amp; Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e381\u003c/strong\u003e, 187\u0026ndash;192 (2023).\u003c/li\u003e\n\u003cli\u003eChui, M. \u003cem\u003eet al.\u003c/em\u003e The economic potential of generative AI: The next productivity frontier. \u003cem\u003eMcKinsey \u0026amp; Company\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eEloundou, T., Manning, S., Mishkin, P. \u0026amp; Rock, D. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. \u003cem\u003eArXiv\u003c/em\u003e (2023) doi:10.48550/arXiv.2303.10130.\u003c/li\u003e\n\u003cli\u003eYam, K. C., Tang, P. M., Jackson, J. C., Su, R. \u0026amp; Gray, K. The Rise of Robots Increases Job Insecurity and Maladaptive Workplace Behaviors: Multimethod Evidence. \u003cem\u003eJournal of Applied Psychology\u003c/em\u003e (2023) doi:10.1037/apl0001045.\u003c/li\u003e\n\u003cli\u003eGranulo, A., Fuchs, C. \u0026amp; Puntoni, S. Psychological reactions to human versus robotic job replacement. \u003cem\u003eNat Hum Behav\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 1062\u0026ndash;1069 (2019).\u003c/li\u003e\n\u003cli\u003eAutor, D. H., Levy, F. \u0026amp; Murnane, R. J. The Skill Content of Recent Technological Change: and Empirical Exploration. \u003cem\u003eQ J Econ\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 1279\u0026ndash;1333 (2003).\u003c/li\u003e\n\u003cli\u003eFrey, C. B. \u0026amp; Osborne, M. A. The future of employment: How susceptible are jobs to computerisation? \u003cem\u003eTechnol Forecast Soc Change\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 254\u0026ndash;280 (2017).\u003c/li\u003e\n\u003cli\u003eBrynjolfsson, E., Mitchell, T. \u0026amp; Rock, D. What Can Machines Learn and What Does It Mean for Occupations and the Economy? \u003cem\u003eAEA Papers and Proceedings\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 43\u0026ndash;47 (2018).\u003c/li\u003e\n\u003cli\u003eDas, S. \u003cem\u003eet al.\u003c/em\u003e Learning Occupational Task-Shares Dynamics for the Future of Work. in \u003cem\u003eProceedings of the AAAI/ACM Conference on AI, Ethics, and Society\u003c/em\u003e 36\u0026ndash;42 (2020). doi:10.1145/3375627.3375826.\u003c/li\u003e\n\u003cli\u003eWebb, M. The Impact of Artificial Intelligence on the Labor Market. \u003cem\u003eSSRN \u003c/em\u003e(2020) doi:http://dx.doi.org/10.2139/ssrn.3482150.\u003c/li\u003e\n\u003cli\u003eFelten, E., Raj, M. \u0026amp; Seamans, R. Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. \u003cem\u003eStrategic Management Journal\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 2195\u0026ndash;2217 (2021).\u003c/li\u003e\n\u003cli\u003eNedelkoska, L. \u0026amp; Quintini, G. Automation, skills use and training. \u003cem\u003eOECD Social, Employment and Migration Working Papers No.202\u003c/em\u003e (2018) doi:10.1787/2e2f4eea-en.\u003c/li\u003e\n\u003cli\u003eArntz, M., Gregory, T. \u0026amp; Zierahn, U. The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. (2016) doi:10.1787/5jlz9h56dvq7-en.\u003c/li\u003e\n\u003cli\u003eAnteby, M., Curtis, K. C. \u0026amp; DiBenigno, J. Three Lenses on Occupations and Professions in Organizations: Becoming, Doing, and Relating. \u003cem\u003eAcad Manag Ann\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 183\u0026ndash;244 (2016).\u003c/li\u003e\n\u003cli\u003eGottfredson, L. S. Circumscription and Compromise: A Developmental Theory of Occupational Aspirations. \u003cem\u003eJournal of Counseling Psychology Monograph\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 545\u0026ndash;579 (1981).\u003c/li\u003e\n\u003cli\u003eFouad, N. A. Work and vocational psychology: Theory, research, and applications. \u003cem\u003eAnnu Rev Psychol\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 543\u0026ndash;564 (2007).\u003c/li\u003e\n\u003cli\u003eWall, J., Covell, K. \u0026amp; Macintyre, P. D. Implications of Social Supports for Adolescents\u0026rsquo; Education and Career Aspirations. \u003cem\u003eCanadian Journal of Behavioral Science \u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e, 63\u0026ndash;71 (1999).\u003c/li\u003e\n\u003cli\u003eReichert, A. R. \u0026amp; Tauchmann, H. Workforce reduction, subjective job insecurity, and mental health. \u003cem\u003eJ Econ Behav Organ\u003c/em\u003e \u003cstrong\u003e133\u003c/strong\u003e, 187\u0026ndash;212 (2017).\u003c/li\u003e\n\u003cli\u003eGeishecker, I. Simultaneity bias in the analysis of perceived job insecurity and subjective well-being. \u003cem\u003eEcon Lett\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 319\u0026ndash;321 (2012).\u003c/li\u003e\n\u003cli\u003eVerma, P. \u0026amp; De Vynck, G. ChatGPT took their jobs. Now they walk dogs and fix air conditioners. \u003cem\u003eThe Washington Post \u003c/em\u003e(2023).\u003c/li\u003e\n\u003cli\u003eLeonhardt, M. Some workers are worried that ChatGPT will replace their jobs. They might be right. \u003cem\u003eFortune \u003c/em\u003e(2023).\u003c/li\u003e\n\u003cli\u003eLanger, M. \u0026amp; Landers, R. N. The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. \u003cem\u003eComput Human Behav\u003c/em\u003e \u003cstrong\u003e123\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eMahmud, H., Islam, A. K. M. N., Ahmed, S. I. \u0026amp; Smolander, K. What influences algorithmic decision-making? A systematic literature review on algorithm aversion. \u003cem\u003eTechnol Forecast Soc Change\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eCastelo, N., Bos, M. W. \u0026amp; Lehmann, D. R. Task-Dependent Algorithm Aversion. \u003cem\u003eJournal of Marketing Research\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 809\u0026ndash;825 (2019).\u003c/li\u003e\n\u003cli\u003eLongoni, C., Bonezzi, A. \u0026amp; Morewedge, C. K. Resistance to Medical Artificial Intelligence. \u003cem\u003eJournal of Consumer Research\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 629\u0026ndash;650 (2019).\u003c/li\u003e\n\u003cli\u003eGranulo, A., Fuchs, C. \u0026amp; Puntoni, S. Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts. \u003cem\u003eJournal of Consumer Psychology\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 72\u0026ndash;80 (2021).\u003c/li\u003e\n\u003cli\u003eWaytz, A. \u0026amp; Norton, M. I. Botsourcing and Outsourcing: Robot, British, Chinese, and German Workers Are for Thinking - Not Feeling - Jobs. \u003cem\u003eEmotion\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 434\u0026ndash;444 (2014).\u003c/li\u003e\n\u003cli\u003eLee, M. K. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. \u003cem\u003eBig Data Soc\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, (2018).\u003c/li\u003e\n\u003cli\u003eHe, J. C., Kang, S. K., Tse, K. \u0026amp; Toh, S. M. Stereotypes at work: Occupational stereotypes predict race and gender segregation in the workforce. \u003cem\u003eJ Vocat Behav\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eOswald, P. A. Sex-typing and prestige ratings of occupations as indices of occupational stereotypes. \u003cem\u003ePercept Mot Skills\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 953\u0026ndash;959 (2003).\u003c/li\u003e\n\u003cli\u003eGlick, P., Wilk, K. \u0026amp; Perreault, M. Images of Occupations: Components of Gender and Status in Occupational Stereotypes. \u003cem\u003eSex Roles\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 565\u0026ndash;582 (1995).\u003c/li\u003e\n\u003cli\u003eAcemoglu, D. \u0026amp; Restrepo, P. Automation and new tasks: How technology displaces and reinstates labor. \u003cem\u003eJournal of Economic Perspectives\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 3\u0026ndash;30 (2019).\u003c/li\u003e\n\u003cli\u003eSmith, P. K. \u0026amp; Galinsky, A. D. The Nonconscious Nature of Power: Cues and Consequences. \u003cem\u003eSoc Personal Psychol Compass\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 918\u0026ndash;938 (2010).\u003c/li\u003e\n\u003cli\u003eKteily, N., Saguy, T., Sidanius, J. \u0026amp; Taylor, D. M. Negotiating power: Agenda ordering and the willingness to negotiate in asymmetric intergroup conflicts. \u003cem\u003eJ Pers Soc Psychol\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 978\u0026ndash;995 (2013).\u003c/li\u003e\n\u003cli\u003eBrynjolfsson, E., Mitchell, T. \u0026amp; Rock, D. What Can Machines Learn and What Does It Mean for Occupations and the Economy? \u003cem\u003eAEA Papers and Proceedings\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 43\u0026ndash;47 (2018).\u003c/li\u003e\n\u003cli\u003eArntz, M., Gregory, T. \u0026amp; Zierahn, U. Revisiting the risk of automation. \u003cem\u003eEcon Lett\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 157\u0026ndash;160 (2017).\u003c/li\u003e\n\u003cli\u003ePeng, S., Kalliamvakou, E., Cihon, P. \u0026amp; Demirer, M. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. \u003cem\u003eArXiv\u003c/em\u003e (2023) doi:https://doi.org/10.48550/arXiv.2302.06590.\u003c/li\u003e\n\u003cli\u003eSiy, J. O. \u003cem\u003eet al.\u003c/em\u003e Does the Follow-Your-Passions Ideology Cause Greater Academic and Occupational Gender Disparities Than Other Cultural Ideologies? \u003cem\u003eJ Pers Soc Psychol\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 548\u0026ndash;570 (2023).\u003c/li\u003e\n\u003cli\u003eGelman, A., Carlin, J. B., Stern, H. S. \u0026amp; Rubin, D. B. \u003cem\u003eBayesian Data Analysis.\u003c/em\u003e (Chapman \u0026amp; Hall/CRC, 2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"job automation, lay beliefs, occupational stereotype, prestige","lastPublishedDoi":"10.21203/rs.3.rs-6464311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6464311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Advancements in automation technology, including AI and robotics, threaten many occupations. Prior work has shed light on experts’ views of automation risk and identified key determinants of the risk for different occupations. Yet research into lay beliefs about job automation risk is limited, a gap this research addresses. This is important because students and job seekers should consider automation risk when making educational and occupational choices. Understanding what drives those beliefs and how they differ from expert predictions could help mitigate the threats linked to future economic displacement (e.g., by informing effective communication strategies about jobs at high risk of automation). A comprehensive study involving 4,388 respondents assessing 542 occupations demonstrates both alignment and divergence between laypeople’s perceptions and expert opinions. Crucially, job prestige is a key but often misleading predictor of lay beliefs about job automation. These findings have significant implications for workers, educators, and policymakers.","manuscriptTitle":"Predicting the Future of Work: Lay Beliefs about Job Automation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 19:32:05","doi":"10.21203/rs.3.rs-6464311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5734fe00-420c-4930-9f2d-e5056a932271","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47949184,"name":"Biological sciences/Psychology"},{"id":47949185,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-05-05T19:32:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 19:32:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6464311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6464311","identity":"rs-6464311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}