key: cord-0987414-0p7wu79o
authors: Ghimire, Ramesh; Skinner, Jim; Carnathan, Mike
title: “Who perceived automation as a threat to their jobs in metro atlanta: Results from the 2019 Metro Atlanta Speaks survey”
date: 2020-08-30
journal: Technol Soc
DOI: 10.1016/j.techsoc.2020.101368
sha: d8f42ad3edcaf36d55183e1504e2ea1466027a68
doc_id: 987414
cord_uid: 0p7wu79o

While ethnic minorities, less-educated or less-skilled workers, and low-income workers are, in general, deemed more vulnerable to automation, the literature has not adequately investigated whether or not these sociodemographic groups perceive automation as a threat to their jobs. Using the 2019 Metro Atlanta Speaks survey, we find that high-income residents and residents with a graduate or a professional degree did not perceive automation as a threat to their jobs, but relatively older residents, blacks or African Americans, and low-income residents perceived automation as a threat to their jobs. Although Hispanics or Latinos and less-educated residents are identified to be more vulnerable to automation, they did not perceive automation as a threat to their jobs. Hence, automation is most likely to make Hispanics or Latinos and less-educated residents unemployed in metro Atlanta as they do not perceive automation as a threat to their jobs despite being deemed more vulnerable to automation.

While technological progress, in general, improves efficiency and increases labor productivity, it 2 also increases unemployment risk in society as it eliminates several jobs or occupation families 3 employing less-skilled or less-educated workers. One such example of technological progress 4 threatening many jobs or occupation families is job automation. A most recent study by the 5 Brookings Institution indicates that one-quarter of the United States (U.S.) employment in 2016 6 (36 million jobs) will face "high exposure" to automation by 2030, with more than 70 percent of 7 current tasks at the risk of automation in the coming decades. Another 36 percent of employment 8 in 2016 (52 million jobs) are expected to experience "medium exposure" to automation by 2030 9 [1] . It is almost sure that automation, to some extent, will impact every occupation, but the 10 severity of this impact will vary across sociodemographic groups. While previous studies have 11 indicated that less-educated or less-skilled workers, ethnic minorities, or low-income workers younger, less-educated, or low-skilled workers in the U.S. [6] . If jobs involve repetitive functions 1 with very minimal human interactions, those jobs are also at high risk of being automated in the 2 future [7, 8, 9] . For instance, warehouse tasks, such as data collection, software operations, 3 inventory control, and inventory distribution and documentation involve repetitive tasks and also 4 do not require any human interactions, in most of the cases. Accordingly, warehouse-related jobs 5 are expected to be increasingly automated in the future. In many cases, low-paying jobs comprise 6 repetitive functions, making these jobs more vulnerable to automation. The U.S. Council of 7 Economic Advisers (2016) estimated that jobs paying less than $20 an hour have an 83 percent 8 probability of being automated in the coming decades [10] . Regarding demographic 9 characteristics, males and non-white ethnic groups (e.g., blacks or African Americans, Hispanics 10 or Latinos, or American Indians) are deemed to be more vulnerable to automation because of the 11 nature of the job or occupation family involved and the prevalent sociodemographic 12 characteristics of the typical worker in that occupation family [1, 6] . 13 In this study, we investigate the sociodemographic characteristics of employed residents 14 who perceived automation as a threat to their jobs in metro Atlanta. This study is timely and 15 policy-relevant for several reasons. The metro Atlanta is the economic capital of the Southeast 16 region in the U.S. and is headquarters for the 26 Fortune 1,000 companies, generating a 17 combined $415 billion of aggregate revenue in 2018 [11] . Compared to other peer metro regions, 18 metro Atlanta's business climate is more investment-friendly, and a recent study from the 19 personal finance website WalletHub (2019) ranks metro Atlanta among the fastest-growing cities 20 in the U.S., based on the metro's sociodemographic characteristics, including, job openings and 21 the strength of the local economy [12] . The ten core counties in metro Atlanta have nearly 2.4 22 million workers, and job growth in metro Atlanta is relatively stronger compared to the nation. probability of automation in the coming decades or so [13] . Note that office and administrative 16 support; transportation and material moving; food preparation and serving related; construction 17 and extraction; production operation; and farming, fishing, and forestry are identified as the 18 occupations where automation risk is estimated at 50 percent or more in the coming decades [1] . 19 While the average annual wage rate across all occupations in metro Atlanta is nearly $52,000, it 20 is nearly $37,000 in the occupation families where automation risk is estimated to be 50 percent 21 or more in the coming decades [1, 13] .

J o u r n a l P r e -p r o o f As we discussed earlier, low-skilled or less-educated workers, low-wage workers, males, 1 and ethnic minorities are deemed to be more vulnerable to automation than their counterparts. In 2 this regard, the findings of this study will be valuable information in understanding whether or 3 not metro Atlanta residents who are deemed to be more vulnerable to automation perceived that 4 they are indeed subject to automation. Agencies working in workforce development such as the 5 Atlanta Regional Workforce Development Board may find these findings useful to tailor the 6 agency's existing workforce development policy to mitigate any automation-induced disruption 7 in the labor market in the future. For instance, the agency may use these findings to target 8 outreach efforts to certain sociodemographic groups who do not perceive automation as a threat 9 to their jobs despite being identified as more susceptible to automation, as well as to educate 10 them about the negative consequences of automation, such as the possibility of being 11 unemployed. The agency may offer training opportunities or provide information about training 12 opportunities to these sociodemographic groups and so mitigate the automation-induced 13 disruption in the labor market. Also, addressing these issues would help improve racial equity as 14 non-whites (e.g., blacks or African Americans, Hispanics or Latinos, or other racial groups) are, 15 in general, more susceptible to automation than the white counterpart. 16 The rest of this paper proceeds as follows. Section 2 revisits the theoretical links between 17 automation and unemployment. Section 3 describes the methods used to identify the 18 sociodemographic characteristics of employees who perceived automation as a threat to their 19 jobs in the future. Section 4 summarizes the results, and the last two sections discuss the findings 20 and provide study conclusions. The literature on job automation and unemployment is relatively new, and there is no consensus 2 among economists and social scientists on how the future looks like in the age of intelligent 3 technologies or the digital economy. Some argue that the new technology will disrupt the labor 4 market by displacing thousands of workers, in particular, those who are less-educated or less-5 skilled. The transition of these displaced workers to other sectors is limited by the skill gap and 6 mismatch between the place they live and job availability. Hence, technology is likely to make 7 these workers unemployed [1, 3, 7, 14] . In contrast, some see a brighter future with smart 8 technologies as it can bring multiple opportunities for growth and employment, particularly in 9 the long run [15, 16, 17] . Shook and Knickrehm (2018) estimates that artificial intelligence alone 10 could boost revenues by 38 percent and jobs by 10 percent by 2022, provided that there are 11 sufficient investments in intelligent technologies and human-machine collaborations [18] . 12 The concern that technology can make workers unemployed is, in fact, not a novel 13 concern in economics as J.M. Keynes estimated that millions of jobs currently performed by less-educated or less-skilled workers are 20 at risk of automation in the coming decades [1, 3, 7, 20] . Moreover, because of the skill or 21 geography mismatch, these less-educated or less-skilled workers are less likely to be absorbed in 22 other sectors. Hence, technology is expected to disrupt the labor market by eliminating thousands 23 J o u r n a l P r e -p r o o f of blue-and white-collar jobs currently performed by less-educated or less-skilled workers, 1 ranging from truck drivers and warehouse workers to telephone operators to insurance agents to 2 health-care managers.

The literature, in general, uses a macroeconomics approach to analyze the potential 4 impact of automation on jobs. In any economy, final goods (P) are produced using capital (K) 5 and labor (L) as inputs, as specified in equation (1).

In the traditional economic analysis, technology is assumed to take a "factor-augmenting" between capital and labor is small [12] . 15 Besides augmenting factor parameters, technological changes can also substitute labor 16 directly, resulting in job losses. For instance, if capital is sufficiently cheap or productive at the 17 margin, automation will substitute capital for labor, displacing workers from the tasks that are 18 being automated. This displacement can cause a decline in labor demand and hence wages. . Hence, less-9 educated or less-skilled workers are expected to be displaced in the future for several reasons. As 10 the economy becomes more digital, the prospect of maintaining employment becomes less 11 attractive to employers because programming software can do many tasks currently performed 12 by human workers more efficiently (e.g., cheaply and more accurately). Further, computer 13 technology is becoming cheaper than human labor, and it has improved its capabilities to 14 perform various tasks more efficiently with little or no human interactions [28] . As workers are 15 often not hired on a permanent basis but on-demand, automation is argued to change in 16 employment relationships, and this situation can make both high-and low-skilled workers 17 unemployed [29, 30] . Hence, jobs such as paralegal professions, retail salespersons, cashiers, 18 office clerks, accountants, insurance agents, and waiters and waitresses are expected to be 19 automated in the coming decades [1, 7] . Again, the transition of these less-educated workers to 20 other sectors (e.g., sectors where job opportunities are created because of automation) is limited 2 Automation-prone industries or jobs (e.g., retail salespersons, cashiers, office clerks, 3 accountants, insurance agents, and waiters and waitresses) are characterized by historically a 4 large share of young workers in their workforce, making these young workers more vulnerable to 5 automation in the future. According to a Brookings Institution study, half of the tasks currently 6 performed by workers who are 16 to 24 years old can be automated over the next decades, 7 compared to 40 percent of the tasks of older workers [1] . Although there is no consensus among 8 economists regarding the potential impact of automation by gender in the future (see [31] ), 9 historical trends indicate that male workers can be more vulnerable to automation in the future 10 because of the nature of jobs traditionally represented by male workers (e.g., men are over-11 represented in manufacturing, production, transportation, and construction jobs in the U.S. [1] . 1 

It is estimated that 43 percent of jobs currently performed by male workers in the U.S. could be 13 automated by 2040, compared to 40 percent of women's jobs [1] . 2 Regarding race/ethnicity, The MAS survey is an ongoing regional opinion survey conducted by the Atlanta Regional https://atlantaregional.org/atlanta-region/regional-data-resources/metro-atlanta-speaks-survey-10 report/.

Like the previous year's survey, the core part of the 2019 MAS survey was a series of 12 questions related to residents' opinions or perceptions on various critical issues facing by the 13 metro Atlanta region or its residents. One question in the survey asked employed residents 14 whether or not they perceive automation as a threat to their jobs. In particular, employed 15 residents were asked whether or not they agree with the statement: "I am worried that I may lose 16 my job to some type of automated process" with the five possible response options to be chosen -17 strongly agree, agree, disagree, strongly disagree, or do not know. An automated process was 18 defined as the use of technology to perform jobs partly or fully with little or no human 8 We model the response of the resident 'i' who lives in zip code 'z' on whether or not he/she 9 perceived automation as a threat to his/her job (automation threat) (A i,z ) to depend on his/her 10 sociodemographic characteristics (S i,z ) while controlling for the zip code fixed effects (α z ) in the 11 model, as specified in equation (1):

As we discussed earlier, several jobs or occupations currently typically performed by 14 males, non-whites, less-educated, or low-income residents are at higher risk of being automated 15 in the coming decades [1, 7] . Accordingly, we account for age, gender, race/ethnicity, education, 16 and income of the residents. We also account for tenure length in metro Atlanta in the model to 17 see how residents with different tenure lengths perceive automation as a threat to their jobs. 18 Homeownership status is also accounted for in the model to assess whether respondents with 19 homeownership perceive automation as a threat to their jobs differently than renters. The American-dominated [38] . Accordingly, we account for whether the respondent lives north or 3 south of I-20 in the model.

The zip code fixed effects α z are included in the model to account for zip code-specific perceive automation as a threat to their jobs because of their sociodemographic characteristics.

Accounting for the zip code fixed effects also controls for heterogeneities in physical 11 development (e.g. road access and mobility, proximity to transit and recreation areas, and access 12 to jobs) across communities as represented by zip codes. The use of zip code fixed effects 13 mitigates the omitted variable bias, and hence, endogeneity concern in the regression model as it African American, 13 percent were other racial groups, and 5 percent were Hispanic or Latino.

Thirteen percent of the respondents had a high school or less education, 31 We explore the interaction effects to see how residents with a combination of various 10 sociodemographic characteristics perceived automation as a threat to their jobs. Table 3 reports 11 the interactions between the variables age and race in column 1, age and education in column 2, were less likely to perceive automation as a threat to their jobs. Likewise, as the results indicate 16 in column 3, residents who are 65 years or older and whose annual household income is $25,000 17 or less or between $120,000 and $250,000 were less likely to perceive automation as a threat to 18 their jobs. Blacks or African Americans whose annual household income is $25,000 or less were 19 less likely to perceive automation as a threat to their jobs. Nevertheless, blacks or African 20 Americans whose annual household income is between $120,000 and $250,000 were more likely 21 to perceive automation as a threat to their job (column 5). Likewise, residents with a graduate or 22 a professional degree whose annual household income is $25,000 or less were more likely to African Americans compared to 33 percent white workers [1, 47] . Note that food preparation and 

In addition to providing these general services, the Regional Workforce Development 20 Board also may need to design policies and programs specifically for these sociodemographic 21 groups who do not perceive automation as a threat to their jobs despite being deemed more induced labor market disruption. The region needs to focus on the growth of the "opportunity 5 jobs," which are automation resilience, pay living wages, and are projected to grow in the future, 6 rather than mere expanding automation prone, low-wage jobs. Also, the region needs to focus on 7 narrowing down the skill gaps through reforming the education system, in general. 

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Atlanta. • Relatively older residents perceived automation as a threat to their jobs.• African Americans and low-income residents perceived automation as a threat.• Hispanics and less-educated residents did not perceive automation as a threat.• Automation may further increase unemployment rates among Hispanics and lesseducated residents.

Ramesh Ghimire reviewed the literature, developed the theoretical model, analyzed the data, and discussed the findings.Jim Skinner developed the surve instruments and helped developed the introduction section.Mike Carnathan developed the survey instruments and helped developed the introduction section.