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Covid-19'S Impact on the Labor Market Shaped

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Covid-19'S Impact on the Labor Market Shaped Covid-19’s Impact on the Labor Market shaped by Automation: Evidence from Chile1 Pablo Egaña del Sol Gabriel Cruz Escuela de Negocios Escuela de Negocios Universidad Adolfo Ibáñez Universidad Adolfo Ibáñez July 1st, 2020 1 We thank the financial support provided by the Columbia University´s Global Center in Santiago, Chile. 1 I Introduction In this article, we argue that the ongoing COVID-19 pandemic would serve as a catalyzer for automation processes in several firms across industries and territories. It is a desolate fact that many workers had lost their job in the recent months due the restrictions (quarantine and confinements, curfews, general physical movement restrictions, etc.) put in place to control for the COVID-19 pandemic (Superintendencia de Pensiones, 2020; INE, 2020; Coibion et al., 2020). Many of those workers are expecting to return to their previous occupation when the current situation turns out to be safer and the government lift restrictions. Regardless is a very complicated, rather speculative, task to predict the situation in the labor market when the pandemic fades out, we will expose a couple of stylized fact that are consistent with the following hypothesis: industries are accelerating the digital transformation of their operations and we will experience a jobless recovery in many sectors, especially those were the automation technologies are available, the degree of at-work physical proximity is high, the level of exposure of covid-19 is high, and the capabilities to work remotely are low.2 In other words, companies are forced to operate relying heavily on technology during the pandemic, and this would become the new normal in several economic sectors. We will provide empirical evidence supporting our hypothesis using data at individual and industry/sector level in Chile. Recent developments on digitalization, cognitive computing, artificial intelligence, and robotics, which are key parts of the so-called automation in the Industry 4.0 context, have been offering countless benefits through increased productivity and efficiency.3 Irrespective of these positive effects, automation may have undesirable consequences on the labor market, at least in the medium term. There is empirical evidence indicating that various occupations have become redundant, and in some cases, have become unnecessary (Winick, 2018). Furthermore, Automation may increase demand of some occupations (the complementarity effect or new tasks) and decrease others (the substitution effect).4 Indeed, Webb (2020) finds that the exposure to robots is associated with a decline in wages of between 8 and 14% and a decline in the employment share of between 9 and 18% in the United States between 1980 and 2010. For example, Arntz et al. (2016) found that the fraction of jobs at 2 This jobless recovery, or “cleansing effect”, has been studied regarding previous economic crisis, such as the Great Recession in 2009 (Micco, 2020). 3 E.g. Acemoglu and Restrepo (2018), Frey and Osborne (2017), Arntz et al. (2017), McKinsey (2017), Fort (2017), Egana-delSol (2019), Egaña-delSol and Joyce (2020), among others, argue that due to recent developments, a significant proportion of current jobs are susceptible to automation. 4 See Graetz and Michaels (2018), Brynjolfsson and McAfee (2014), and Acemoglu and Restrepo (2018; 2019). 2 high risk of automation (that is, with a predicted probability greater than 70% at the individual level), among member countries of the Organization for Economic Co- operation and Development (OECD), was 9%, with a range of 6 to 12%, while Egaña- delSol and Joyce (2020) estimate that the percentage of jobs under high risk of automation are in the 11-42% range for developing economies. Bustelo et al. (2020) finds that women have a higher risk of been replaced by automation than their male peers in Latin America. In Chile, there is a recent report showing that the share of workers at high risk of automation is around 17% (Bravo et al., 2019). On the other hand, COVID-19 has had an immediate effect on the labor market, destroying thousands of jobs (Atkeson, 2020; Beland et al, 2020; Sanchez et al., 2020; Superintendencia de Pensiones, 2020). In particular, Beland et al. (2020) find that COVID-19 increased the unemployment rate especially in men, younger workers, Hispanics, and less-educated workers in the United States. They also built three indexes to identify what type of occupations are (1) more exposed to disease, workers that work with (2) proximity to coworkers, and workers who can easily (3) work remotely. Their data suggest that employees in occupations working in proximity to others are more affected while occupations able to work remotely are less affected. We claim that the COVID-19 pandemic has been accelerating many of these technological changes, such as the use of chatbots, virtual agents, automatic financial reports, middle-men in the delivery or the supply chain, among many others. Unfortunately, there is no reliable and validated source of information regarding the degree of recent adoption of technology. Therefore, we build our analysis on well- known metrics of the potential of automation of certain occupations (Frey and Osborne, 2017) as well as indices capturing the capacity of computers, software and robots to perform task within occupations, based on recent patent records in the US (Webb, 2020). That is to say, we will estimate the potential of each sector of the Chilean economy to replace workers by machines or computers unrelatedly to the COVID-19 pandemic, but based on the current information that we have of the available technology to perform it. In short, the current sanitary crisis may result in an acceleration of the effects of automation on the labor market. We will show the heterogenous impacts that COVID-19 fueled by automation may have industries and territories would have in Chile. In this milieu, this article has two objectives: (1) Identify which sectors, territories, and workers would be most affected by automation in Chile; and (2) identify what sectors would be most affected by COVID-19 and shed lights on how this is related with automation in Chile. 3 In this paper we use CASEN dataset, Frey and Osborne (2017) probabilities of automation at occupation level, and Webb (2020) new index of automation capabilities based on patent records in the US. We use an expectation-maximization algorithm to predict the index of automation of each occupation in Chile. With this analysis, we identify what productivity sectors and geographical areas are more likely to be automated. We find evidence that COVID-19 might act as catalyzer on the digital transformation of firms in Chile. In particular, we find that the automation risks have a significant geographical heterogeneity, even within subnational areas. This heterogeneity is partly determined by their economic activities ---e.g. agriculture, mining, and services--- as well as demographic characteristics (i.e. worker’s average age, income, and gender). In addition, we argue that Chile might experience a jobless recovery in many economic sectors, especially in those were the automation technologies are available, the degree of both at-work physical proximity and the level of exposure to COVID-19 are high, and the capabilities to work remotely are low. The rest of the article is organized as follows. Section II presents the data and methodology. Section III presents the main results. Then, section IV offers some conclusions. II Data and Methodology We use three sources to measure the automation prediction in Chile. First, Encuesta de Caracterización Socioeconómica Nacional (CASEN) provides information about workers' occupations, age, gender, region, and other relevant characteristics that help to answer our first objective. Particularly, each person that works is associated to some International Standard Classification of Occupations (ISCO) occupation code. Then, we use Webb (2020) to predict the automation score by occupations controlled by workers' characteristics. Moreover, we also estimated the same predictions using Frey and Osborne (2017) as a robustness check of our results. Webb (2020) uses patent data to show what occupations are most exposed to robotics and new technologies such as software and artificial intelligence. In particular, he quantifies the overlap between the text of patents and the text of job description using the Google Patents Public Data, and the O*Net database of occupations and tasks5. Then, he uses the text of patents to identify what the technology can do. 6 5 Table A1 shows an example. 6 Figure A1 illustrates the method how Webb (2020) builds technology exposure measures. 4 Frey and Osborne (2017) use machine learning experts that manually analyzed 70 occupations and labelled them as either automatable or not. Then, they label the remaining 632 occupation contained on the O*Net data base using an algorithm. They find that 47% of total US employment is highly susceptible to automation, this means that 47% of occupations have a probability to be automated higher to 70%. Webb (2020) does not choose a threshold to classify occupations in high risk, but we classify occupations in high risk when their result is higher than the mean plus one standard deviation.7 Table 1: Example Crosswalk ISCO to SOC code Frey & Webb ISCO 4- ISCO-08 Title 2010 SOC Code 2010 SOC Title Osborne (2017) (2020) Digit Index Index Elementary School Primary school 2341 25-2021 Teachers, Except Special 0.0044 0.2182 teachers Education Middle School Teachers, Primary school Except Special and 2341 25-2022 0.17 0.1937 teachers Career/Technical Education Early Preschool Teachers, 2342 childhood 25-2011 Except Special 0.0074 0.3141 educators Education Early Kindergarten Teachers, 2342 childhood 25-2012 Except Special 0.15 0.2867 educators Education On the other hand, Beland et al. (2020) provide information about three indexes that allow estimating the impact of COVID-19 on occupations. They use ACS (American Community Survey) and O*NET data to classify occupations in three: exposure to disease, physical proximity to other people and how easily occupations can work from home.
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