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Reducing automation risk through career mobility: Where and for whom?

László Czaller, Rikard Eriksson, Balázs Lengyel

发表年份
2021
引用次数
13
访问权限
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摘要

Automation risk prevails less in large cities compared to small cities but little is known about the drivers of this emerging urban phenomenon. A major challenge is that automation risk is quantified by work-related tasks that allows for measurement through occupation, which is in turn implicitly related to local economic structure and to individual career paths. This paper examines the role of working in cities on changes in automation risk through individual career mobility. Using panel data on Swedish workers, we show that the metropolitan effect of reducing automation risk is mainly induced through inter-firm job mobility. Separate estimates for different groups show that this effect accrues mostly to native, high-skilled and male workers. El riesgo de automatización prevalece menos en las grandes ciudades que en las pequeñas, pero se sabe poco sobre los factores que impulsan este fenómeno urbano emergente. Una de las principales dificultades es que el riesgo de automatización se cuantifica mediante tareas relacionadas con el trabajo que permiten la medición a través de la profesión, que a su vez está relacionada implícitamente con la estructura económica local y con las trayectorias profesionales individuales. Este artículo explora el papel del trabajo en las ciudades en los cambios en el riesgo de automatización a través de la movilidad de trayectorias profesionales individuales. Se utilizaron datos de panel sobre trabajadores suecos, con los que se mostró que el efecto metropolitano de la reducción del riesgo de automatización está inducido principalmente por la movilidad laboral entre empresas. Las estimaciones separadas para los distintos grupos muestran que este efecto corresponde sobre todo a los trabajadores nativos, altamente cualificados y de sexo masculino. オートメーション(自動化)のリスクは小都市に比べて大都市では少ないが、この新たな都市現象の要因についてはほとんど解明されていない。オートメーションのリスクは職業による測定を可能にする仕事に関連するタスクによって定量化されるのであるが、逆にそれは、地域の経済構造や個人のキャリアパスに絶対的に関連するということが大きな課題である。本稿では、個人の雇用流動性によるオートメーションのリスクの変化における都市部で働くことの役割を検討する。スウェーデンの労働者に関するパネルデータを用いて、オートメーションのリスクを低減する大都市効果は、主に企業間の雇用流動性によって惹起されることを示す。異なる集団の各々の推定から、この効果の恩恵の大部分は、地元出身でスキルが高い男性の労働者が受けていることが示される。 Automation influences labour markets by replacing human workforce in certain tasks (Autor et al., 2003; Brynjolfsson & McAfee, 2011). Whether this threatens existing jobs or facilitates creation of new jobs depends on the type of investments into automation, worker skills and their potential renewal (Acemoglu & Restrepo, 2020a). Because investments into technologies as well as the skill level of human workforce typically differ across regions, the problem has a necessary spatial dimension. Accordingly, workers in small cities have been found to face higher automation risks than workers in large cities (Crowley et al., 2021; Frank et al., 2018). Further evidence suggests that investments into labour replacing technologies in metropolitan regions lead to employment growth in the long run because robots supplement automatable tasks that trigger upgrades in local skills (Leigh et al., 2020). Nevertheless, how individuals adopt their skills to avoid the threat of automation and how geography facilitates this process is one of the important questions in this quickly evolving, but still largely uncovered, field (Frank et al., 2019). In this paper, we expand on recent aggregate analyses (e.g., Crowley et al., 2021) by detailing the micro-mechanisms explaining the role of agglomeration in relation to regional automation vulnerability. Specifically, we look at how working in cities facilitates career upgrades and prevents automation risks. We argue that large labour markets create more favourable conditions for job mobility, through which individuals are able to reduce their exposure to automation. The role of cities in reducing automation risk therefore lies in facilitating upward career mobility. According to central tenets in urban economics, two mechanisms take place in cities in this regard. First, the demand for non-aut

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AutomationComputer scienceEngineeringMechanical engineering

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