Automation in the future of public sector employment: the case of Brazilian Federal Government

Adamczyk, Willian Boschetti; Monastério, Leonardo Monteiro; Fochezatto, Adelar

doi:10.1016/j.techsoc.2021.101722

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…All these things considered, the study reinforces, in line with extant literature (Adamczyk et al, 2021;Arntz et al, 2016) how low educated workers across the PA likely will bear the These findings point toward the need to focus more on the potential inequalities and requirement for the re-training arising from technological change and on how public management would be able to rely on DX to ensure inclusive income growth, i.e. reducing the gap between high and low salaries across the public sector through higher labor productivity boosted by DX.…”

Section: Discussion and Policy Implicationssupporting

confidence: 86%

“…All these things considered, the study reinforces, in line with extant literature (Adamczyk et al , 2021; Arntz et al , 2016) how low educated workers across the PA likely will bear the impact of adjustment costs to technological change, in terms of requirements for further training and occupational retraining. For this group of workers, regaining the competitive advantage over new digital technologies by means of upskilling and training may be difficult to achieve, especially as the speed of the current 4.0 technological revolution appears to exceed the pace of its predecessors.…”

Section: Discussion and Policy Implicationssupporting

confidence: 81%

“…Few studies have sought to apply these estimates to a specific national context. Using a measure of automation susceptibility, Adamczyk et al (2021) show that approximately 20% of Brazilian Public Sector employees work in jobs with a high potential of automation in the coming decades.…”

Section: Tg 181mentioning

confidence: 99%

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Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment

Bonomi Savignon,

Zecchinelli,

Costumato

et al. 2023

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Purpose This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services. Design/methodology/approach The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs). Findings This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years. Originality/value To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.

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Section: Discussion and Policy Implicationssupporting

confidence: 86%

“…All these things considered, the study reinforces, in line with extant literature (Adamczyk et al , 2021; Arntz et al , 2016) how low educated workers across the PA likely will bear the impact of adjustment costs to technological change, in terms of requirements for further training and occupational retraining. For this group of workers, regaining the competitive advantage over new digital technologies by means of upskilling and training may be difficult to achieve, especially as the speed of the current 4.0 technological revolution appears to exceed the pace of its predecessors.…”

Section: Discussion and Policy Implicationssupporting

confidence: 81%

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Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment

Bonomi Savignon,

Zecchinelli,

Costumato

et al. 2023

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“…The relentless expansion of AI brings about concerns about the future of work (Adamczyk et al, 2021;Park & Kim, 2022;Petersen et al, 2022). According to some reports, an estimated 50% of the current occupations may be displaced due to automation (Frey & Osborne, 2017;Petersen et al, 2022).…”

Section: Background: Trust In Aimentioning

confidence: 99%

Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities

Lukyanenko

Maaß

2022

Electron Markets

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With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount societal concern. Despite increased attention of researchers, the topic remains fragmented without a common conceptual and theoretical foundation. To facilitate systematic research on this topic, we develop a Foundational Trust Framework to provide a conceptual, theoretical, and methodological foundation for trust research in general. The framework positions trust in general and trust in AI specifically as a problem of interaction among systems and applies systems thinking and general systems theory to trust and trust in AI. The Foundational Trust Framework is then used to gain a deeper understanding of the nature of trust in AI. From doing so, a research agenda emerges that proposes significant questions to facilitate further advances in empirical, theoretical, and design research on trust in AI.

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“…Neves Jr., Azzoni and Chagas (2017) estimate the skill premium by city size. Albuquerque et al (2019) and Maciente, Rauen and Kubota (2019) predict the impact of automation on the Brazilian labour market, and Adamczyk, Monasterio, and Fochezatto (2021) on the public sector. Ehrl (2018) analyses the heterogeneous effects of intermediate goods importing on the skill structure within firms.…”

Section: Skills and Occupationsmentioning

confidence: 99%

Skills and employment transitions in Brazil

Adamczyk¹,

Ehrl²,

Monastério³

2022

Self Cite

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This paper analyses employment transitions and workers’ skills in Brazil using a random sample from the universe of formal labour contracts covering the period from 2003 to 2018. We develop a novel procedure to derive a measure of occupational distance and internationally comparable skill measures from occupations’ task descriptions in the country under analysis based on machine learning and natural language processing methods, but without usual ad hoc classifications. Our findings confirm that workers who use non-routine cognitive skills intensively experience the highest employment growth rates and wages. Their labour market exit risk is relatively low, occupational and sectoral changes are least common and, in the case of occupational switching, non-routine cognitive workers tend to find occupations that are higher-paid and closer in terms of their task content. Against the same characteristics, routine and non-routine manual workers are worse off in the labour market. Overall, there have been signs of routine-biased technological change and employment polarization since the 2014 Brazilian economic crisis.

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Automation in the future of public sector employment: the case of Brazilian Federal Government

Cited by 13 publications

References 29 publications

Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment

Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment

Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities

Skills and employment transitions in Brazil

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