The Skill Content of Occupations Across Low and Middle Income Countries: Evidence from Harmonized Data

Dicarlo, Emanuele; Bello, Salvatore Lo; Monroy-Taborda, Sebastian; Oviedo, Ana María; Puerta, Maria Laura Sanchez; Santos, Indhira

doi:10.2139/ssrn.2846315

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…In the case of Brazil, this implies that, if we estimate that a large proportion of this country´s jobs may be substituted by machines, it is because a substantial portion of its workforce is employed in occupations with a high risk of automation. Furthermore, while the direct association between occupations in different countries is open to criticism, we emphasize that, as argued by Dicarlo et al 2016, the nature of occupations in most industrialized nations is quite similar. 7 Finally, it is important to mention that our study adds to a small and growing strand of Brazilian literature that documents other impacts of technology on labor markets (e.g., Adamczyk et al, 2019;Gonzaga and Guanziroli, 2019;Maciente, 2014;Santos et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 67%

Automation and job loss: the Brazilian case

Vaz

Oliveira²,

Estrela

et al. 2022

Nova econ.

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Technological innovations are enabling machines to further replace human labor. In this context, we estimate - based on the Frey and Osborne (2017) study, which uses data from the United States of America (USA) - how many Brazilian jobs may be eliminated in one or two decades due to currently existing technologies. We add to earlier research, that included the Brazilian case, as we consider the entire employment structure - including both formal and informal sectors - in order to estimate the proportion of jobs in the country that may be substituted by machines. Our results indicate that 58.1% of Brazilian jobs may disappear over the next 10 to 20 years due to automation. Moreover, we observe that jobs in the informal sector face higher probabilities of elimination by automation when compared to the formal sector.

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Section: Literature Reviewmentioning

confidence: 67%

Automation and job loss: the Brazilian case

Vaz

Oliveira²,

Estrela

et al. 2022

Nova econ.

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“…2 Other studies of tasks that use international survey data define measures of "de-routinisation" (de la Rica and Gortazar, 2016), group occupations into non-, low-, medium-and high-routine intensive (Marcolin et al, 2016), or combine manual and cognitive tasks (Dicarlo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…CULS data instead the STEP survey for the Chinese Yunnan province, as the former contains far more observations (almost 15 500) and covers a more comprehensive area. Yunnan is one of the poorer and more rural provinces in China so it might not reflect the dominant patterns of work in Chinese urban areas Dicarlo et al (2016). also omitted the Yunnan dataset.…”

mentioning

confidence: 99%

Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data

Lewandowski

Park

Hardy

et al. 2022

The World Bank Economic Review

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The shift from routine work to nonroutine cognitive work is a key feature of labor markets globally, but there is little evidence on the extent to which tasks differ among workers performing the same jobs in different countries. This paper constructs survey-based measures of routine task intensity (RTI) of jobs consistent with those based on the U.S. O*NET database for workers in 47 countries. It confirms substantial cross-country differences in the content of work within occupations. The extent to which workers’ RTI is predicted by technology, supply of skills, globalization, and economic structure is assessed; and their contribution to the variation in RTI across countries is quantified. Technology is by far the most important factor. Supply of skills is next in importance, especially for workers in high-skilled occupations, while globalization is more important than skills for workers in low-skilled occupations. Occupational structure explains only about one-fifth of cross-country variation in RTI.

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“…This procedure has already been applied in several papers (Rutzer and Niggli, 2020;Lobsiger and Rutzer, 2021;Elliott et al, 2021;Valero et al, 2021) and is common in the automation literature (Gasparini et al, 2020;Brambilla et al, 2021) and more recently in the teleworking literature (Albrieu, 2020;Foschiatti and Gasparini, 2020;de la Vega, 2021). As stated in the previous section, these studies have been criticized because the task content varies depending on the level of development and, therefore, it would not be correct to extrapolate estimates based on the United States to other countries, particularly emerging ones (Dicarlo et al, 2016;Bello et al, 2019). However, we lack alternatives based on data availability.…”

Section: Data Sources and Variable Definitionmentioning

confidence: 99%

“…Their results reveal that the share of green jobs ranges from 17% (Greece) to 22% (Germany), and they suggest that these kinds of jobs tend to be held by older, male workers, with higher educational level, and with permanent contracts. The main criticism that these studies have received is that the task content varies depending on the level of development and, therefore, it would not be correct to extrapolate estimates based on the United States to other countries, particularly emerging ones (Dicarlo et al, 2016;Bello et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Going Green: Estimating the Potential of Green Jobs in Argentina

Porto¹,

Vega²,

Cerimelo³

2022

Preprint

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This paper aims to identify and characterize the potential of green jobs in Argentina, i.e., those that would benefit from a transition to a green economy, using occupational green potential scores calculated in US O*NET data. We apply the greenness scores to Argentine household survey data and estimate that 25% of workers are in green jobs, i.e., have a high green potential. However, when taking into account the informality dimension, we find that 15% of workers and 12% of wage earners are in formal green jobs. We then analyze the relationship between the greenness scores (with emphasis on the nexus with decent work) and various labor and demographic variables at the individual level. We find that for the full sample of workers the green potential is relatively greater for men, the elderly, those with very high qualifications, those in formal positions, and those in specific sectors such as construction, transportation, mining, and industry. These are the groups that are likely to be the most benefited by the greening of the Argentine economy. When we restrict the sample to wage earners, the green potential score is positively associated with informality.

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The Skill Content of Occupations Across Low and Middle Income Countries: Evidence from Harmonized Data

Cited by 12 publications

References 7 publications

Automation and job loss: the Brazilian case

Automation and job loss: the Brazilian case

Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data

Going Green: Estimating the Potential of Green Jobs in Argentina

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