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Enrique Fernández Macías

@quiquefm.bsky.social
272 followers 488 following 90 posts

enriquefernandezmacias.github.io

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Reposted by Enrique Fernández Macías
Sarah O'Connor @sarahoconnorft.ft.com · 15/05/2026
Are you tired of the non-stop utopian/dystopian AI predictions from CEOs? Do you want to hear what's ACTUALLY happening, from the people it's actually happening TO? If so, I have good news: I felt the same, so I have worn out my shoe-leather & written this book! www.penguin.co.uk/books/462159...
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Reposted by Enrique Fernández Macías
Joe Bak-Coleman @jbakcoleman.bsky.social · 20/09/2026
We can simulate fish schools or ants in ways that solve complex problems (gradient detection, bridge building, nest selection) but no one would create this weird soul/no-soul dichotomy to argue they these that multi-agent swarms of extremely simple rules are “thinking”.
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Reposted by Enrique Fernández Macías
elDiario.es @eldiario.es · 18/09/2026
La IA no “se rebela”: qué hay realmente detrás de los últimos incidentes de OpenAI
eldiario.es
La IA no "se rebela": qué hay realmente detrás de los últimos incidentes de OpenAI
Analistas independientes que han revisado los incidentes señalados por OpenAI concluyen que sus modelos no se rebelaron contra sus creadores, sino que obedecieron instrucciones mal definidas hasta sus...
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 16/09/2026
New post in Social Europe, about the Hugging Face incident and what I think it means for the future of work
socialeurope.eu
The Hugging Face Incident and the Future of Work
The agents hacked a global platform to hide their cheating from a scorer that did not exist.
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Enrique Fernández Macías @quiquefm.bsky.social · 16/09/2026
New post in Social Europe, about the Hugging Face incident and what I think it means for the future of work
socialeurope.eu
The Hugging Face Incident and the Future of Work
The agents hacked a global platform to hide their cheating from a scorer that did not exist.
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Reposted by Enrique Fernández Macías
Adam Przeworski @adamprz.bsky.social · 15/08/2026
A summary of what I understand and do not understand about the way democracy works: papers.ssrn.com/sol3/papers.cfm?abstract_id=7269378
papers.ssrn.com
<p>Swan Song: My Understanding of Democracy</p>
I summarize my understanding of democracy: the theory which predicts when elections peacefully manage conflicts and the empirical evidence regarding the co
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Reposted by Enrique Fernández Macías
Chris Dillow @chrisdillow.bsky.social · 12/08/2026
New substack: without big changes, slow growth is normal: chrisdillow.substack.com/p/stagnation...
chrisdillow.substack.com
Stagnation: the norm
We need more than tinkering if we are to raise economic growth significantly.
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
Our paper "A rising tide: revisiting the occupational impact of AI in the generative era" is now out in Structural Change and Economic Dynamics. With P. Casas, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti #econsky #sociology 1/6
sciencedirect.com
A rising tide: revisiting the occupational impact of AI in the generative era
Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and task…
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
For more papers on this topic, check out our Labour, Education and Technology working paper series 6/6
joint-research-centre.ec.europa.eu
JRC Working Papers on Labour, Education and Technology (LET)
The JRC Working Papers on Labour, Education and Technology (LET) address economic, social and policy questions related to the changing nature of work, skills and education as a result of technological...
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
Our paper measures AI exposure, not automation risk. A high score doesn't mean job loss, only an increasing overlap between the abilities of humans and AI. Whether that becomes substitution or complementarity depends on work organisation, bargaining power and policy, not on the technology alone 5/6
This figure plots task-level AI exposure in 2024 (vector l2024) against the number of cognitive abilities required by each task (vector m), with bubbles weighted by total intensity across the 127 occupations and colour-coded by task nature. The figure confirms that tasks rich in cognitive content are more exposed, while those relying on social or physical skills are less exposed. Intellectual tasks dominate the top-right of the plot, indicating that AI exposure rises with task complexity, with activities such as writing reports, learning from others, business management and scientific reading standing out — consistent with the strong alignment between AI capabilities and the demands of analytical, reasoning and information-processing tasks. Social tasks occupy the middle range even when they involve multiple cognitive abilities, reflecting the continued difficulty of emulating social reasoning and emotional intelligence. Physical tasks cluster at the lower end of both dimensions, indicating that embodied and manual activities remain largely insulated from AI-driven substitution.
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
Why so pervasive? Because recent AI progress clusters in "ideas" abilities (reading, writing, searching, reasoning) that show up in almost every job, not just knowledge work. The "creative" and cognitive skills once seen as safe from automation are exactly where GenAI has advanced most 4/6
This figure shows exposure scores across broader categories of related tasks, showing AI exposure across the 19 task categories and 127 occupations over time. The figure is based on the matrices 
, which combine the exposure of each task index with the intensity of those tasks across occupations. This structure allows us to capture both cross-task and between-occupation variation in AI exposure for the years 2012, 2018, and 2024. Each horizontal line represents the dispersion of exposure values across occupations for a given task index, while the dots indicate individual occupational scores. The figure confirms that exposure intensifies over time in knowledge- and information-based activities, whereas tasks dependent on physical and social capabilities remain lower. The highest exposure is concentrated in information processing, analysis, and reasoning tasks, while activities requiring physical interaction or social intelligence remain comparatively less exposed to AI.
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
The result that surprised us most is that AI exposure is now everywhere. It's still highest for managers and professionals, but it has climbed steeply across the whole occupational structure. Elementary occupations in 2024 score higher than professionals did in 2018 3/6
Panel (a) of Fig. 7 tracks a set of illustrative three-digit occupations that differ markedly in task content. As expected, knowledge- and information-intensive occupations such as medical doctors display the highest and fastest-growing exposure, reflecting their reliance on reasoning, information processing, and decision-making tasks, where GenAI-enabled systems have advanced most rapidly. However, the trajectories for office clerks and shop salespersons also rise steadily over time, signalling that AI is increasingly relevant for occupations centred on routine information handling and customer interaction. And even cleaners and helpers-archetypal low-skilled, manual jobs - exhibit a clear upward trend in exposure. The key message of panel (a) is therefore not only that high-skilled occupations are highly exposed, but that exposure is cascading down the occupational ladder. As we will discuss later in more detail, this is because some of the cognitive abilities most affected by recent AI developments are very transversal and required in one way or another across all occupational groups.
Panel (b) aggregates these patterns to the one-digit ISCO level (occupational family level), providing a broader view of exposure patterns across the entire labour market. The familiar hierarchy remains: managers, professionals and technicians sit at the top of the exposure distribution and experience the sharpest post-2015 acceleration. Yet occupational families dominated by routine manual or physical tasks, such as machine operators and elementary occupations, also show a pronounced and persistent increase in exposure, even if they start from lower levels. By 2024, the average exposure of low-skilled occupations is already substantially higher than that of high-skilled occupational groups in 2016. This temporal comparison underlines how quickly GenAI-era capabilities have diffused into jobs that earlier studies would have classified as only weakly exposed to AI.
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
Instead of asking experts which jobs AI will hit, we measure where AI research is concentrated. We track 352 benchmarks from 2008 to 2024 and map them onto cognitive abilities, then onto 108 tasks and 127 occupations across Europe 2/6
This figure tracks the number of AI papers associated with selected benchmarks over time, classified according to the cognitive abilities each benchmark engages with. Benchmarks are grouped by whether they predominantly relate to ideas (e.g. CiteSeer, UCI, Wikipedia), things (e.g. LibriSpeech, TIMIT), ideas and things combined (e.g. ImageNet, CIFAR), or people and ideas (e.g. IMDb, SST, Yelp). Publication counts rise markedly across all groups, especially after 2018, and grow disproportionately faster than the general scientific output (Baruffaldi et al., 2020; Bommasani, 2021; Bornmann and Mutz, 2015) — indicating an acceleration of AI research in domains that bridge multiple cognitive types. Growth is most pronounced for benchmarks combining ideas and things, where advances in deep learning for computer vision and language models have driven a surge in academic output.
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Enrique Fernández Macías @quiquefm.bsky.social · 06/08/2026
Our paper "A rising tide: revisiting the occupational impact of AI in the generative era" is now out in Structural Change and Economic Dynamics. With P. Casas, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti #econsky #sociology 1/6
sciencedirect.com
A rising tide: revisiting the occupational impact of AI in the generative era
Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and task…
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Enrique Fernández Macías @quiquefm.bsky.social · 23/07/2026
This is the one solid peer reviewed study I know, but may be a peculiar case: www.nature.com/articles/s41.... This is a good overview of gridwise correlational evidence: gadallon.substack.com/p/the-impact.... Then there are model-based projections which have little value imo
nature.com
Robotaxis reduce taxi drivers’ income - Humanities and Social Sciences Communications
Humanities and Social Sciences Communications - Robotaxis reduce taxi drivers’ income
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Reposted by Enrique Fernández Macías
Carl T. Bergstrom @carlbergstrom.com · 21/07/2026
1. We—@eduede.bsky.social, @mjcrockett.bsky.social, Kevin Gross, and I—have a new preprint on the arXiv today, based on ideas that emerged during an @sfiscience.bsky.social workshop in November 2024: The unintended consequences of large language models as a labor-augmenting technology in science.
arxiv.org
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at ...
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Reposted by Enrique Fernández Macías
Carlos J. Gil @karlosj89.bsky.social · 21/07/2026
New ESR paper: In traditional Italy, income positively predicts parenthood for men/women, especially among high-income couples. This is at odds with classic sex-specialisation theory, implying high economic prerequisites/stratification of parenthood under rising childlessness doi.org/10.1093/esr/...
@ESR
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 14/07/2026
Just published a new paper in our LET working paper series, one I particularly enjoyed writing. A theoretical and somewhat speculative take on whether AI can really replace human labour, with some strong claims that I hope help open up the debate a bit more #econsky #sociology tinyurl.com/5t4wwnmp
This paper proposes a conceptual framework for analysing the automation of human labour, with particular attention to artificial intelligence. Rather than presenting empirical findings, it builds an argument step by step from simple initial definitions and examines the implications, aiming to clarify a debate that is often muddled. The central proposal is to distinguish two components of human labour: operational (the execution of specifiable transformations) and agentic (the interpretation of situations, the exercise of judgement, the capacity to adapt and innovate). Machines can substitute for the operational component, but not for the agentic one. Automatability is therefore best understood not as a property of task content but as a property of work organisation: only labour that has been stripped of agency and reduced to the operation of predefined tasks can be automated. The framework is illustrated through the historical case of Taylorism, which suggests that agency extraction has typically preceded mechanisation, and then applied to AI. AI expands the operational frontier and appears to extend into the agentic domain, but its apparent agency is simulated rather than real: a system with genuine agency would be able to refuse and resist as human workers can. The implication is that AI cannot fully replace human labour while retaining its productive value, though a dystopian arrangement in which agency is concentrated in few hands while operational systems carry out the rest remains conceivable. A more desirable arrangement, in which AI augments individual workers as an extension of their own capacities, is also conceivable but requires treating AI as a public good rather than a private corporate asset.
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Enrique Fernández Macías @quiquefm.bsky.social · 14/07/2026
Just published a new paper in our LET working paper series, one I particularly enjoyed writing. A theoretical and somewhat speculative take on whether AI can really replace human labour, with some strong claims that I hope help open up the debate a bit more #econsky #sociology tinyurl.com/5t4wwnmp
This paper proposes a conceptual framework for analysing the automation of human labour, with particular attention to artificial intelligence. Rather than presenting empirical findings, it builds an argument step by step from simple initial definitions and examines the implications, aiming to clarify a debate that is often muddled. The central proposal is to distinguish two components of human labour: operational (the execution of specifiable transformations) and agentic (the interpretation of situations, the exercise of judgement, the capacity to adapt and innovate). Machines can substitute for the operational component, but not for the agentic one. Automatability is therefore best understood not as a property of task content but as a property of work organisation: only labour that has been stripped of agency and reduced to the operation of predefined tasks can be automated. The framework is illustrated through the historical case of Taylorism, which suggests that agency extraction has typically preceded mechanisation, and then applied to AI. AI expands the operational frontier and appears to extend into the agentic domain, but its apparent agency is simulated rather than real: a system with genuine agency would be able to refuse and resist as human workers can. The implication is that AI cannot fully replace human labour while retaining its productive value, though a dystopian arrangement in which agency is concentrated in few hands while operational systems carry out the rest remains conceivable. A more desirable arrangement, in which AI augments individual workers as an extension of their own capacities, is also conceivable but requires treating AI as a public good rather than a private corporate asset.
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 13/07/2026
Will AI act as a great equalizer across Europe or will it widen the gap between leading hubs and lagging regions? Our new paper maps the spatial distribution of AI across EU regions using six indicators of AI innovation, occupational exposure, and actual use @dfkogler.bsky.social #econsky #sociology
regions.regionalstudies.org
The geography of artificial intelligence in European regions: innovation, exposure and use.
The geography of artificial intelligence in European regions: innovation, exposure and use.
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Enrique Fernández Macías @quiquefm.bsky.social · 13/07/2026
Will AI act as a great equalizer across Europe or will it widen the gap between leading hubs and lagging regions? Our new paper maps the spatial distribution of AI across EU regions using six indicators of AI innovation, occupational exposure, and actual use @dfkogler.bsky.social #econsky #sociology
regions.regionalstudies.org
The geography of artificial intelligence in European regions: innovation, exposure and use.
The geography of artificial intelligence in European regions: innovation, exposure and use.
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Reposted by Enrique Fernández Macías
Dieter F. Kogler @dfkogler.bsky.social · 03/07/2026
Is AI an economic equalizer across Europe, or is it widening the regional divide? 🌍 Our new open-access paper in Regional Studies maps the spatial geography of AI across EU NUTS-2 regions using a multi-dimensional framework. Here is what we discovered: 👇 doi.org/10.1080/0034...
doi.org
The geography of artificial intelligence in European regions: innovation, exposure and use
This paper maps the geography of artificial intelligence (AI) innovation, exposure and use across European Union (EU) NUTS-2 regions using six complementary indicators. Findings reveal a spatial pa...
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
New paper out: a conceptual framework for the implications of digital technologies, including AI, for work and employment. It also summarises more than 10 years of research on the topic by myself and many collaborators in the JRC, Eurofound and the academic community. Thread 1/7 #EconSky #sociology
ideas.repec.org
Data, algorithms and platforms: A framework for understandin
This paper presents a conceptual framework for analysing the impact of digital technologies on work and employment, developed on the basis of nearly a decade of research by the Employment team of the
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
This paper is part of a broader JRC effort on understanding the implications of the digital transformation on work. For more papers on this topic, check out our Labour, Education and Technology working paper series. 7/7 END.
joint-research-centre.ec.europa.eu
JRC Working Papers on Labour, Education and Technology (LET)
The JRC Working Papers on Labour, Education and Technology (LET) address economic, social and policy questions related to the changing nature of work, skills and education as a result of technological...
000
Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
Will generative AI change these trends? Will it automate labour at scale? The future is open, but the paper argues AI is probably a late manifestation of the digital age rather than the spark of a new technological revolution. Thus, the same trends probably continue to apply, but intensified 6/7
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
Platformisation: most recent vector, but may end up the most disruptive. Digital labour platforms stabilised as marginal, but their way of organising work through algorithms is spreading to regular workers, via internal communication & coordination software platforms. Work is platformising fast 5/7
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
Automation has been the least consequential vector, despite the obsessive focus on it. It hasn't accelerated compared to previous periods: structural change in employment has been slow and incremental, and generally positive in qualitative terms, with variation across countries and periods 4/7
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
Digitisation is the most consequential vector so far. Digital tools are everywhere: not only as instruments of labour but also as tools of monitoring and control. They take over routine tasks while routinising work: standardising and quantifying processes, intensifying effort, reducing autonomy 3/7
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
It locates current trends in historical context, discusses the peculiarities of the digital economy, and translates them into three vectors of change in work and employment. The key concepts are data, algorithms and platforms, linked to the digitisation, automation and platformisation of work 2/7
A diagram with the temporal dimension of the ADP framework
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Enrique Fernández Macías @quiquefm.bsky.social · 06/06/2026
New paper out: a conceptual framework for the implications of digital technologies, including AI, for work and employment. It also summarises more than 10 years of research on the topic by myself and many collaborators in the JRC, Eurofound and the academic community. Thread 1/7 #EconSky #sociology
ideas.repec.org
Data, algorithms and platforms: A framework for understandin
This paper presents a conceptual framework for analysing the impact of digital technologies on work and employment, developed on the basis of nearly a decade of research by the Employment team of the
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Reposted by Enrique Fernández Macías
elDiario.es @eldiario.es · 01/05/2026
La inteligencia artificial irrumpe con despidos en el mercado de trabajo con muchas dudas sobre su impacto real
eldiario.es
La inteligencia artificial irrumpe con despidos en el mercado de trabajo con muchas dudas sobre su impacto real
Los temores sobre la destrucción de empleos debido a la IA cobran fuerza tras varios anuncios de recortes en tecnológicas, aunque no es la tendencia general por ahora y hay quien duda incluso de los m...
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Enrique Fernández Macías @quiquefm.bsky.social · 14/04/2026
I have updated and improved my website, in case anyone is interested. Lots of material in there!
enriquefernandezmacias.github.io
Enrique Fernández-Macías — Research on Work, Technology and Employment
Research on how digital technologies are reshaping work in Europe: automation, digitisation, platformisation, job quality and inequality.
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
New paper out! With P. Casas, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti, we revisit the occupational impact of AI in the GenAI era, tracking exposure from 2008 to 2024 across 127 occupations in Europe. Thread 1/13 #EconSky #sociology
publications.jrc.ec.europa.eu
Revisiting the occupational impact of AI in the generative AI era
Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evo...
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
This paper is part of a broader JRC effort on understanding the implications of the digital transformation on work. For more papers on this topic, check out our Labour, Education and Technology working paper series. 13/13 END
joint-research-centre.ec.europa.eu
JRC Working Papers on Labour, Education and Technology (LET)
The JRC Working Papers on Labour, Education and Technology (LET) address economic, social and policy questions related to the changing nature of work, skills and education as a result of technological...
000
Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
The policy challenge is clear: AI is a rising tide that reaches all boats. Whether it lifts them or sinks them depends on education, skills policies, collective bargaining, and how we choose to implement these technologies. 12/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
We remain agnostic about whether exposure means automation or augmentation. The same exposure can translate into complementarity for some workers and substitution for others, depending on work organisation, bargaining power, and institutional context. 11/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
A methodological aside: we used LLMs to help annotate benchmarks and tasks (alongside human experts). Interestingly, AI agents worked well as complements to humans but failed when used as substitutes — they couldn't learn from each other in a Delphi process. 10/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
An interesting quirk of our measure: simple arithmetic or form-filling get LOW exposure scores. Not because AI can't do them — quite the opposite. There's no research on them because they were "solved" long ago. Our metric is forward-looking, not backward. 9/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
At the task level, the most exposed activities are things like writing reports, reading professional documents, learning from others, and analytical reasoning. The least exposed: physical strength, dexterity, body coordination. Social tasks sit in between. 8/13
AI exposure and number of abilities required by specific types of task content
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
The abilities where AI is still weak? Social cognition (emotion, mind modelling, metacognition) and embodied interaction (navigation, sensorimotor). These remain hard frontiers for current AI systems. 7/13
AI exposure across cognitive abilities. AS = Attention and search; CE = Comprehension and expression; CL = Conceptualisation, learning and abstraction; QL = Quantitative and logical reasoning; MP = Memory processes; PA = Planning, sequential decision-making and acting; CO = Communication; EC = Emotion and self-control; MS = Mind modelling and social interaction; MC = Metacognition and confidence assessment; VP = Visual processing; AP = Auditory processing; SI = Sensorimotor interaction; NV = Navigation.
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
Why is exposure reaching everyone? Because the cognitive abilities where AI research has advanced most are "ideas-related": attention & search, comprehension & expression, logical reasoning, conceptualisation. These are needed in virtually ALL jobs, not just the fancy ones. 6/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
Exposure is reaching everyone, but it´s not uniform. High-skilled occupations (professionals, technicians) remain the most exposed, and the gap with elementary occupations has widened, not narrowed. The correlation between AI exposure and income is 0.85. 5/13
AI exposure and average income decile for specific occupations, showing a strong positive correlation
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
Our measure of AI exposure shows a steep increase across ALL occupational categories since ~2015, accelerating sharply in recent years. In relative terms: elementary occupations in 2024 score higher than professionals did in 2018 — a sign of how fast the frontier is moving. 4/13
Average scores of our measure of occupational AI exposure for specific occupations and broad occupational families at 1-digit level
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
What makes our measure different? Most AI exposure metrics rely on expert opinions or rubrics. Ours is grounded in actual AI research output: we count papers across 352 benchmarks over time. This captures where AI is heading, not just where it is — and lets us track the acceleration. 3/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
How? We link 352 AI benchmarks → 14 cognitive abilities → 108 work tasks → 127 occupations. Key contribution: instead of a static snapshot, we use the intensity of AI research over time to build a dynamic measure of occupational exposure to AI. 2/13
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Enrique Fernández Macías @quiquefm.bsky.social · 23/03/2026
New paper out! With P. Casas, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti, we revisit the occupational impact of AI in the GenAI era, tracking exposure from 2008 to 2024 across 127 occupations in Europe. Thread 1/13 #EconSky #sociology
publications.jrc.ec.europa.eu
Revisiting the occupational impact of AI in the generative AI era
Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evo...
264
Reposted by Enrique Fernández Macías
Agathe Demarais @agathedemarais.com · 20/03/2026
The war in Iran could trigger a global food shock • More than 1.1mn tonnes of fertiliser and fertiliser inputs is currently stuck in the Gulf • Shortages are starting during northern hemisphere’s planting season, raising the risk of lower harvests for staples like rice www.ft.com/content/1054...
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Reposted by Enrique Fernández Macías
Enrique Fernández Macías @quiquefm.bsky.social · 19/03/2026
New paper out! With Álvaro Mariscal de Gante and Ignacio González-Vázquez, we analyse how the platformisation of regular work affects job quality across the EU. We find negative effects for some types of platformisation but not others, and big country variation. 1/10 #EconSky #sociology
publications.jrc.ec.europa.eu
Job quality and the platformisation of regular work: A cross-country analysis of digital monitoring and algorithmic management in the EU
Digitalisation and the increasing use of digital platforms for the coordination of work processes in an algorithmic way can have deep implications for the world of work. While digital monitoring and a...
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Enrique Fernández Macías @quiquefm.bsky.social · 19/03/2026
This paper is part of a broader JRC effort on the digital transformation of work, building on the AIM-WORK survey. For more papers on this topic, check out our Labour, Education and Technology working paper series. 10/10 END
joint-research-centre.ec.europa.eu
JRC Working Papers on Labour, Education and Technology (LET)
The JRC Working Papers on Labour, Education and Technology (LET) address economic, social and policy questions related to the changing nature of work, skills and education as a result of technological...
000
Enrique Fernández Macías @quiquefm.bsky.social · 19/03/2026
For policy: it's not black or white. Soft platformisation has no major consequences for job quality. But hard forms clearly shrink autonomy and intensify work — especially for blue-collar workers. The challenge is ensuring institutional safeguards, particularly where they are weakest. 9/10
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