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Looking back from 2030 over the previous 10 years shows that many challenges have only partially been addressed. Much of the decade countries all over the world were struggling to get back on their feet following a global pandemic that wiped out many gains in jobs, earnings, and living standards painfully created previously. Public institutions have remained more important than ever but have been weakened by slow growth, faltering demographics, climate change, and other challenges. At the same time, opportunities from new technologies have only slowly benefited a larger part of the (working) population. New initiatives stemming from civil society and the private sector are only slowly emerging but have shown first signs of making a bigger impact by 2030. Building on a new policy consensus that puts sustainability and resilient societies rather than short-term profits at the center of collective action, signs of optimism are finally re-emerging gradually.

AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers' skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (2018) for the US and the World Bank's STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in Lao PDR are less likely than in Viet Nam to be in the "machine terrain", where workers will have to adapt to occupational transformations due to AI and are at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.

Dialogue des terrains et des disciplines au sein des sciences sociales, cet ouvrage s’inscrit dans une volonté d’élaboration collective d’une réflexion institutionnaliste sur les pays en développement. Il est le fruit d’un séminaire de recherche Institutions et développement (Matisse-CNRS, Paris, 2001-2006). Il souhaite permettre au lecteur de s’orienter dans les travaux d’économie du développement contemporaine (théories de la croissance, anti-développement) et parmi les discours des institutions financières internationales. Ceci passe par un travail de clarification des positions théoriques et idéologiques, face à la vague montante du néo-institutionnalisme en sciences sociales. Au-delà de contributions purement théoriques, l’ancrage en économie politique est clairement affirmé dans des textes analysant les changements institutionnels à l’œuvre dans des pays ou régions particuliers (Afrique de l’Ouest, La Réunion, le Mali, la Russie, l’Iran et l’Équateur). La question de la souveraineté des États s’y trouve nécessairement soulevée. Vues d’ailleurs, de nouvelles questions apparaissent, qui demandent un travail conceptuel nourri par des références situées en-dehors de la discipline économique auxquelles ce livre souhaite ouvrir. En effet, les pays dits du Sud sont des lieux d’élaboration politique, économique et sociale singuliers que les économistes européens gagnent à connaître et à réfléchir afin de penser la mondialisation et ses effets en connaissance de cause.

« An economic transaction is a solved political problem. Economics has gained the title of queen of the social sciences by choosing solved political problems as its domain. »(Lerner, 1972 : 259) Les économies développées sont caractérisées par une grande diversité quant à leur environnement institutionnel : le marché financier transforme l’épargne en investissement selon des structures variées, tandis que la détermination du salaire et l’ajustement quantita...

The impact of robots on employment and trade is a highly discussed topic in the academic And public debates. Particularly, there are concerns that automation may threat jobs in emerging countries given the erosion of the labour cost advantage. We provide evidence on the effects of robots on worldwide employment, including emerging economies. To instrument the use of robots, we introduce an index of technical progress, defined as the ability of robots to carry out different tasks. Robots turn out to have a significantly negative impact on worldwide employment. While it is small in developed countries, for emerging economies it amounts to -11 per cent between 2005 and 2014. However, here, there appear positive spillovers especially from robotisation in manufacturing on employment outside manufacturing. Furthermore, we assess cross-country effects, finding that robots in developed countries decrease offshoring just as employment in emerging economies. Die Auswirkungen von Robotern auf Beschäftigung und Handel sind in der akademischen und öffentlichen Debatte ein viel diskutiertes Thema. Insbesondere gibt es Bedenken, dass die Automatisierung Arbeitsplätze in Schwellenländern gefährden könnte, da der Arbeitskostenvorteil nachlässt. Wir liefern Belege für die Auswirkungen von Robotern auf die weltweite Beschäftigung, einschließlich der Schwellenländer. Um den Einsatz von Robotern zu instrumentieren, führen wir einen Index des technischen Fortschritts ein, definiert als die Fähigkeit von Robotern, verschiedene Aufgaben auszuführen. Roboter wirken sich signifikant negativ auf die weltweite Beschäftigung aus. Während der Effekt in Industrieländern klein ist, beträgt er in Schwellenländern zwischen 2005 und 2014 - 11 Prozent. Hier zeigen sich jedoch positive Spillover-Effekte insbesondere durch die Robotisierung im Verarbeitenden Gewerbe auf die Beschäftigung außerhalb des Verarbeitenden Gewerbes. Darüber hinaus untersuchen wir länderübergreifenden Auswirkungen. Dabei stellen wir fest, dass Roboter in Industrieländern zu weniger Offshoring führen und sich so negativ auf die Beschäftigung in Schwellenländern auswirken.